"markdown" matching MCP servers:

Provides access to markdown documentation for local NPM, Go, and PyPi packages as MCP resources or tools. It enables informed code generation by surfacing documentation directly from a project's working directory.

Deterministic navigation maps over code AND markdown for AI agents: one MCP server, two lenses (tree-sitter TS/JS/Python + markdown). Map a whole project, read one function or one doc-section, ~99% less context. Merges codelens + docslens.

MCP server for collecting code from files and directories into a single markdown document.

Enables agents and tools to access Markdown linting through the Model Context Protocol, providing structured lint results and workspace-aware checks.

* analyze your react projects locally * consistent output w/ AST parsing + AI * create markdown docs, llm.txt for your react code at once

Token-efficient MCP server for multi-language project analysis (Java, TypeScript, JavaScript, Markdown, Python) with plugins, semantic search, and static analysis.

A stdio MCP server that audits other MCP servers by linting their tool schemas and calling tools with malformed inputs to produce a 0–100 conformance score and Markdown report.

MCP server that runs JetBrains' ReSharper command-line tools for code inspection and cleanup, exposed to C# coding agents over stdio.

Audits Ethereum/Solidity smart contracts using the Cysic Minimax model, providing automated vulnerability detection, gas optimization suggestions, and markdown report generation.

Exposes read-only repository scanning across architecture, security, performance and product lenses, citation verification of findings against a working tree or git revision, and HTML/SARIF/Markdown dossier rendering as MCP tools for assistants such as Claude and Cursor.

Enables MCP-compatible AI clients to generate markdown documentation for single code files or batches of up to 20 files, using a Writulos Scribe API key.

Analyzes codebases and extracts all symbols (functions, classes, methods, interfaces, etc.) from 10+ programming languages into LLM-optimized markdown format. Enables AI assistants to understand entire project structures efficiently without processing full source code.

Independent verification for code written by AI agents. Adds an /elliot-audit command to Claude Code, Cursor and Codex that checks whether a repo does what its docs claim, verifies every cited line at file:line, and writes an HTML/Markdown/JSON report into the repo.

Lints local Markdown files or whole directory trees for broken relative links and images, empty link targets or text, heading hierarchy problems, and common README issues. Only relative targets on disk are checked, so external http(s) links are never fetched.

A codebase ontology and a cited wiki, both kept as Markdown in one folder on your disk. Typed graph answers — what a change affects, how two concepts connect, which wiki lines lack a citation — and every write lands as a Git diff a person reviews.

Enables developers and product owners to scan a codebase and extract a readable business overview—features, roles, access control, data model, and endpoints—along with findings on missing or inconsistent rules, and generate shareable HTML or Markdown reports and diagrams.

Enables AI coding assistants to run pre-publish security and code-quality scans across 17 categories, generate a 0–100 security score and Markdown report, quickly check critical vulnerabilities, and receive fix/refactor suggestions by finding ID.

Analyzes source code across multiple languages to extract structural elements like classes, functions, and parameters using tree-sitter. It provides LLM-optimized markdown output that includes nesting levels, line numbers, and signatures to facilitate codebase navigation.

Enables users to conduct evidence-driven product research through MCP by importing documents, extracting JavaScript code clues, recording cited facts and inferences, tracking validation, managing research lifecycles, and exporting Markdown or JSON reports. It provides 25 MCP tools for turning source materials into traceable research findings and verification plans.

Enables MCP clients to query project-scoped Graphify graphs, search, read, and write Obsidian Markdown notes, and recall or store persistent ICM memories over authenticated private HTTPS. It serializes access per project and supports slim and standard Docker deployments without embeddings or extra memory services.