Integrating AI development agents into your workflow requires structured interfaces that allow models to query, read, and validate documentation context efficiently. docmd satisfies this need via a native Model Context Protocol (MCP) server and auto-generated Agent Skills instructions.

Model Context Protocol (MCP) Setup

The Model Context Protocol connects LLM environments directly to your local workspace tools over stdio.

1. Claude Desktop Integration

Add the following to your claude_desktop_config.json:

{
  "mcpServers": {
    "docmd": {
      "command": "npx",
      "args": ["@docmd/core", "mcp"],
      "cwd": "/path/to/your/docs/project"
    }
  }
}

2. IDE Integration (Cursor / Windsurf)

In your editor’s MCP settings panel, add a new server using the stdio transport:

• Command: npx @docmd/core mcp
• Transport: stdio

Available MCP Tools

Once connected, agents can execute 6 primary tool handlers:

1. search_docs(query): Performs full-text searches across workspace files.
2. list_docs(subdir?): Lists relative Markdown file paths, optionally scoped to a locale or version.
3. read_doc(route): Reads raw Markdown contents for a sandboxed file route.
4. get_config(): Inspects resolved docmd.config.json options with secret values redacted.
5. validate_docs(): Lints internal link targets and reports broken anchors.
6. get_llms_context(): Fetches the consolidated llms-full.txt context payload.

Leveraging Agent Skills (SKILL.md)

Running docmd init generates a SKILL.md file in your repository root. This document serves as an instruction manual for AI agents operating on your codebase.