agent-mcp

An MCP (Model Context Protocol) facade server that exposes high-level tools backed by autonomous agent loops. This project acts as a bridge between client applications and multiple downstream MCP servers, using an LLM to intelligently delegate tasks as natural-language instructions.

Overview

agent-mcp is a meta-MCP server that:

1. Aggregates multiple MCP servers - Connects to any number of downstream MCP servers

2. Exposes unified tools - Each downstream server becomes a single high-level tool

3. Powers tools with agents - When a tool is invoked, an autonomous agent (powered by Google ADK) runs to fulfill the instruction using the downstream server's capabilities

4. Handles complexity internally - Clients stay simple; agents handle tool selection, error recovery, and multi-step operations

Related MCP server: mcpcute

Use Case

Instead of a client needing to:

• Know which MCP server has which tools

• Understand tool parameters and schemas

• Handle tool call sequences and errors

Clients can say:

Tool: "my-server"
Input: "Create a task 'Buy groceries' and add it to the Groceries project"

And the agent handles the complexity of looking up projects, calling the right tools, and returning results.

Architecture

┌──────────────────┐
│   Client (e.g.   │
│   Claude Code)   │
└────────┬─────────┘
         │ MCP Protocol
         ▼
┌─────────────────────┐
│  agent-mcp Server   │
├─────────────────────┤
│  Tool 1: server-a   ├──→ ADK Agent ─→ LLM (via LiteLLM)
│  Tool 2: server-b   ├──→ ADK Agent ─→ LLM (via LiteLLM)
│  Tool 3: server-c   ├──→ ADK Agent ─→ LLM (via LiteLLM)
│  ...                │
└─────────┬───────────┘
          │
          │ MCP Protocol (stdio/http)
          ▼
    ┌─────────────┐
    │ Downstream  │
    │ MCP Servers │
    └─────────────┘

Installation

Prerequisites

• Python 3.10+

• uv (optional, but recommended)

• API keys for the LLM provider you want to use (e.g. ANTHROPIC_API_KEY)

• API tokens for any downstream MCP servers you configure (optional)

Setup

1. Clone the repository:

git clone https://github.com/yourusername/agent-mcp.git
cd agent-mcp

2. Install dependencies:

# Using uv (recommended)
uv sync

# Or using pip
pip install -e .

3. Create configuration files:

# Create .mcp.json from the example
cp .mcp.json.example .mcp.json

# Edit with your API keys and paths
nano .mcp.json

4. Configure downstream MCP servers in config.yaml:

model: anthropic/claude-sonnet-4-20250514
max_tokens: 8096
max_llm_calls: 20

servers:
  my-server:
    description: "Description of what this server does"
    transport: stdio
    command: uv
    args:
      - run
      - --directory
      - /path/to/my-mcp-server
      - my-mcp-server
    env:
      MY_API_TOKEN: ${MY_API_TOKEN}

The model field uses LiteLLM format (provider/model-name), so you can use any supported LLM provider.

Configuration

.mcp.json

The .mcp.json file configures how Claude Code (or other MCP clients) launches agent-mcp:

{
  "mcpServers": {
    "agent-mcp": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/agent-mcp", "agent-mcp"],
      "env": {
        "ANTHROPIC_API_KEY": "your-key",
        "MY_API_TOKEN": "your-token"
      }
    }
  }
}

Important: Never commit .mcp.json to git. Use .mcp.json.example as a template.

config.yaml

The config.yaml file defines:

• Agent settings - Model (LiteLLM format), max tokens, LLM call limits

• Downstream servers - MCP servers to aggregate

  • Transport type (stdio, http)

  • How to launch them (command + args for stdio, url for http)

  • Environment variables (supports ${VAR} substitution)

  • Authentication (static headers or OAuth browser flow)

Usage

As an MCP Server

Once agent-mcp is running, clients connect via MCP and call tools:

# Pseudocode example
response = await client.call_tool("my-server", {
    "instruction": "List all my projects and tasks due today"
})

Running Standalone

uv run agent-mcp

The server listens on stdin/stdout for MCP protocol messages.

With Claude Code

1. Copy your .mcp.json configuration to ~/.config/Claude/claude_desktop_config.json

2. Restart Claude Code

3. Your configured downstream server tools become available in conversations

How It Works

1. Initialization - agent-mcp loads config.yaml and registers tools for each downstream server

2. Tool Registration - Each downstream server becomes a tool with its configured name

3. Request Handling - When a tool is invoked with a natural-language instruction:

   • agent-mcp creates an ADK McpToolset connected to the downstream server

   • An ADK LlmAgent runs with the downstream server's tools

   • The agent reads the instruction and decides which tools to call

   • The agent handles tool calls, errors, and retries

   • Results are returned to the client, and the toolset is closed

4. Per-invocation lifecycle - Each tool call gets a fresh MCP connection, avoiding stale connection state

Project Structure

agent-mcp/
├── README.md                 # This file
├── .gitignore              # Git exclusions
├── .mcp.json.example       # Template for .mcp.json
├── config.yaml             # Downstream server configuration
├── pyproject.toml          # Python project metadata
├── uv.lock                 # Locked dependencies
├── src/
│   └── agent_mcp/
│       ├── __init__.py
│       ├── server.py       # MCP server entrypoint
│       ├── agent.py        # ADK agent orchestration
│       ├── oauth.py        # OAuth browser-based authentication
│       └── config.py       # Configuration loading and parsing
└── tests/
    ├── test_agent.py       # Agent unit tests
    ├── test_config.py      # Config parsing tests
    ├── test_oauth.py       # OAuth flow tests
    ├── test_server.py      # Server CLI tests
    └── test_e2e_oauth.py   # End-to-end OAuth tests (require cached tokens)

Module Overview

• server.py - FastMCP server implementation, handles MCP protocol

• agent.py - ADK-based agent orchestration using LlmAgent, Runner, and McpToolset

• oauth.py - OAuth browser-based authentication for downstream MCP servers

• config.py - YAML parsing, environment variable substitution, validation

Development

Running Tests

uv run pytest -m "not e2e"

Running End-to-End Tests

E2E tests require cached OAuth tokens (run the OAuth flow manually first):

uv run pytest -m e2e

Code Style

• Python 3.10+

• Async-first (asyncio)

• Type hints throughout

• Minimal dependencies

Limitations & Future Work

• Only supports stdio and http transports

• No caching of tool schemas (reloaded on every call)

• Error messages are agent-generated (could be inconsistent)

Contributing

Contributions welcome! Areas of interest:

• Tool schema caching for performance

• Better error recovery strategies

• Additional test coverage

License

MIT

Acknowledgments

Built with:

• Google ADK - Agent Development Kit

• LiteLLM - Multi-provider LLM gateway

• mcp-python

• FastMCP