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.
@andrewginns
Basic information
Demonstrate Agentic use of Model Context Protocol (MCP) server tools with several Agent Frameworks
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"
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
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.
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.
example_server.py, mermaid_validator.py)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.
Google Agent Development Kit (ADK), LangGraph Agents, OpenAI Agents, and Pydantic‑AI Agents are demonstrated.
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).
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.
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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