Model Context Protocol (MCP) Agent Frameworks Demo

Name: Model Context Protocol (MCP) Agent Frameworks Demo
Author: andrewginns

@andrewginns

リポジトリ

Model Context Protocol (MCP) Agent Frameworks Demo について

Demonstrate Agentic use of Model Context Protocol (MCP) server tools with several Agent Frameworks

#mcp #gemini #openai #agents #llm #logfire #mcp-server #pydantic-ai #agents-sdk #adk-python

基本情報

カテゴリ

AI とエージェント

ランタイム

python

トランスポート

stdio

公開者

andrewginns

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "agents-mcp-usage": {
      "command": "uv",
      "args": [
        "run",
        "agents_mcp_usage/basic_mcp/basic_mcp_use/adk_mcp.py"
      ]
    }
  }
}

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Model Context Protocol (MCP) Agent Frameworks Demo?

The Model Context Protocol (MCP) Agent Frameworks Demo is a demonstration and benchmarking platform that shows how to integrate Model Context Protocol (MCP) servers with multiple LLM agent frameworks: Google Agent Development Kit (ADK), LangGraph Agents, OpenAI Agents, and Pydantic‑AI Agents. It includes example Python MCP servers and a comprehensive evaluation suite for benchmarking LLM performance on mermaid diagram correction tasks.

How to use Model Context Protocol (MCP) Agent Frameworks Demo?

Set environment variables (GEMINI_API_KEY and/or OPENAI_API_KEY) in a .env file, then run one of the example scripts, e.g., uv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp.py. For benchmarking, use make benchmark MODEL="..." RUNS=5 PARALLEL=1 or the multi‑model evaluation scripts. Optional LOGFIRE_TOKEN enables tracing visualisation in the Logfire web UI.

Key features of Model Context Protocol (MCP) Agent Frameworks Demo

Demonstrates MCP integration with four agent frameworks
Includes example Python MCP servers (example_server.py, mermaid_validator.py)
Supports single and multiple MCP server coordination
Provides comprehensive evaluation and benchmarking suite
Offers interactive Streamlit dashboard for model comparison
Uses Pydantic Logfire for tracing and debugging

Use cases of Model Context Protocol (MCP) Agent Frameworks Demo

Learning how to connect MCP servers to different agent frameworks
Benchmarking LLM performance on mermaid diagram correction tasks
Evaluating cost, accuracy, and speed across multiple models
Developing modular, scalable agent architectures with multiple MCP servers

FAQ from Model Context Protocol (MCP) Agent Frameworks Demo

What is MCP?

The Model Context Protocol allows applications to provide context for LLMs in a standardised way, separating the concerns of providing context from the actual LLM interaction.

Which agent frameworks are demonstrated?

Google Agent Development Kit (ADK), LangGraph Agents, OpenAI Agents, and Pydantic‑AI Agents are demonstrated.

What MCP servers are included in the repository?

Two Python MCP servers: example_server.py (provides an add(a,b) tool, get_current_time() tool, and a dynamic greeting resource) and mermaid_validator.py (Mermaid diagram validation using mermaid‑cli).

How do I run a single‑model benchmark?

Use the Makefile command: make benchmark MODEL="openai:gpt-5.2 (none)" RUNS=5 PARALLEL=1 to run a Dockerised single‑model benchmark with isolated dependencies.

What tracing tool is used?

Tracing is done through Pydantic Logfire. An optional LOGFIRE_TOKEN can be set to visualise evaluations in the Logfire web UI.

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