md-mcp

md-mcp

An MCP server that provides surgical read/write access to individual sections of Markdown files, allowing agents to fetch, edit, or delete specific slices without touching the entire file.

Category
Visit Server

README

md-mcp

An MCP server that gives agents surgical read/write access to individual sections of Markdown files.

Overview

Large Markdown files — documentation, changelogs, wikis — are expensive for agents to work with: reading the entire file just to update one section wastes tokens, and rewriting the whole file risks accidental data loss. md-mcp solves this by exposing each section as an individually addressable unit, so an agent can fetch, edit, or delete exactly the slice it needs without touching anything else.

The server runs over stdio as a local MCP server. Files are addressed by path on disk; sections within a file are addressed by a dot-separated heading path (e.g. "User Guide.Installation.Prerequisites"). Parsed ASTs are cached in memory and invalidated automatically on mtime change, so repeated reads of an unchanged file are fast.

Installation

The package is not yet published to PyPI. Install it in editable mode directly from the repository.

pip

pip install -e .

uv

uv pip install -e .

Connecting to opencode / Claude Desktop

After installation the md-mcp entry-point script is on your PATH. Add it as a local stdio MCP server in your client config.

opencode (opencode.json / opencode.jsonc)

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "md-mcp": {
      "type": "local",
      "command": ["md-mcp", "--allow-root", "/your/docs/dir"]
    }
  }
}

Claude Desktop (claude_desktop_config.json)

Claude Desktop uses the top-level key mcpServers:

{
  "mcpServers": {
    "md-mcp": {
      "command": "md-mcp",
      "args": [],
      "transport": "stdio"
    }
  }
}

Dot-path addressing

Every tool that targets a section takes a path argument — a dot-separated string of heading texts from the document root down to the target section. Given this Markdown file:

# My Project

## Installation

### Prerequisites

## Usage

The available paths are:

Section Path
# My Project My Project
## Installation My Project.Installation
### Prerequisites My Project.Installation.Prerequisites
## Usage My Project.Usage

Matching is case-insensitive, so my project.installation and My Project.Installation resolve to the same section. Ambiguous paths (duplicate heading texts at the same level) resolve to the first match.

Tool reference

Tool Arguments Returns Description
get_index file_path: str dict Returns the full section tree of a file as a nested dict with heading, level, path, and children fields.
get_section file_path: str, path: str, depth: int | None = None str Returns the raw Markdown text of the named section. depth=None (default): full subtree; depth=0: heading + own body only; depth=N: heading + N levels of children.
search_sections file_path: str, query: str, case_sensitive: bool = False list Searches all section bodies for lines matching query (Python regex). Returns a list of {"path", "matches": [{"line", "text"}]} objects in file order. Each section's own body is searched independently — results are never duplicated across parent and child. Heading text is not searched — use get_index to find terms in headings.
add_section file_path: str, heading: str, content: str, under: str | None = None, before: str | None = None, after: str | None = None str Inserts a new section. heading must start with ####### followed by a space. Placement: under (last child), before (immediately before), after (immediately after including its children), or omit all to append. Returns "ok".
replace_section file_path: str, path: str, new_content: str str Replaces the body of the named section, preserving its heading line. Returns "ok".
patch_section file_path: str, path: str, new_content: str str Returns a unified diff of what replace_section would write, without modifying the file. Returns an empty string if there are no changes.
delete_section file_path: str, path: str, include_children: bool = True str Deletes the named section. With include_children=True (default) removes the heading, its body, and all child sections; with False removes only the heading and its direct body, promoting children. Returns "ok".

Examples

A short worked session against a file docs/guide.md whose top-level heading is User Guide:

1. Inspect the structure

get_index("docs/guide.md")

Returns a nested tree:

{
  "sections": [
    {
      "heading": "User Guide",
      "level": 1,
      "path": "User Guide",
      "children": [
        {
          "heading": "Getting Started",
          "level": 2,
          "path": "User Guide.Getting Started",
          "children": []
        },
        {
          "heading": "Configuration",
          "level": 2,
          "path": "User Guide.Configuration",
          "children": []
        }
      ]
    }
  ]
}

2. Read a section

get_section("docs/guide.md", "User Guide.Getting Started")

Returns the raw Markdown text of that section (heading line + body).

3. Preview a change

patch_section("docs/guide.md", "User Guide.Configuration", "Set `debug: true` in `config.yaml`.")

Returns a unified diff showing exactly what would change — nothing is written yet.

4. Apply the change

replace_section("docs/guide.md", "User Guide.Configuration", "Set `debug: true` in `config.yaml`.")

Returns "ok". The file is updated; the heading line is preserved unchanged.

5. Add a new section

add_section("docs/guide.md", "## Troubleshooting", "See the FAQ.", after="User Guide.Configuration")

Returns "ok". The new ## Troubleshooting section is inserted immediately after ## Configuration.

6. Find sections mentioning a term

search_sections("docs/guide.md", "debug")

Returns:

[
  {
    "path": "User Guide.Configuration",
    "matches": [
      {"line": 18, "text": "Set `debug: true` in `config.yaml`."}
    ]
  }
]

Development

Requirements: Python 3.11+

Install the package with dev dependencies:

pip install -e ".[dev]"

Run the test suite:

pytest

Set up and run pre-commit hooks (ruff + mypy):

pre-commit install
pre-commit run --all-files

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured