Model Context Protocol (MCP) Agent Frameworks Demo

@andrewginns

About Model Context Protocol (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

Config

Add this server to your MCP-compatible client using the configuration below.

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

Tools

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Overview

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.

Frequently asked questions

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.

Comments

Basic information

More AI & Agents MCP servers

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AgentChat 是一个基于 LLM 的智能体交流平台，内置默认 Agent 并支持用户自定义 Agent。通过多轮对话和任务协作，Agent 可以理解并协助完成复杂任务。项目集成 LangChain、Function Call、MCP 协议、RAG、Memory、HITL、Skill、Milvus 和 ElasticSearch 等技术，实现高效的知识检索与工具调用，使用 FastAPI 构建高性能后端服务。

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