"Vector Database" matching MCP servers:

Enables AI agents to semantically search GitHub repository documentation by automatically fetching, vectorizing, and indexing content into an Upstash Vector database. It provides a standard MCP interface for agents to retrieve relevant documentation snippets through natural language queries.

Enables AI agents to navigate large Markdown libraries structure-first — walking library → folder → document → section and reading original text with line numbers, with keyword search as fallback — so answers stay explainable and citable without embeddings or a vector database.

Enables precise extraction of PDF content, including text coordinates, vector shapes, images, and on-demand raster rendering, for use cases like reconstructing design files.

MCP server for the StitchAPI docs: search_docs (hybrid BM25+vector) + get_doc. Local/offline npm package (@stitchapi/docs-mcp, docs bundled, no per-query network) and a hosted Streamable-HTTP server (stitchapi.dev/api/mcp).

MCP server that parses 3GPP .docx specifications into structured sections with full-text search, letting LLMs browse, search, and follow cross-references without a vector database.

Provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.

Enables LLMs to chat with long PDFs using a reasoning-based, tree-structured document index that navigates content like a human would, without requiring vector databases or hitting context limits.

A static, read-only MCP server that exposes a database knowledge base (schema, relationships, workflows, and reasoning patterns) so Claude Code / Copilot can reason about a database without a live connection.

An MCP server that provides AI assistants with access to Multi Theft Auto: San Andreas function documentation through vector similarity search and smart keyword expansion. It enables efficient information retrieval with features like deprecation warnings and SQLite caching for technical documentation.

Enables creating executive presentation suites from a single prompt, generating PowerPoint, interactive web deck, KPI dashboard, whiteboard infographic, and vector PDF files through MCP.

Converts vector PDF circuit schematics into evidence-preserving SchematicIR JSON with tools to inspect, convert, and validate schematic PDFs.

Enables semantic search over local markdown documentation by indexing files and ranking results using vector similarity and BM25 fusion.

An offline-first MCP server for querying Google ADK documentation using vector search, enabling AI models to access and understand ADK docs.

Enables MCP clients to search and browse 1C:Enterprise syntax help, with full-text, vector, and hybrid search, page/chunk retrieval, hierarchy navigation, related-page lookup, and index rebuilding.

Enables question answering over technical documents (Markdown, TXT, PDF) using hybrid retrieval (vector + lexical) with cited sources and audit logging, fully locally with optional cloud fallback.

Enables vector similarity search and serving of Svelte documentation via the MCP protocol, with support for local caching and multiple llms.txt documentation formats.

Local MCP server for querying Oli/LimX documentation via keyword, vector, or hybrid search, with citation support for use in Claude Code or OpenCode/August.

Unified MCP server that orchestrates 5D Database and Documentation System plugins, providing AI agents with centralized tools for database queries, semantic search, documentation validation, and workflow coordination.

MCP server for hybrid documentation search (keyword + vector) and RAG chat, enabling natural language queries with cited answers from indexed repositories.

A Model Context Protocol server that provides search and retrieval capabilities for JSer.info's JavaScript resource database, enabling access to items, posts, product information, and timeline data through various specialized tools.

Lets coding agents search a local Markdown docs folder, ask questions answered with linked source passages, and add or replace pages. Backed by Postgres full-text search with no vector database required.

An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context

Enables semantic search of project documentation using hybrid vector and full-text search with fast and deep query modes for immediate results or complex multi-round synthesis.

Enables IDEs like Cursor, Windsurf, and Claude Code to access and query Appwrite documentation through a vector-powered search system. Provides developers with contextual Appwrite documentation directly within their development environment.

Aggregates documentation from multiple sources (llms.txt format or web scraping) and provides semantic search capabilities using vector embeddings and hybrid search for each documentation source.

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.

Enables coding agents to retrieve relevant sections from a repository's declared documentation scope using hybrid vector and full-text search, with incremental indexing.

Enables semantic search across Cairo and Starknet documentation, providing AI assistants with precise code examples and documentation chunks via vector search.

A Model Context Protocol (MCP) server that coordinates AI agents to transform database tables into Wikipedia-style data dictionaries.

Enables multi-project workspaces to share structured notes, API contracts, and handoff messages via a local SQLite database, with versioning and read tracking.