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How to attach an MCP client

Goal

Connect an AI agent to Codefang over the Model Context Protocol (MCP) so the agent can run code analysis as a tool.

Prerequisites

  • codefang installed and on your PATH.
  • An MCP-compatible client (for example, Claude Code or another agent that launches MCP servers over stdio).
  • For the codefang_history tool, an absolute path to a local Git repository.

Steps

  1. Confirm the MCP server starts. It communicates over stdio using JSON-RPC, the standard transport for local MCP tool use:
codefang mcp

Press Ctrl-C to stop it — the agent will launch its own copy.

  1. Register Codefang as an MCP server in your client. For Claude Code, add it to ~/.claude/settings.json (user scope) or .claude/settings.json (project scope):
{
  "mcpServers": {
    "codefang": {
      "command": "codefang",
      "args": ["mcp"],
      "env": {}
    }
  }
}

  1. Restart the client so it picks up the new server. The agent then discovers three tools via the standard MCP tools/list method: codefang_analyze (static analysis on inline code), uast_parse (parse source into a UAST), and codefang_history (history analysis on a local repo).

  2. Ask the agent to run history analysis. The codefang_history tool requires an absolute repo_path; a relative path is rejected:

{
  "name": "codefang_history",
  "arguments": {
    "repo_path": "/home/user/projects/myapp",
    "analyzers": ["burndown", "devs"],
    "limit": 500
  }
}
  1. If a call misbehaves, enable debug logging to stderr by adding the --debug argument to the server registration:
{
  "mcpServers": {
    "codefang": {
      "command": "codefang",
      "args": ["mcp", "--debug"],
      "env": {}
    }
  }
}

Result

The agent lists the three Codefang tools and returns structured analysis when it calls them. A successful codefang_analyze or codefang_history call that comes back without isError: true confirms the client is attached.

See also