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MCP Agent Frameworks Demo

@andrewginns

代码仓库

基本信息

分类

AI 与智能体

传输方式

stdio

作者

andrewginns

关于 MCP Agent Frameworks Demo

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

MCP Agent Frameworks Demo 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

MCP Agent Frameworks Demo 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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

常见问题

1

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.

2

Which agent frameworks are demonstrated?

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

3

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).

4

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.

5

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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