Md Server for AI Automation

Md Server: Local Markdown bridge for MCP-powered AI assistants

Md Server, created by Peteretelej, connects AI clients to local Markdown documentation using the Model Context Protocol (MCP). The server exposes directory listing, file reading, and keyword search so models can reference local .md files during a session. It uses a JSON configuration for directory selection, runs as a lightweight Go binary, and targets developers, technical writers, and researchers who need AI access to private documentation without uploading files.

What tasks can you actually use it for?

The server turns a local Markdown folder into callable tools for an MCP-compatible model, providing three explicit operations: list_files, read_file, and search_files. These tools let a model enumerate available documents, retrieve full text for analysis, or locate keywords across the configured directory. Use cases include model-assisted documentation lookup, in-conversation reference checks, and research note retrieval where Markdown is the canonical storage format.

How reliable are its file reads and searches?

The tool supplies raw Markdown text to the model on demand, so read accuracy depends on the source files rather than the server. Search operates across the configured directory and supports subdirectories when enabled, which helps find scattered notes quickly. Because the server exposes file contents directly, outputs generated by an AI using those files should be independently verified for critical decisions, matching standard practice for model-based references.

What inputs and setup does it require?

Configuration is handled via a JSON file that points the server at a local path, and the server is optimized for Markdown (.md) files only. It requires a host environment that supports the Model Context Protocol and either a precompiled binary or a Go build from source. The developer implemented the tool in Go, which keeps the runtime lightweight and portable across desktop systems that run MCP clients.

How it fits into workflows and handles privacy

The server runs locally and sends file content to the active model session, so documents are not automatically uploaded to external services. That model of operation suits teams who maintain private knowledge bases and prefer local processing. The open source codebase permits auditing or extension, letting teams inspect exactly how files are read and indexed before integrating the server into an existing documentation workflow.

Md Server is a practical fit for local-documentation workflows

Md Server is a pragmatic option for developers and technical writers who need AI-assisted access to local Markdown notes. It integrates into MCP-based workflows and keeps data on the host machine, but any assertions the model makes using those files should be checked by a human for accuracy. Treat the server as a bridge to improve retrieval, not as a substitute for final editorial review.