"How to document a codebase using Markdown" matching MCP servers:

Provides AI tools with access to ProudNet documentation through search, page retrieval, and section listing capabilities. Enables users to query ProudNet networking library documentation using natural language.

Enables AI agents to search, deep-read, and build knowledge bases from Markdown, PDF, DOCX, and PPTX documents via MCP tools for retrieval, document navigation, and ingestion.

Provides access to microCMS documentation, enabling AI assistants to search and retrieve the latest document content.

A Model Context Protocol (MCP) server designed to easily dump your codebase context into Large Language Models (LLMs).

This MCP server lets AI agents connect to Figma to read, analyze, create, and modify designs, including elements, styles, assets, and code generation. It works with free Figma accounts and supports parallel multi-agent use across MCP-compatible clients.

Verifiable document intelligence for AI agents. Extract text, tables, and structured data from PDFs and URLs. Summarize, answer questions, check claims, and translate — all with cited evidence. Store tamper-evident evidence bundles with cryptographic signatures and on-chain attestation via Base L2. Cross-document semantic search and Q&A across named collections. Pay per call with USDC

An MCP server for reading, creating, and managing PDF, DOCX, and Excel documents with OCR, styling, categorization, and remote file bridges.

Enables searching and retrieving content, module information, and Twig template snippets from a-blog cms via its GET API.

Bridges AI agents with watsonx.data document libraries, enabling natural language queries and intelligent retrieval from document libraries.

MCP-first A/B testing server that enables agents to manage experiments (create, update traffic splits, read results, apply winning variants) from tools like Claude Code, with self-hosted Cloudflare backend.

Thin MCP server that enables AI agents to generate video, image, and audio content via the Deepy API using a personal API key, with safe generation flow and no business logic.

Lightweight project management for teams and AI agents.

Exposes a RAG document-search API as MCP tools (rag_health, rag_ingest, rag_query), enabling agents to index and search markdown documents with cited results through natural language.

Provides fresh project context to coding agents by combining Markdown documentation and live Git state, enabling deterministic startup briefs and bounded document retrieval for MCP-compatible tools.

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

Enables AI-powered document editing, drafting, searching, summarizing, and exporting styled documents (docx, PDF, HTML, Markdown) across large files via 38 MCP tools.

What is PandaDoc MCP? PandaDoc MCP (Model Context Protocol) server connects AI agents directly to PandaDoc's document workflow — creating, sending, and tracking contracts and agreements without leaving your AI client. How to use PandaDoc MCP? To use PandaDoc MCP, connect your PandaDoc account, provide the required API credentials, and configure the appropriate MCP client.

Measures and improves how findable, readable, and usable a website is to AI answer engines and autonomous AI agents through hosted AEO audit tools.

Enables AI agents to compare documents, analyze changes, and retrieve document info using GroupDocs.Comparison. Supports various formats and includes licensing for production use.

Search, document and execute authenticated API calls across all your apps (Gmail, Slack, Stripe, Notion, GitHub, and more) through 4 universal tools whose context footprint stays constant no matter how many connections you add. Hosted remote server with OAuth at https://mcp.withone.ai/mcp, or run locally via npx @withone/mcp.