"A guide to creating software project documentation" matching MCP servers:

Local workspace memory for Claude Desktop. Indexes documents into a vector store with hybrid search, cross-session memory, auto-learn, and knowledge graph.

A self-contained Claude Code plugin that profiles a project by running a fixed DAG to emit agent-facing context pages, a human/developer guide, and a queryable vectorstore.

Provides semantic search capabilities over the Plesk Extensions Guide documentation using Retrieval-Augmented Generation (RAG) and vector embeddings. It enables AI assistants to retrieve relevant technical information and answer natural language queries regarding Plesk extension development.

Local-first document management and semantic search for AI coding agents, with a built-in web dashboard and REST API.

Routes coding agents to the most relevant project documentation (decisions, intent, constraints) with provenance and freshness, providing tools for task routing, knowledge search, and document context.

Enables AI assistants to fetch, index, and perform semantic RAG-based searches on API documentation from various sources. It provides tools for hybrid search and collection management, allowing users to access up-to-date documentation from projects like Gemini and FastMCP.

Provides MCP tools for searching, retrieving, and answering questions about indexed Nextcloud developer documentation from multiple sources.

Enables AI agents to search and query documentation from git repositories using hybrid search and structured metadata queries.

Enables semantic search over a software project's Markdown and text documentation by indexing document chunks in Qdrant and exposing retrieval through MCP tools.

Improves AI agent task performance by providing task area segmentation, hierarchical RAG, and hybrid search capabilities.

Enables AI agents to manage, upload, chunk, and semantically search local documents through MCP tools and a REST API, with a built-in web dashboard and optional Gemini-powered AI search.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

A Python MCP server that gives LLMs persistent, searchable access to project context — documentation, architecture decisions, and session notes.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

Hardware-accelerated codebase mapping that indexes Git repositories into Postgres/pgvector and serves code search, relationships, and static analysis results via a stdio MCP server.

A tool that creates MCP (Model Context Protocol) servers from OpenAPI/Swagger specifications, enabling AI assistants to interact with your APIs.

Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.

Enables hybrid retrieval-augmented generation with semantic and keyword search over documents, supporting multiple formats and providing MCP integration for Claude Desktop.

A guide for implementing Model Context Protocol (MCP) servers that provide AI models with external tools like web search, text manipulation, and mathematical operations.

Provides project-aware context for coding agents by selecting relevant AGENTS.md, docs, module paths, and decision records based on task, reducing redundant context loading via MCP stdio.