Markdown Specs MCP Server

This project provides a simple, read-only MCP (Model Context Protocol) server for serving local markdown documents to an LLM. It's built using the fastmcp library.

The server provides tools to list_specs, read_spec, and search_specs from a local directory, but it does not support creating, editing, or deleting files. This makes it a secure way to provide a model with access to a corpus of local documents.

Prerequisites

• Python 3.13 or newer
• uv package manager

Installation

1. Install project dependencies:

   This project uses uv for dependency management. Running the command below will install all dependencies listed in pyproject.toml and create a command-line script to run the server.

   uv pip install .
   

Configuration

The server's behavior can be customized by setting environment variables.

• SPECS_DIR: Specifies the root directory for all spec-related operations. If this variable is not set, the server will default to using the "specs" directory. Both the list_specs and read_spec tools will resolve file and directory paths relative to this base path.

For advanced configuration, you can modify the src/config.py file, but using environment variables is the recommended approach.

Running the Server

You can run the server in two modes: run for production/consumption and dev for development and testing.

Run Mode

This mode is for running the server to be used by the Gemini CLI. After installation, you can run the server using the mdspec command.

mdspec

The server will now run using stdio as the transport mechanism.

Dev Mode

This mode starts the MCP Inspector, a web-based UI that allows for interactive testing of the tools provided by the MCP server.

fastmcp dev src/main.py --ui-port="9080" --server-port="5080"

You will see output like this, including a URL to access the MCP Inspector:

Starting MCP inspector...
⚙ Proxy server listening on localhost:5080
🔑 Session token: <your_session_token>
   Use this token to authenticate requests or set DANGEROUSLY_OMIT_AUTH=true to disable auth

🚀 MCP Inspector is up and running at:
   http://localhost:9080/?MCP_PROXY_PORT=5080&MCP_PROXY_AUTH_TOKEN=<your_session_token>

🌐 Opening browser...

Connecting to Gemini CLI

To use this MCP server with the Gemini CLI, you need to add it as a source. Since the server now uses stdio, you can add it with the following command:

gemini mcp add --transport stdio mdspec mdspec

After adding the server, you can use the /mcp command in the Gemini CLI to see the available tools.

Tool Reference

list_specs(path: str = "", recursive: bool = False, hierarchical: bool = False) -> dict

Lists specs in a given directory.

Parameters:

• path (optional): The path to a directory relative to SPECS_DIR. Defaults to the root of SPECS_DIR.
• recursive (optional): If True, lists specs in all subdirectories. Defaults to False.
• hierarchical (optional): If True, returns a tree-like structure of the specs directory. Defaults to False.

read_spec(file_path: str) -> dict

Reads the content and metadata of a spec.

Parameters:

• file_path: The path to a spec file relative to SPECS_DIR.

search_specs(keyword: str, recursive: bool = False, before_context: int = 2, after_context: int = 2) -> dict

Searches for a keyword in all specs.

Parameters:

• keyword: The keyword to search for.
• recursive (optional): If True, searches in all subdirectories. Defaults to False.
• before_context (optional): The number of lines to include before the matching line. Defaults to 2.
• after_context (optional): The number of lines to include after the matching line. Defaults to 2.

search_in_spec(file_path: str, keyword: str, before_context: int = 2, after_context: int = 2) -> dict

Searches for a keyword in a specific spec.

Parameters:

• file_path: The path to a spec file relative to SPECS_DIR.
• keyword: The keyword to search for.
• before_context (optional): The number of lines to include before the matching line. Defaults to 2.
• after_context (optional): The number of lines to include after the matching line. Defaults to 2.

get_table_of_contents(file_path: str) -> dict

Generates a table of contents from the markdown headings in a file.

Parameters:

• file_path: The path to a spec file relative to SPECS_DIR.

index_specs() -> dict

Indexes all specs for semantic search.

semantic_search(query: str, n_results: int = 5) -> dict

Performs a semantic search over the indexed specs.

Parameters:

• query: The search query.
• n_results (optional): The number of results to return. Defaults to 5.

search_by_tag(tag: str) -> dict

Searches for specs with a specific tag in their frontmatter.

Parameters:

• tag: The tag to search for.

Suggested Prompts for Developers

Here are some suggested prompts to help developers effectively use the mdspec MCP server through the Gemini CLI or other coding agents:

General Information & Discovery

• "List all specs in the 'project_docs' directory, including their last modified times."
• "Show me a hierarchical view of all specs in the codebase."
• "What are the main topics discussed in the 'architecture_overview.md' file?"
• "Summarize the content of the file 'api_design.md'."

Targeted Search & Retrieval

• "Find all specs that mention 'authentication' or 'authorization'."
• "Search for usage of 'ChromaDB' across all documentation, and show me the surrounding context."
• "In the 'troubleshooting.md' file, find all mentions of 'error code 500' and show me the lines around it."
• "What are the specs tagged with 'feature-x'?"
• "Which specs are semantically similar to 'how to integrate with external services'?"

Code Understanding & Refactoring

• "I'm looking for documentation related to our new payment gateway. Can you find relevant specs?"
• "I need to understand how user roles are managed. What documentation exists for this?"
• "Find all specs that discuss 'performance optimization' and list their titles."

Onboarding & New Features

• "Give me an overview of the 'user management' module by listing relevant documentation."
• "What are the best practices for writing new API endpoints? Show me relevant specs."
• "Find documentation on the new 'notification service' feature."

Tips for Effective Use:

• Consistent Tagging: Encourage developers to use consistent and meaningful tags in their specs (e.g., in YAML frontmatter) to maximize the effectiveness of search_by_tag.
• Keep Index Up-to-Date: For semantic search to provide the most relevant results, ensure that the index_specs tool is run regularly after changes to the spec corpus.