Go MCP Markdown Server

Overview

The Go‑based MCP Markdown Server (go-mcp-server-mds) bridges static markdown content with modern AI assistants by exposing files as MCP resources and tools. It solves the common problem of turning a local or networked file system full of documentation, notes, or blog posts into an AI‑ready data source without the need for a separate database or API layer. By adhering to the MCP specification, the server can be discovered and interacted with by any Claude‑compatible assistant, allowing developers to query or list documents directly from the AI interface.

At its core, the server reads a directory (or any implementation) and registers every Markdown file as a resource. Each resource carries metadata derived from the file’s frontmatter, whether YAML or TOML, and includes attributes such as size, MIME type (), and a human‑readable name. This metadata is surfaced to the assistant through MCP’s resource descriptors, enabling intelligent search and filtering based on tags, dates, or titles. Two lightweight tools are also provided: one that lists all available markdown files with their parsed frontmatter, and another that retrieves the full content of a specified file. These tools are automatically named after the server instance, ensuring namespace safety when multiple servers run concurrently.

Developers can integrate this MCP server into AI workflows in several practical ways. For example, a technical writer could expose their documentation repository to an assistant that automatically drafts release notes by querying the latest files. A data scientist might let the AI pull experiment reports stored as markdown to generate summaries or highlight trends. In educational settings, instructors can make lecture notes available for students’ question‑answering bots, while keeping the source files under version control. Because the server operates over standard I/O, it can be launched as a lightweight subprocess within larger applications or CI pipelines, maintaining minimal overhead.

Unique advantages of this implementation include full support for both YAML and TOML frontmatter, giving teams flexibility in how they annotate documents. The use of Go’s abstraction allows the server to run against in‑memory files, network shares, or even embedded assets without code changes. Moreover, the resource URIs follow a simple scheme, making them instantly usable by any tool that understands file paths. The command‑line binary () further lowers the barrier to entry, letting teams spin up a server with a single flag and start querying from an AI assistant in minutes.