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GitHub MCP

Source example: agentflow/examples/github-mcp/git_mcp.py

What you will build​

A ReAct agent that connects to the GitHub Copilot MCP endpoint and asks it to retrieve repository commit data through MCP tools.

Prerequisites​

  • Python 3.11 or later
  • 10xscale-agentflow installed
  • fastmcp installed
  • a Google model key such as GEMINI_API_KEY
  • GITHUB_TOKEN with access to the GitHub MCP endpoint

Install:

pip install fastmcp

Set environment variables:

export GITHUB_TOKEN=your_token_here
export GEMINI_API_KEY=your_google_key_here

External service requirement​

This tutorial depends on a remote hosted MCP service:

https://api.githubcopilot.com/mcp/

If your token is missing or invalid, the MCP tool discovery or invocation will fail.

Architecture​

Step 1 — Configure the remote MCP server​

The example registers a github server:

config = {
    "mcpServers": {
        "github": {
            "url": "https://api.githubcopilot.com/mcp/",
            "headers": {"Authorization": f"Bearer {os.getenv('GITHUB_TOKEN')}"},
            "transport": "streamable-http",
        },
    }
}

This is the same pattern as the local MCP examples, but with:

  • a hosted remote endpoint
  • auth headers

Step 2 — Build an MCP-backed ToolNode​

client_http = Client(config)
tool_node = ToolNode(functions=[], client=client_http)

The agent then uses that tool_node like any other tool source.

Step 3 — Create the ReAct graph​

The graph is a standard MAIN -> TOOL -> MAIN loop:

main_agent = Agent(
    model="gemini-2.0-flash",
    provider="google",
    system_prompt=[...],
    tools=tool_node,
    trim_context=True,
)

The routing function checks whether the assistant emitted tool calls and either routes to TOOL or ends the run.

GitHub MCP execution flow​

Step 4 — Ask for repository data​

The example asks the agent to list commits:

inp = {
    "messages": [
        Message.text_message(
            "Please call the list_commits function for the github repo "
            "'https://github.com/suchith83/portfolio' of the 'suchith83' username, "
            "and give me the all commits in that repo."
        )
    ]
}
config = {"thread_id": "12345", "recursion_limit": 10}

res = app.invoke(inp, config=config)

Step 5 — Print message history​

The example includes a pretty-printer to inspect:

  • role
  • content
  • tool calls
  • metadata

That is useful when integrating remote MCP tools, because it helps you see:

  • which tool was chosen
  • how the tool call arguments were structured
  • what data came back from the server

Verification​

Successful behavior should include:

  • the graph completes without auth errors
  • the message history contains at least one tool call
  • the final assistant message summarizes repository commit information

Common mistakes​

  • Missing GITHUB_TOKEN.
  • Using a token that lacks the required access.
  • Assuming all GitHub MCP tools are always available.
  • Treating remote MCP latency like local function-call latency.

Key concepts​

ConceptDetails
hosted MCP endpointRemote shared tool service
auth headerRequired to access protected MCP tools
MCP-backed ReAct graphStandard AgentFlow loop with remote tool execution

What you learned​

  • How to connect AgentFlow to a hosted MCP server.
  • How to authorize GitHub MCP requests.
  • How to inspect a graph run that depends on remote repository tooling.

Next step​

→ MCP File Download to retrieve repository files rather than repository metadata.