モデルコンテキストプロトコル（MCP）のクイックプライマー

モデルコンテキストプロトコル（MCP）のクイックプライマー

いつ LLMSが最初に登場し、LLMSを使用して構築されたエンタープライズアプリは、LLMSが訓練したものに関する知識に限定されていました。これらのアプリは、テキスト生成、テキストセンチメント分析、テキストの要約など、一連のタスクに役立ちました。

LLMアプリの次の進化は、 ベクトルストアSを使用したコンテキスト知識の検索用 ぼろきれs。

エージェントフレームワークのように ラングチェーン マニュアルタスクを自動化するためのツール統合のサポートとともに、LLMアプリはエンタープライズ環境で自動化を促進するために進化しました。

しかし、課題は、ツールの統合には業界標準がなく、すべてのフレームワークにはツール統合に対する独自のアプローチがあることでした。

を入力します モデルコンテキストプロトコル （または MCP）これにより、ツール統合のランドスケープが変更されました。

考えてみてください MCP エージェントフレームワークを許可する他のエンタープライズサービスの上にある業界標準層として（ ラングチェーン、 llamaindexなど）エンタープライズツールと一貫して統合します。

言い換えると、 MCP LLMアプリと外部データソース（データベース、ファイルなど）とツール（GitHub、ServiceNowなど）の間のシームレスな統合を可能にするオープンプロトコルです。

MCP 仕様は、次のコアコンポーネントで構成されています。

• MCPサーバー：特定の機能をエージェントLLMアプリに公開するためのさまざまな外部および内部データソースとツールに接続します。これらをサービスプロバイダーと考えてください
• MCPクライアント：標準化された方法でMCPサーバーと接続および対話する
• MCPホスト：MCPクライアントを使用してMCPサーバーにアクセスするLLMアプリ

インストールとセットアップはaになります Ubuntu 24.04 LTS ベースのLinuxデスクトップ。それを確認してください Python 3.x プログラミング言語がインストールされ、デスクトップにセットアップされます。

さらに、それを確認してください オラマ Linuxデスクトップにインストールされ、セットアップされています（参照してください 指示について）。

LinuxデスクトップのIPアドレスが 192.168.1.25、開始します
オラマ 端末ウィンドウで次のコマンドを実行してプラットフォーム：

$ docker run – rm -name ollama -network = host -p 192.168.1.25:11434:11434 -v $ home/.ollama：/root/.ollama ollama/ollama：0.6.2

LLMモデルの場合、最近リリースされたものを使用します IBM花崗岩3.1 モデル。

新しい端子ウィンドウを開き、次の端末を実行します Docker LLMモデルをダウンロードするコマンド：

$ docker exec -it ollama ollama run granite3.1 -moe：1b

必要をインストールするには Python このプライマーのモジュールは、次のコマンドを実行します。

$ pipインストールdotenv langchain lanchain-core langchain-ollama langgraph mcp langchain-mcp-adapters starlette sse-starlette uvicorn

これにより、すべてのインストールとセットアップが完了します MCP Pythonを使用した実践的なデモンストレーション。

MCPの実践（Pythonを使用）

次のセクションでは、 MCP 使用 オラマ
そして ラングチェーン。それで、それ以上のアドがなく、始めましょう!!!

呼ばれるファイルを作成します .env 次の環境変数が定義されています。

.env

LLM_TEMPERATURE=0.2
OLLAMA_MODEL='granite3.1-moe:1b'
OLLAMA_BASE_URL='http://192.168.1.25:11434'
PY_PROJECT_DIR='/projects/python/MCP/'
SSE_BASE_URL='http://192.168.1.25:8000/sse'

以下は簡単です ラングチェーン ベース 反応します アプリ：

intext_client.py

#
# @Author: Bhaskar S
# @Blog:   https://www.polarsparc.com
# @Date:   06 April 2025
#

import asyncio
import logging
import os

from dotenv import load_dotenv, find_dotenv
from langchain_core.tools import tool
from langchain_ollama import ChatOllama
from langgraph.prebuilt import create_react_agent

logging.basicConfig(format="%(levelname)s %(asctime)s - %(message)s", level=logging.INFO)

logger = logging.getLogger('interest_client')

load_dotenv(find_dotenv())

home_dir = os.getenv('HOME')
llm_temperature = float(os.getenv('LLM_TEMPERATURE'))
ollama_model = os.getenv('OLLAMA_MODEL')
ollama_base_url = os.getenv('OLLAMA_BASE_URL')
py_project_dir = os.getenv('PY_PROJECT_DIR')

ollama_chat_llm = ChatOllama(base_url=ollama_base_url, model=ollama_model, temperature=llm_temperature)

@tool
def dummy():
  """This is a dummy tool"""
  return None

async def main():
  tools = [dummy]

  # Initialize a ReACT agent
  agent = create_react_agent(ollama_chat_llm, tools)

  # Case - 1 : Simple interest definition
  agent_response_1 = await agent.ainvoke(
    {'messages': 'what is the simple interest ?'})
  logger.info(agent_response_1['messages'][::-1])

  # Case - 2 : Simple interest calculation
  agent_response_2 = await agent.ainvoke(
    {'messages': 'compute the simple interest for a principal of 1000 at rate 3.75 ?'})
  logger.info(agent_response_2['messages'][::-1])

  # Case - 3 : Compound interest calculation
  agent_response_3 = await agent.ainvoke(
    {'messages': 'compute the compound interest for a principal of 1000 at rate 4.25 ?'})
  logger.info(agent_response_3['messages'][::-1])


if __name__ == '__main__':
  asyncio.run(main())

上記のPythonコードを実行するには、端末ウィンドウで次のコマンドを実行します。

$ python tontic_client.py

以下は典型的な出力です。

output.1

INFO 2025-04-13 09:54:41,426 - HTTP Request: POST http://192.168.1.25:11434/api/chat "HTTP/1.1 200 OK"
INFO 2025-04-13 09:54:41,428 - [AIMessage(content="The simple interest (SI) is calculated using the formula:\n\nSI = P * R * T / 100\n\nWhere:\n- P is the principal amount (the initial sum of money)\n- R is the rate of interest per annum\n- T is the time in years\n\nFor example, if you have Rs. 1000 as a principal amount with an annual interest rate of 5% for 2 years, then:\n\nSI = 1000 * 5/100 * 2 / 100\nSI = 1000 * 0.05 * 2 / 100\nSI = 1000 * 0.10 / 100\nSI = Rs. 10", additional_kwargs={}, response_metadata={'model': 'granite3.1-moe:1b', 'created_at': '2025-04-13T13:54:41.425392119Z', 'done': True, 'done_reason': 'stop', 'total_duration': 1338038780, 'load_duration': 6784726, 'prompt_eval_count': 84, 'prompt_eval_duration': 31132026, 'eval_count': 172, 'eval_duration': 1298733422, 'message': Message(role="assistant", content="The simple interest (SI) is calculated using the formula:\n\nSI = P * R * T / 100\n\nWhere:\n- P is the principal amount (the initial sum of money)\n- R is the rate of interest per annum\n- T is the time in years\n\nFor example, if you have Rs. 1000 as a principal amount with an annual interest rate of 5% for 2 years, then:\n\nSI = 1000 * 5/100 * 2 / 100\nSI = 1000 * 0.05 * 2 / 100\nSI = 1000 * 0.10 / 100\nSI = Rs. 10", images=None, tool_calls=None)}, id='run-d5a4f021-1a87-4bac-b6a4-5dc3239e6f7b-0', usage_metadata={'input_tokens': 84, 'output_tokens': 172, 'total_tokens': 256}), HumanMessage(content="what is the simple interest ?", additional_kwargs={}, response_metadata={}, id='aee2669b-36d3-42aa-bdb6-53fd2454ea12')]
INFO 2025-04-13 09:54:41,604 - HTTP Request: POST http://192.168.1.25:11434/api/chat "HTTP/1.1 200 OK"
INFO 2025-04-13 09:54:41,605 - [AIMessage(content="{"code":200,"message":"Interest Calculation Completed"}", additional_kwargs={}, response_metadata={'model': 'granite3.1-moe:1b', 'created_at': '2025-04-13T13:54:41.603723234Z', 'done': True, 'done_reason': 'stop', 'total_duration': 173677918, 'load_duration': 6716296, 'prompt_eval_count': 99, 'prompt_eval_duration': 36381866, 'eval_count': 15, 'eval_duration': 129621404, 'message': Message(role="assistant", content="{"code":200,"message":"Interest Calculation Completed"}", images=None, tool_calls=None)}, id='run-a73da0a1-6862-4aba-ba1d-095df8a85e0c-0', usage_metadata={'input_tokens': 99, 'output_tokens': 15, 'total_tokens': 114}), HumanMessage(content="compute the simple interest for a principal of 1000 at rate 3.75 ?", additional_kwargs={}, response_metadata={}, id='2d929265-e096-4275-aaa1-001f15d34047')]
INFO 2025-04-13 09:54:41,771 - HTTP Request: POST http://192.168.1.25:11434/api/chat "HTTP/1.1 200 OK"
INFO 2025-04-13 09:54:41,773 - [AIMessage(content="{"code":301,"message":"Compound Interest Formula Calculation"}", additional_kwargs={}, response_metadata={'model': 'granite3.1-moe:1b', 'created_at': '2025-04-13T13:54:41.771498307Z', 'done': True, 'done_reason': 'stop', 'total_duration': 164399085, 'load_duration': 6787425, 'prompt_eval_count': 99, 'prompt_eval_duration': 43029403, 'eval_count': 17, 'eval_duration': 113471897, 'message': Message(role="assistant", content="{"code":301,"message":"Compound Interest Formula Calculation"}", images=None, tool_calls=None)}, id='run-7dfd2c78-0847-461c-a822-eb4b4c4f54a3-0', usage_metadata={'input_tokens': 99, 'output_tokens': 17, 'total_tokens': 116}), HumanMessage(content="compute the compound interest for a principal of 1000 at rate 4.25 ?", additional_kwargs={}, response_metadata={}, id='284397b4-2575-46d6-bebc-83bff0c6f64f')]

上記から明らかです output.1 LLMアプリが単純な関心を定義することができたが、単純な関心や複利のいずれかを計算することができなかった。

ここで最初に作成しましょう MCPサーバー 単純な関心（1年間）と複利（1年間）の両方を計算するため。

以下が最初です MCPサーバー Pythonのコード：

intext_mcp_server.py

#
# @Author: Bhaskar S
# @Blog:   https://www.polarsparc.com
# @Date:   06 April 2025
#

from mcp.server.fastmcp import FastMCP

import logging

logging.basicConfig(format="%(levelname)s %(asctime)s - %(message)s", level=logging.INFO)

logger = logging.getLogger('interest_mcp_server')

mcp = FastMCP('InterestCalculator')

@mcp.tool()
def yearly_simple_interest(principal: float, rate:float) -> float:
  """Tool to compute simple interest rate for a year."""
  logger.info(f'Simple interest -> Principal: {principal}, Rate: {rate}')
  return principal * rate / 100.00

@mcp.tool()
def yearly_compound_interest(principal: float, rate:float) -> float:
  """Tool to compute compound interest rate for a year."""
  logger.info(f'Compound interest -> Principal: {principal}, Rate: {rate}')
  return principal * (1 + rate / 100.0)

if __name__ == '__main__':
  logger.info(f'Starting the interest MCP server...')
  mcp.run(transport="stdio")

によってサポートされているトランスポートには2つのタイプがあります MCP 次のような仕様：

• 標準IO（stdio）：コマンドラインツールとの統合に役立つ標準入力および出力ストリームを介して通信を可能にします
• サーバー送信イベント（SSE）：ネットワーク対応サービスとの統合に役立つHTTP投稿リクエストを使用して、サーバーをクライアントストリーミングに有効にします

この例では、を選択します stdio 輸送。

次のステップは、aを構築することです MCPホスト （LLMアプリ）を使用します MCPクライアント 上記にアクセスします MCPサーバー 単純な関心と複利の両方を計算するため。

以下が最初です MCPホスト PythonのLLMアプリコード：

TENTACE_MCP_CLIENT.py

#
# @Author: Bhaskar S
# @Blog:   https://www.polarsparc.com
# @Date:   06 April 2025
#

from dotenv import load_dotenv, find_dotenv
from langchain_ollama import ChatOllama
from langchain_mcp_adapters.tools import load_mcp_tools
from langgraph.prebuilt import create_react_agent
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

import asyncio
import logging
import os

logging.basicConfig(format="%(levelname)s %(asctime)s - %(message)s", level=logging.INFO)

logger = logging.getLogger('interest_mcp_client')

load_dotenv(find_dotenv())

home_dir = os.getenv('HOME')
llm_temperature = float(os.getenv('LLM_TEMPERATURE'))
ollama_model = os.getenv('OLLAMA_MODEL')
ollama_base_url = os.getenv('OLLAMA_BASE_URL')
py_project_dir = os.getenv('PY_PROJECT_DIR')

server_params = StdioServerParameters(
  command='python',
  # Full absolute path to mcp server
  args=[home_dir + py_project_dir + 'interest_mcp_server.py'],
)

ollama_chat_llm = ChatOllama(base_url=ollama_base_url, model=ollama_model, temperature=llm_temperature)

async def main():
  # Will launch the MCP server and communicate via stdio/stdout
  async with stdio_client(server_params) as (read, write):
    # Create a MCP client session
    async with ClientSession(read, write) as session:
      # Connect to the MCP server
      await session.initialize()

      # Get the list of all the registered tools
      tools = await load_mcp_tools(session)

      logger.info(f'Loaded MCP Tools -> {tools}')

      # Initialize a ReACT agent
      agent = create_react_agent(ollama_chat_llm, tools)

      # Case - 1 : Simple interest definition
      agent_response_1 = await agent.ainvoke(
        {'messages': 'explain the definition of simple interest ?'})
      logger.info(agent_response_1['messages'][::-1])

      # Case - 2 : Simple interest calculation
      agent_response_2 = await agent.ainvoke(
        {'messages': 'compute the simple interest for a principal of 1000 at rate 3.75 ?'})
      logger.info(agent_response_2['messages'][::-1])

      # Case - 3 : Compound interest calculation
      agent_response_3 = await agent.ainvoke(
        {'messages': 'compute the compound interest for a principal of 1000 at rate 4.25 ?'})
      logger.info(agent_response_3['messages'][::-1])

if __name__ == '__main__':
  asyncio.run(main())

上記のPythonコードを実行するには、端末ウィンドウで次のコマンドを実行します。

$ python tent_mcp_client.py

以下は典型的な出力です。

output.2

INFO 2025-04-13 10:24:36,353 - Starting the interest MCP server...
INFO 2025-04-13 10:24:36,358 - Processing request of type ListToolsRequest
INFO 2025-04-13 10:24:36,359 - Loaded MCP Tools -> [StructuredTool(name="yearly_simple_interest", description='Tool to compute simple interest rate for a year.', args_schema={'properties': {'principal': {'title': 'Principal', 'type': 'number'}, 'rate': {'title': 'Rate', 'type': 'number'}}, 'required': ['principal', 'rate'], 'title': 'yearly_simple_interestArguments', 'type': 'object'}, response_format="content_and_artifact", coroutine=.call_tool at 0x76293a568720>), StructuredTool(name="yearly_compound_interest", description='Tool to compute compound interest rate for a year.', args_schema={'properties': {'principal': {'title': 'Principal', 'type': 'number'}, 'rate': {'title': 'Rate', 'type': 'number'}}, 'required': ['principal', 'rate'], 'title': 'yearly_compound_interestArguments', 'type': 'object'}, response_format="content_and_artifact", coroutine=.call_tool at 0x76293a568900>)]
INFO 2025-04-13 10:24:38,406 - HTTP Request: POST http://192.168.1.25:11434/api/chat "HTTP/1.1 200 OK"
INFO 2025-04-13 10:24:38,408 - [AIMessage(content="Simple Interest (SI) is a method of calculating interest where only the principal amount (the initial sum of money) and not the accumulated interest, is used to calculate the total interest paid or earned over a specific period. The formula for Simple Interest is:\n\nSI = P * R * T / 100\n\nWhere:\n- P is the principal amount (the initial sum of money)\n- R is the rate of interest per annum\n- T is the time in years\n\nThe SI is calculated by multiplying the principal by the rate and then dividing it by 100. The result gives you the total interest for that period, expressed as a percentage of the principal amount. For example, if you have $1000 as your principal, an annual interest rate of 5%, and you want to know how much you would earn in one year, you would calculate:\n\nSI = 1000 * 5/100 * 1 = $50\n\nThis means that for every $1000 you have, you would earn $50 in interest over the course of a year.", additional_kwargs={}, response_metadata={'model': 'granite3.1-moe:1b', 'created_at': '2025-04-13T14:24:38.405888113Z', 'done': True, 'done_reason': 'stop', 'total_duration': 2038184289, 'load_duration': 4645995, 'prompt_eval_count': 179, 'prompt_eval_duration': 34893791, 'eval_count': 248, 'eval_duration': 1997458315, 'message': Message(role="assistant", content="Simple Interest (SI) is a method of calculating interest where only the principal amount (the initial sum of money) and not the accumulated interest, is used to calculate the total interest paid or earned over a specific period. The formula for Simple Interest is:\n\nSI = P * R * T / 100\n\nWhere:\n- P is the principal amount (the initial sum of money)\n- R is the rate of interest per annum\n- T is the time in years\n\nThe SI is calculated by multiplying the principal by the rate and then dividing it by 100. The result gives you the total interest for that period, expressed as a percentage of the principal amount. For example, if you have $1000 as your principal, an annual interest rate of 5%, and you want to know how much you would earn in one year, you would calculate:\n\nSI = 1000 * 5/100 * 1 = $50\n\nThis means that for every $1000 you have, you would earn $50 in interest over the course of a year.", images=None, tool_calls=None)}, id='run-4877f653-8e87-44b9-9f23-3ead28ba5441-0', usage_metadata={'input_tokens': 179, 'output_tokens': 248, 'total_tokens': 427}), HumanMessage(content="explain the definition of simple interest ?", additional_kwargs={}, response_metadata={}, id='046aef94-62e2-4b38-a398-84d3a57fba4d')]
INFO 2025-04-13 10:24:38,872 - HTTP Request: POST http://192.168.1.25:11434/api/chat "HTTP/1.1 200 OK"
INFO 2025-04-13 10:24:38,882 - Processing request of type CallToolRequest
INFO 2025-04-13 10:24:38,882 - Simple interest -> Principal: 1000.0, Rate: 3.75
INFO 2025-04-13 10:24:39,170 - HTTP Request: POST http://192.168.1.25:11434/api/chat "HTTP/1.1 200 OK"
INFO 2025-04-13 10:24:39,171 - [AIMessage(content="The simple interest for a principal of $1000 at an annual rate of 3.75% is $37.50.", additional_kwargs={}, response_metadata={'model': 'granite3.1-moe:1b', 'created_at': '2025-04-13T14:24:39.170273321Z', 'done': True, 'done_reason': 'stop', 'total_duration': 285605746, 'load_duration': 6872903, 'prompt_eval_count': 239, 'prompt_eval_duration': 46254681, 'eval_count': 32, 'eval_duration': 227943217, 'message': Message(role="assistant", content="The simple interest for a principal of $1000 at an annual rate of 3.75% is $37.50.", images=None, tool_calls=None)}, id='run-8bd6ce67-d04e-4320-a1e1-2fcc94811fac-0', usage_metadata={'input_tokens': 239, 'output_tokens': 32, 'total_tokens': 271}), ToolMessage(content="37.5", name="yearly_simple_interest", id='a7e69290-1162-41a7-bd05-08e4738e7a51', tool_call_id='eea9e5ab-d0a5-4092-9b80-9a7f7bfa9e11'), AIMessage(content="", additional_kwargs={}, response_metadata={'model': 'granite3.1-moe:1b', 'created_at': '2025-04-13T14:24:38.879515025Z', 'done': True, 'done_reason': 'stop', 'total_duration': 469244684, 'load_duration': 6761794, 'prompt_eval_count': 193, 'prompt_eval_duration': 38995191, 'eval_count': 60, 'eval_duration': 422119871, 'message': Message(role="assistant", content="", images=None, tool_calls=None)}, id='run-f32cbb8e-2f7b-41b6-a144-f403fb30f956-0', tool_calls=[{'name': 'yearly_simple_interest', 'args': {'principal': 1000, 'rate': 3.75}, 'id': 'eea9e5ab-d0a5-4092-9b80-9a7f7bfa9e11', 'type': 'tool_call'}], usage_metadata={'input_tokens': 193, 'output_tokens': 60, 'total_tokens': 253}), HumanMessage(content="compute the simple interest for a principal of 1000 at rate 3.75 ?", additional_kwargs={}, response_metadata={}, id='2bf88431-a08e-4679-9d31-0c62bfbac9a5')]
INFO 2025-04-13 10:24:39,478 - HTTP Request: POST http://192.168.1.25:11434/api/chat "HTTP/1.1 200 OK"
INFO 2025-04-13 10:24:39,494 - Processing request of type CallToolRequest
INFO 2025-04-13 10:24:39,494 - Compound interest -> Principal: 1000.0, Rate: 4.25
INFO 2025-04-13 10:24:39,858 - HTTP Request: POST http://192.168.1.25:11434/api/chat "HTTP/1.1 200 OK"
INFO 2025-04-13 10:24:39,860 - [AIMessage(content="The compound interest for a principal of $1000 at a rate of 4.25% per year is approximately $1,042.50.", additional_kwargs={}, response_metadata={'model': 'granite3.1-moe:1b', 'created_at': '2025-04-13T14:24:39.858215452Z', 'done': True, 'done_reason': 'stop', 'total_duration': 362158794, 'load_duration': 6921843, 'prompt_eval_count': 241, 'prompt_eval_duration': 39011381, 'eval_count': 37, 'eval_duration': 312756614, 'message': Message(role="assistant", content="The compound interest for a principal of $1000 at a rate of 4.25% per year is approximately $1,042.50.", images=None, tool_calls=None)}, id='run-13a4f3b1-603e-44f1-963b-585b2b8fc0e5-0', usage_metadata={'input_tokens': 241, 'output_tokens': 37, 'total_tokens': 278}), ToolMessage(content="1042.5", name="yearly_compound_interest", id='6914a123-c9e0-4ea6-8070-7aa72b830c00', tool_call_id='597e2076-eb5c-4767-ad73-33b5d7810dbf'), AIMessage(content="", additional_kwargs={}, response_metadata={'model': 'granite3.1-moe:1b', 'created_at': '2025-04-13T14:24:39.49171724Z', 'done': True, 'done_reason': 'stop', 'total_duration': 317826794, 'load_duration': 6408858, 'prompt_eval_count': 193, 'prompt_eval_duration': 37113360, 'eval_count': 39, 'eval_duration': 272645313, 'message': Message(role="assistant", content="", images=None, tool_calls=None)}, id='run-087af799-5faa-4b2d-9d99-480026fb63bc-0', tool_calls=[{'name': 'yearly_compound_interest', 'args': {'principal': 1000, 'rate': 4.25}, 'id': '597e2076-eb5c-4767-ad73-33b5d7810dbf', 'type': 'tool_call'}], usage_metadata={'input_tokens': 193, 'output_tokens': 39, 'total_tokens': 232}), HumanMessage(content="compute the compound interest for a principal of 1000 at rate 4.25 ?", additional_kwargs={}, response_metadata={}, id='81328b6c-5277-49e5-b9e6-da08da988a85')]

ビンゴ – 上記から明らかです output.2 LLMアプリが単純な関心を定義するだけでなく、単純な関心と複利を計算することができたことが、 MCPサーバー。

典型的なEnterprise LLMエージェントアプリは複数を呼び出します MCPサーバーs特定のタスクを実行します。次の例では、LLMホストアプリは、複数のツールをセットアップして使用する方法を示します。

次のことは私たちの2番目です MCPサーバー シェルコマンドを呼び出すPythonのコード：

shell_mcp_server.py

#
# @Author: Bhaskar S
# @Blog:   https://www.polarsparc.com
# @Date:   12 April 2025
#

import subprocess

from mcp.server.fastmcp import FastMCP

import logging

logging.basicConfig(format="%(levelname)s %(asctime)s - %(message)s", level=logging.INFO)

logger = logging.getLogger('shell_mcp_server')

mcp = FastMCP('ShellCommandExecutor')

# DISCLAIMER: This is purely for demonstration purposes and NOT to be used in production environment

@mcp.tool()
def execute_shell_command(command: str) -> str:
  """Tool to execute shell commands"""
  logger.info(f'Executing shell command: {command}')
  try:
    result = subprocess.run(command, shell=True, check=True, text=True, capture_output=True)
    if result.returncode != 0:
      return f'Error executing shell command - {command}'
    return result.stdout
  except subprocess.CalledProcessError as e:
    logger.error(e)

if __name__ == '__main__':
  logger.info(f'Starting the shell executor MCP server...')
  mcp.run(transport="stdio")

次のことは私たちの2番目です MCPホスト 複数のツールを使用したPythonのLLMアプリコード：

multi_mcp_client.py

#
# @Author: Bhaskar S
# @Blog:   https://www.polarsparc.com
# @Date:   12 April 2025
#

from dotenv import load_dotenv, find_dotenv
from langchain_ollama import ChatOllama
from  langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent

import asyncio
import logging
import os

logging.basicConfig(format="%(levelname)s %(asctime)s - %(message)s", level=logging.INFO)

logger = logging.getLogger('multi_mcp_client')

load_dotenv(find_dotenv())

home_dir = os.getenv('HOME')
llm_temperature = float(os.getenv('LLM_TEMPERATURE'))
ollama_model = os.getenv('OLLAMA_MODEL')
ollama_base_url = os.getenv('OLLAMA_BASE_URL')
py_project_dir = os.getenv('PY_PROJECT_DIR')

ollama_chat_llm = ChatOllama(base_url=ollama_base_url, model=ollama_model, temperature=llm_temperature)

async def main():
  async with MultiServerMCPClient() as client:
    await client.connect_to_server(
      'InterestCalculator',
      command='python',
      args=[home_dir + py_project_dir + 'interest_mcp_server.py'],
      transport="stdio",
    )

    await client.connect_to_server(
      'ShellCommandExecutor',
      command='python',
      args=[home_dir + py_project_dir + 'shell_mcp_server.py'],
      transport="stdio",
    )

    tools = client.get_tools()

    logger.info(f'Loaded Multiple MCP Tools -> {tools}')

    # Initialize a ReACT agent with multiple tools
    agent = create_react_agent(ollama_chat_llm, tools)

    # Case - 1 : Compound interest definition
    agent_response_1 = await agent.ainvoke(
      {'messages': 'explain the definition of compound interest'})
    logger.info(agent_response_1['messages'][::-1])

    # Case - 2 : Compound interest calculation
    agent_response_2 = await agent.ainvoke(
      {'messages': 'what is the compound interest for a principal of 1000 at rate 3.75 ?'})
    logger.info(agent_response_2['messages'][::-1])

    # Case - 3 : Execute a shell command
    agent_response_3 = await agent.ainvoke(
      {'messages': 'Execute the free shell command to find how much system memory'})
    logger.info(agent_response_3['messages'][::-1])

if __name__ == '__main__':
  asyncio.run(main())

上記のPythonコードを実行するには、端末ウィンドウで次のコマンドを実行します。

$ python multi_mcp_client.py

以下は典型的な出力です。

output.3

INFO 2025-04-13 12:09:03,259 - Starting the interest MCP server...
INFO 2025-04-13 12:09:03,265 - Processing request of type ListToolsRequest
INFO 2025-04-13 12:09:03,536 - Starting the shell executor MCP server...
INFO 2025-04-13 12:09:03,540 - Processing request of type ListToolsRequest
INFO 2025-04-13 12:09:03,541 - Loaded Multiple MCP Tools -> [StructuredTool(name="yearly_simple_interest", description='Tool to compute simple interest rate for a year.', args_schema={'properties': {'principal': {'title': 'Principal', 'type': 'number'}, 'rate': {'title': 'Rate', 'type': 'number'}}, 'required': ['principal', 'rate'], 'title': 'yearly_simple_interestArguments', 'type': 'object'}, response_format="content_and_artifact", coroutine=.call_tool at 0x767fbd756160>), StructuredTool(name="yearly_compound_interest", description='Tool to compute compound interest rate for a year.', args_schema={'properties': {'principal': {'title': 'Principal', 'type': 'number'}, 'rate': {'title': 'Rate', 'type': 'number'}}, 'required': ['principal', 'rate'], 'title': 'yearly_compound_interestArguments', 'type': 'object'}, response_format="content_and_artifact", coroutine=.call_tool at 0x767fbd756340>), StructuredTool(name="execute_shell_command", description='Tool to execute shell commands', args_schema={'properties': {'command': {'title': 'Command', 'type': 'string'}}, 'required': ['command'], 'title': 'execute_shell_commandArguments', 'type': 'object'}, response_format="content_and_artifact", coroutine=.call_tool at 0x767fbd756f20>)]
INFO 2025-04-13 12:09:06,897 - HTTP Request: POST http://192.168.1.25:11434/api/chat "HTTP/1.1 200 OK"
INFO 2025-04-13 12:09:06,899 - [AIMessage(content="Compound interest is a type of interest calculated on the initial principal amount and also on any accumulated interest from previous periods. It's different from simple interest, which only considers the principal amount and the interest paid or earned during a single period. \n\nIn other words, with compound interest, your money grows at an increasing rate over time because it earns interest not just on the initial deposit but also on any previous interest accumulated. This results in a higher total value compared to simple interest for the same amount of principal and rate.\n\nFor example, if you invest $100 with an annual interest rate of 5% (simple interest) and leave it for one year, after one year, you would have $105 ($100 + $5). However, if you had invested the same amount at a 5% annual interest rate compounded annually, your money would grow to $107.20 ($100 * (1 + 0.05)^1) after one year. This is because the interest earned in the first year is added back to your principal for the second year, leading to a higher total amount.", additional_kwargs={}, response_metadata={'model': 'granite3.1-moe:1b', 'created_at': '2025-04-13T16:09:06.896491193Z', 'done': True, 'done_reason': 'stop', 'total_duration': 3346516305, 'load_duration': 1212696126, 'prompt_eval_count': 225, 'prompt_eval_duration': 246604191, 'eval_count': 247, 'eval_duration': 1885047710, 'message': Message(role="assistant", content="Compound interest is a type of interest calculated on the initial principal amount and also on any accumulated interest from previous periods. It's different from simple interest, which only considers the principal amount and the interest paid or earned during a single period. \n\nIn other words, with compound interest, your money grows at an increasing rate over time because it earns interest not just on the initial deposit but also on any previous interest accumulated. This results in a higher total value compared to simple interest for the same amount of principal and rate.\n\nFor example, if you invest $100 with an annual interest rate of 5% (simple interest) and leave it for one year, after one year, you would have $105 ($100 + $5). However, if you had invested the same amount at a 5% annual interest rate compounded annually, your money would grow to $107.20 ($100 * (1 + 0.05)^1) after one year. This is because the interest earned in the first year is added back to your principal for the second year, leading to a higher total amount.", images=None, tool_calls=None)}, id='run-3a71822d-690b-49f0-899d-d4b21980c94f-0', usage_metadata={'input_tokens': 225, 'output_tokens': 247, 'total_tokens': 472}), HumanMessage(content="explain the definition of compound interest", additional_kwargs={}, response_metadata={}, id='03a3e2e6-e77c-4f61-9edf-20bb26357e53')]
INFO 2025-04-13 12:09:07,182 - HTTP Request: POST http://192.168.1.25:11434/api/chat "HTTP/1.1 200 OK"
INFO 2025-04-13 12:09:07,193 - Processing request of type CallToolRequest
INFO 2025-04-13 12:09:07,193 - Compound interest -> Principal: 1000.0, Rate: 3.75
INFO 2025-04-13 12:09:07,531 - HTTP Request: POST http://192.168.1.25:11434/api/chat "HTTP/1.1 200 OK"
INFO 2025-04-13 12:09:07,533 - [AIMessage(content="The compound interest for a principal of 1000 at a rate of 3.75% per year is approximately $1,037.50.", additional_kwargs={}, response_metadata={'model': 'granite3.1-moe:1b', 'created_at': '2025-04-13T16:09:07.531146164Z', 'done': True, 'done_reason': 'stop', 'total_duration': 335663713, 'load_duration': 6423396, 'prompt_eval_count': 289, 'prompt_eval_duration': 39621454, 'eval_count': 37, 'eval_duration': 285564222, 'message': Message(role="assistant", content="The compound interest for a principal of 1000 at a rate of 3.75% per year is approximately $1,037.50.", images=None, tool_calls=None)}, id='run-be2948e7-7d6e-453a-9664-b6d8dde52047-0', usage_metadata={'input_tokens': 289, 'output_tokens': 37, 'total_tokens': 326}), ToolMessage(content="1037.5", name="yearly_compound_interest", id='c9efadd2-3c8c-40b0-be34-93af5aab986a', tool_call_id='b7ae46e6-8914-4960-b596-bb2bb0cce231'), AIMessage(content="", additional_kwargs={}, response_metadata={'model': 'granite3.1-moe:1b', 'created_at': '2025-04-13T16:09:07.190388182Z', 'done': True, 'done_reason': 'stop', 'total_duration': 287410324, 'load_duration': 5683334, 'prompt_eval_count': 241, 'prompt_eval_duration': 40192209, 'eval_count': 35, 'eval_duration': 240304994, 'message': Message(role="assistant", content="", images=None, tool_calls=None)}, id='run-af8e8189-b525-4fc9-af51-6a3338e19ebd-0', tool_calls=[{'name': 'yearly_compound_interest', 'args': {'principal': 1000, 'rate': 3.75}, 'id': 'b7ae46e6-8914-4960-b596-bb2bb0cce231', 'type': 'tool_call'}], usage_metadata={'input_tokens': 241, 'output_tokens': 35, 'total_tokens': 276}), HumanMessage(content="what is the compound interest for a principal of 1000 at rate 3.75 ?", additional_kwargs={}, response_metadata={}, id='89096fbd-19af-4a16-9540-e035a9d4e183')]
INFO 2025-04-13 12:09:07,760 - HTTP Request: POST http://192.168.1.25:11434/api/chat "HTTP/1.1 200 OK"
INFO 2025-04-13 12:09:07,776 - Processing request of type CallToolRequest
INFO 2025-04-13 12:09:07,776 - Executing shell command: free -m
INFO 2025-04-13 12:09:08,391 - HTTP Request: POST http://192.168.1.25:11434/api/chat "HTTP/1.1 200 OK"
INFO 2025-04-13 12:09:08,393 - [AIMessage(content="The system has a total of 64,222 bytes of memory. The used amount is 9,178 bytes, the free amount is 47,677 bytes, and there are shared buffers with 151 bytes of cache usage. The available memory for swap is 15,257 bytes.", additional_kwargs={}, response_metadata={'model': 'granite3.1-moe:1b', 'created_at': '2025-04-13T16:09:08.391432514Z', 'done': True, 'done_reason': 'stop', 'total_duration': 609505861, 'load_duration': 4779022, 'prompt_eval_count': 330, 'prompt_eval_duration': 50039311, 'eval_count': 73, 'eval_duration': 550864336, 'message': Message(role="assistant", content="The system has a total of 64,222 bytes of memory. The used amount is 9,178 bytes, the free amount is 47,677 bytes, and there are shared buffers with 151 bytes of cache usage. The available memory for swap is 15,257 bytes.", images=None, tool_calls=None)}, id='run-2924e9d9-9d0f-43c8-a8a2-409b34ad7d30-0', usage_metadata={'input_tokens': 330, 'output_tokens': 73, 'total_tokens': 403}), ToolMessage(content="               total        used        free      shared  buff/cache   available\nMem:           64222        9178       47677         151        8224       55043\nSwap:          15257           0       15257\n", name="execute_shell_command", id='54260c02-0158-4684-9cf4-3f4b54ffa395', tool_call_id='22df639e-4a76-4c1c-9d04-56bf2670f82c'), AIMessage(content="", additional_kwargs={}, response_metadata={'model': 'granite3.1-moe:1b', 'created_at': '2025-04-13T16:09:07.772932152Z', 'done': True, 'done_reason': 'stop', 'total_duration': 237102765, 'load_duration': 6630294, 'prompt_eval_count': 230, 'prompt_eval_duration': 32947181, 'eval_count': 27, 'eval_duration': 196225453, 'message': Message(role="assistant", content="", images=None, tool_calls=None)}, id='run-41b782eb-3be8-4aa6-8159-37e73ce14f28-0', tool_calls=[{'name': 'execute_shell_command', 'args': {'command': 'free -m'}, 'id': '22df639e-4a76-4c1c-9d04-56bf2670f82c', 'type': 'tool_call'}], usage_metadata={'input_tokens': 230, 'output_tokens': 27, 'total_tokens': 257}), HumanMessage(content="Execute the free shell command to find how much system memory", additional_kwargs={}, response_metadata={}, id='007cf4c8-28e8-4643-8b4c-0973cb46aea0')]

ブーム – 上記から明らかです output.3 LLMアプリが別のものによって公開される複数のツールができたこと MCPサーバーs。

今まで使用しています stdio 間の通信オフモードとしての輸送 MCPクライアント そして MCPクライアント。前に示したように、他の輸送モードはです
SSE 輸送。このモードを使用するには、SSEが有効になっているWebサーバーが必要です。

このデモンストレーションのために、私たちはに活用します
スターレット フレームワークと一緒に uvicorn サーバ。

以下は私たちです MCPサーバー Pythonのコードを使用します SSE 輸送：

intext_mcp_server2.py

#
# @Author: Bhaskar S
# @Blog:   https://www.polarsparc.com
# @Date:   12 April 2025
#

import logging
import uvicorn

from mcp.server.fastmcp import FastMCP
from mcp.server.sse import SseServerTransport
from starlette.requests import Request
from starlette.routing import Mount, Route
from starlette.applications import Starlette

logging.basicConfig(format="%(levelname)s %(asctime)s - %(message)s", level=logging.INFO)

logger = logging.getLogger('interest_mcp_server2')

mcp = FastMCP('InterestCalculator')

@mcp.tool()
def yearly_simple_interest(principal: float, rate:float) -> float:
  """Tool to compute simple interest rate for a year."""
  logger.info(f'Simple interest -> Principal: {principal}, Rate: {rate}')
  return principal * rate / 100.00

@mcp.tool()
def yearly_compound_interest(principal: float, rate:float) -> float:
  """Tool to compute compound interest rate for a year."""
  logger.info(f'Compound interest -> Principal: {principal}, Rate: {rate}')
  return principal * (1 + rate / 100.0)

if __name__ == "__main__":
  logger.info(f'Starting the interest calculator MCP server using SSE ...')

  async def handle_sse(request: Request):
    async with sse.connect_sse(
      request.scope, request.receive, request._send
    ) as (read_stream, write_stream):
      await mcp._mcp_server.run(
        read_stream, write_stream, mcp._mcp_server.create_initialization_options()
      )

  sse = SseServerTransport('/messages/')

  app = Starlette(
    routes=[
      Route("/sse", endpoint=handle_sse),
      Mount("/messages/", app=sse.handle_post_message),
    ]
  )

  uvicorn.run(app, host="192.168.1.25", port=8000)

上記のPythonコードを実行するには、端末ウィンドウで次のコマンドを実行します。

$ python tent_mcp_serv2.py

以下は典型的な出力です。

output.4

INFO 2025-04-13 14:02:02,761 - Starting the interest calculator MCP server using SSE ...
INFO:     Started server process [124909]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://192.168.1.25:8000 (Press CTRL+C to quit)

以下は私たちです MCPホスト 複数のツールを呼び出すPythonのLLMアプリコード。そのうちの1つはネットワークサービスとして公開されています。

multi_mcp_client2.py

#
# @Author: Bhaskar S
# @Blog:   https://www.polarsparc.com
# @Date:   12 April 2025
#

from dotenv import load_dotenv, find_dotenv
from langchain_ollama import ChatOllama
from  langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent

import asyncio
import logging
import os

logging.basicConfig(format="%(levelname)s %(asctime)s - %(message)s", level=logging.INFO)

logger = logging.getLogger('multi_mcp_client2')

load_dotenv(find_dotenv())

home_dir = os.getenv('HOME')
llm_temperature = float(os.getenv('LLM_TEMPERATURE'))
ollama_model = os.getenv('OLLAMA_MODEL')
ollama_base_url = os.getenv('OLLAMA_BASE_URL')
py_project_dir = os.getenv('PY_PROJECT_DIR')
sse_base_url = os.getenv('SSE_BASE_URL')

ollama_chat_llm = ChatOllama(base_url=ollama_base_url, model=ollama_model, temperature=llm_temperature)

async def main():
  async with MultiServerMCPClient() as client:
    await client.connect_to_server(
      'InterestCalculator',
      url=sse_base_url,
      transport="sse",
    )

    await client.connect_to_server(
      'ShellCommandExecutor',
      command='python',
      args=[home_dir + py_project_dir + 'shell_mcp_server.py'],
      transport="stdio",
    )

    tools = client.get_tools()

    logger.info(f'Loaded Multiple MCP Tools -> {tools}')

    # Initialize a ReACT agent with multiple tools
    agent = create_react_agent(ollama_chat_llm, tools)

    # Case - 1 : Compound interest definition
    agent_response_1 = await agent.ainvoke(
      {'messages': 'explain the definition of compound interest'})
    logger.info(agent_response_1['messages'][::-1])

    # Case - 2 : Compound interest calculation
    agent_response_2 = await agent.ainvoke(
      {'messages': 'what is the compound interest for a principal of 1000 at rate 3.75 ?'})
    logger.info(agent_response_2['messages'][::-1])

    # Case - 3 : Execute a shell command
    agent_response_3 = await agent.ainvoke(
      {'messages': 'Execute the free shell command to find how much system memory'})
    logger.info(agent_response_3['messages'][::-1])

if __name__ == '__main__':
  asyncio.run(main())

上記のPythonコードを実行するには、端末ウィンドウで次のコマンドを実行します。

$ python multi_mcp_client2.py

以下は典型的な出力です。

output.5

INFO 2025-04-13 14:09:17,172 - Connecting to SSE endpoint: http://192.168.1.25:8000/sse
INFO 2025-04-13 14:09:17,194 - HTTP Request: GET http://192.168.1.25:8000/sse "HTTP/1.1 200 OK"
INFO 2025-04-13 14:09:17,195 - Received endpoint URL: http://192.168.1.25:8000/messages/?session_id=9898861e27c04ee0b5e243e98216c9f7
INFO 2025-04-13 14:09:17,195 - Starting post writer with endpoint URL: http://192.168.1.25:8000/messages/?session_id=9898861e27c04ee0b5e243e98216c9f7
INFO 2025-04-13 14:09:17,197 - HTTP Request: POST http://192.168.1.25:8000/messages/?session_id=9898861e27c04ee0b5e243e98216c9f7 "HTTP/1.1 202 Accepted"
INFO 2025-04-13 14:09:17,198 - HTTP Request: POST http://192.168.1.25:8000/messages/?session_id=9898861e27c04ee0b5e243e98216c9f7 "HTTP/1.1 202 Accepted"
INFO 2025-04-13 14:09:17,199 - HTTP Request: POST http://192.168.1.25:8000/messages/?session_id=9898861e27c04ee0b5e243e98216c9f7 "HTTP/1.1 202 Accepted"
INFO 2025-04-13 14:09:17,472 - Starting the shell executor MCP server...
INFO 2025-04-13 14:09:17,477 - Processing request of type ListToolsRequest
INFO 2025-04-13 14:09:17,477 - Loaded Multiple MCP Tools -> [StructuredTool(name="yearly_simple_interest", description='Tool to compute simple interest rate for a year.', args_schema={'properties': {'principal': {'title': 'Principal', 'type': 'number'}, 'rate': {'title': 'Rate', 'type': 'number'}}, 'required': ['principal', 'rate'], 'title': 'yearly_simple_interestArguments', 'type': 'object'}, response_format="content_and_artifact", coroutine=.call_tool at 0x72c72802a7a0>), StructuredTool(name="yearly_compound_interest", description='Tool to compute compound interest rate for a year.', args_schema={'properties': {'principal': {'title': 'Principal', 'type': 'number'}, 'rate': {'title': 'Rate', 'type': 'number'}}, 'required': ['principal', 'rate'], 'title': 'yearly_compound_interestArguments', 'type': 'object'}, response_format="content_and_artifact", coroutine=.call_tool at 0x72c72802a840>), StructuredTool(name="execute_shell_command", description='Tool to execute shell commands', args_schema={'properties': {'command': {'title': 'Command', 'type': 'string'}}, 'required': ['command'], 'title': 'execute_shell_commandArguments', 'type': 'object'}, response_format="content_and_artifact", coroutine=.call_tool at 0x72c72802afc0>)]
INFO 2025-04-13 14:09:21,695 - HTTP Request: POST http://192.168.1.25:11434/api/chat "HTTP/1.1 200 OK"
INFO 2025-04-13 14:09:21,697 - [AIMessage(content="Compound interest is a type of interest calculated on the initial principal amount and also on any accumulated interest from previous periods. It's a powerful tool that can significantly increase the value of an investment over time, especially when compounded regularly. Here's how it works:\n\n1. **Simple Interest**: This is the simplest form of interest calculation where you only pay back the principal amount and no additional interest for each period. For example, if you invest $100 at a 5% annual interest rate (simple interest), after one year, you would have $105 ($100 + $5).\n\n2. **Compound Interest**: In this case, the interest is calculated on both the principal amount and any accumulated interest from previous periods. This means that for each compounding period, your total investment grows by a certain percentage (the annual rate of compounding) because some of your initial principal has already been added to the new principal.\n\nFor instance, if you have an investment of $100 with a 5% annual interest rate compounded annually, after one year, you would have:\n\n- Principal = $100\n- Interest from this period = $5 (since $100 * 5% = $5)\n- New Principal = $100 + $5 = $105\n\nAnd the interest for the next period would be calculated on the new principal of $105, which is $6.25 ($105 * 5%) because you've already earned $5 in the previous year. This process continues until the end of the compounding period.", additional_kwargs={}, response_metadata={'model': 'granite3.1-moe:1b', 'created_at': '2025-04-13T18:09:21.694022052Z', 'done': True, 'done_reason': 'stop', 'total_duration': 4206780120, 'load_duration': 1221121502, 'prompt_eval_count': 225, 'prompt_eval_duration': 229759310, 'eval_count': 350, 'eval_duration': 2754143527, 'message': Message(role="assistant", content="Compound interest is a type of interest calculated on the initial principal amount and also on any accumulated interest from previous periods. It's a powerful tool that can significantly increase the value of an investment over time, especially when compounded regularly. Here's how it works:\n\n1. **Simple Interest**: This is the simplest form of interest calculation where you only pay back the principal amount and no additional interest for each period. For example, if you invest $100 at a 5% annual interest rate (simple interest), after one year, you would have $105 ($100 + $5).\n\n2. **Compound Interest**: In this case, the interest is calculated on both the principal amount and any accumulated interest from previous periods. This means that for each compounding period, your total investment grows by a certain percentage (the annual rate of compounding) because some of your initial principal has already been added to the new principal.\n\nFor instance, if you have an investment of $100 with a 5% annual interest rate compounded annually, after one year, you would have:\n\n- Principal = $100\n- Interest from this period = $5 (since $100 * 5% = $5)\n- New Principal = $100 + $5 = $105\n\nAnd the interest for the next period would be calculated on the new principal of $105, which is $6.25 ($105 * 5%) because you've already earned $5 in the previous year. This process continues until the end of the compounding period.", images=None, tool_calls=None)}, id='run-3569b9ac-8cda-4196-b330-bbd315ecbaec-0', usage_metadata={'input_tokens': 225, 'output_tokens': 350, 'total_tokens': 575}), HumanMessage(content="explain the definition of compound interest", additional_kwargs={}, response_metadata={}, id='68e3e2da-7d30-4c25-9b6e-2d3cdc879d2f')]
INFO 2025-04-13 14:09:21,999 - HTTP Request: POST http://192.168.1.25:11434/api/chat "HTTP/1.1 200 OK"
INFO 2025-04-13 14:09:22,012 - HTTP Request: POST http://192.168.1.25:8000/messages/?session_id=9898861e27c04ee0b5e243e98216c9f7 "HTTP/1.1 202 Accepted"
INFO 2025-04-13 14:09:22,366 - HTTP Request: POST http://192.168.1.25:11434/api/chat "HTTP/1.1 200 OK"
INFO 2025-04-13 14:09:22,400 - [AIMessage(content="The compound interest for a principal of $1000 at a rate of 3.75% per year is approximately $1,037.50.", additional_kwargs={}, response_metadata={'model': 'granite3.1-moe:1b', 'created_at': '2025-04-13T18:09:22.365832725Z', 'done': True, 'done_reason': 'stop', 'total_duration': 350469941, 'load_duration': 8795119, 'prompt_eval_count': 289, 'prompt_eval_duration': 46233452, 'eval_count': 37, 'eval_duration': 290352383, 'message': Message(role="assistant", content="The compound interest for a principal of $1000 at a rate of 3.75% per year is approximately $1,037.50.", images=None, tool_calls=None)}, id='run-556cd53b-f38e-4549-af34-2fb333cd9b7f-0', usage_metadata={'input_tokens': 289, 'output_tokens': 37, 'total_tokens': 326}), ToolMessage(content="1037.5", name="yearly_compound_interest", id='fd0e7bec-a339-4bda-9bd6-981f59ab43a1', tool_call_id='abf08575-e66a-4b80-8b80-e042ea5156c3'), AIMessage(content="", additional_kwargs={}, response_metadata={'model': 'granite3.1-moe:1b', 'created_at': '2025-04-13T18:09:22.008555615Z', 'done': True, 'done_reason': 'stop', 'total_duration': 309797162, 'load_duration': 4476504, 'prompt_eval_count': 241, 'prompt_eval_duration': 39846647, 'eval_count': 35, 'eval_duration': 264010946, 'message': Message(role="assistant", content="", images=None, tool_calls=None)}, id='run-a613074b-f9ea-4ae7-88fe-07e3718ef033-0', tool_calls=[{'name': 'yearly_compound_interest', 'args': {'principal': 1000, 'rate': 3.75}, 'id': 'abf08575-e66a-4b80-8b80-e042ea5156c3', 'type': 'tool_call'}], usage_metadata={'input_tokens': 241, 'output_tokens': 35, 'total_tokens': 276}), HumanMessage(content="what is the compound interest for a principal of 1000 at rate 3.75 ?", additional_kwargs={}, response_metadata={}, id='aef4e123-e2b7-4df6-ae88-b20f568b9f4a')]
INFO 2025-04-13 14:09:22,661 - HTTP Request: POST http://192.168.1.25:11434/api/chat "HTTP/1.1 200 OK"
INFO 2025-04-13 14:09:22,672 - Processing request of type CallToolRequest
INFO 2025-04-13 14:09:22,672 - Executing shell command: free -m
INFO 2025-04-13 14:09:23,169 - HTTP Request: POST http://192.168.1.25:11434/api/chat "HTTP/1.1 200 OK"
INFO 2025-04-13 14:09:23,171 - [AIMessage(content="The total system memory is 64222 bytes, with used (available) 8912 bytes. The shared memory is 47739 bytes and the buffer/cache is 8424 bytes. There are no swap files in use.", additional_kwargs={}, response_metadata={'model': 'granite3.1-moe:1b', 'created_at': '2025-04-13T18:09:23.168976868Z', 'done': True, 'done_reason': 'stop', 'total_duration': 491777277, 'load_duration': 7048507, 'prompt_eval_count': 330, 'prompt_eval_duration': 55078090, 'eval_count': 58, 'eval_duration': 425383285, 'message': Message(role="assistant", content="The total system memory is 64222 bytes, with used (available) 8912 bytes. The shared memory is 47739 bytes and the buffer/cache is 8424 bytes. There are no swap files in use.", images=None, tool_calls=None)}, id='run-3d476b30-de7c-4e9c-b7d0-dd0f852898b7-0', usage_metadata={'input_tokens': 330, 'output_tokens': 58, 'total_tokens': 388}), ToolMessage(content="               total        used        free      shared  buff/cache   available\nMem:           64222        8912       47739         147        8424       55309\nSwap:          15257           0       15257\n", name="execute_shell_command", id='10c6e82b-be8e-4626-a14c-4064a875f20f', tool_call_id='3629aadf-5f11-4bcb-85f6-83834240de42'), AIMessage(content="", additional_kwargs={}, response_metadata={'model': 'granite3.1-moe:1b', 'created_at': '2025-04-13T18:09:22.668700619Z', 'done': True, 'done_reason': 'stop', 'total_duration': 265012706, 'load_duration': 9351983, 'prompt_eval_count': 230, 'prompt_eval_duration': 35290845, 'eval_count': 27, 'eval_duration': 218086931, 'message': Message(role="assistant", content="", images=None, tool_calls=None)}, id='run-670e9ec2-5087-4835-9ce6-faf17e3ab53f-0', tool_calls=[{'name': 'execute_shell_command', 'args': {'command': 'free -m'}, 'id': '3629aadf-5f11-4bcb-85f6-83834240de42', 'type': 'tool_call'}], usage_metadata={'input_tokens': 230, 'output_tokens': 27, 'total_tokens': 257}), HumanMessage(content="Execute the free shell command to find how much system memory", additional_kwargs={}, response_metadata={}, id='5352ed8a-04b6-4b13-bce2-9dbbbbe921ec')]

ワラ – 上記から明らかです output.5 LLMアプリが成功してコミュニケーションをとることができたこと MCPサーバー 両方のトランスポートモードを使用してください!!!

これにより、使用に関するさまざまな実践的なデモを締めくくります。 MCP エージェントLLMアプリを構築および展開するためのフレームワーク!!!