"A server for finding company information and data" matching MCP servers:

Enables AI agents to query and manage company knowledge from a Supabase GraphRAG store, including accounts, meetings, people, org hierarchy, product docs, and call logs.

Enables querying a folder of company documents over MCP and retrieving matching passages with file:line citations for Claude to answer from. Supports keyword and multilingual hybrid search over an incrementally indexed SQLite knowledge base.

A local AI assistant that answers questions across a company's internal knowledge using MCP, providing controlled access to internal documents with role-based permissions and citations.

Enables AI agents to query structured data refined from unstructured web sources, including developer breaking changes, B2B pricing matrices, regulatory compliance, semantic search, and on-demand URL refinement.

Enables MCP clients to ask bilingual data questions, inspect database tables and schemas, and execute validated read-only SQL through exposed tools.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.

An Operit-compatible adapter of the Exa MCP server, enabling web search, code search, and company research capabilities in AI assistants. It fixes MCP handshake compatibility issues, allowing tools like web_search_exa and web_fetch_exa to load and run reliably.

An MCP server that runs Apify scraping actors with quality-gated validation, scoring, and retries, returning embedding-ready chunks for AI agents.

Provides semantic search and access to Contraption Company blog posts and essays. Uses AI-powered embeddings to find relevant content and supports real-time updates via webhooks.

Enables AI coding assistants to search and cite company Markdown knowledge stored in GitLab repositories using a local, read-only index.

MCP for Azure DevOps Boards is a MCP server that lets your favourite AI browse, query and update Azure DevOps work items as if it were a project manager. Written in Rust and optimized for tokens usagem, It runs via stdio or HTTP mode and uses standard Azure authentication with az login.

A semantic layer query engine with MCP support, enabling AI assistants to query structured data through natural language and declarative interfaces.

A proof of concept MCP server for Apache OFBiz

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

Enables unified access to SQL, semantic vector search, and columnar analytics through a single MCP interface.

Enables Claude to answer plain-English questions by listing and describing database tables, running read-only SQL queries, and searching a folder of policy documents.

Enables asking questions and performing semantic searches against an Inkeep knowledge base. Deployable to Vercel with Next.js.

Multi-datasource MCP server that connects AI assistants to 6 database types (MySQL, PostgreSQL, ClickHouse, MongoDB, SQLite, Huawei DWS) with dynamic configuration, encrypted credential storage, and schema discovery.

Enables natural language querying of PostgreSQL via MCP, combining SQL, vector search, and knowledge graph with automatic routing and token-aware curation.

Enables querying and analyzing user, product, and order data with filtering capabilities and real-time statistics. Supports WebSocket connections to XiaoZhi AI platform with automatic reconnection.

Automatically routes natural language questions to RAG or Text2SQL paths to answer queries about virtual construction site data, supporting semantic search and structured aggregation.

Enables grounded question-answering over internal documents via a single MCP tool that retrieves relevant passages and generates answers with citations, returning sources and diagnostics.

Secure Edge Gateway for Data Normalization.

A TypeScript-based server to interact with ArangoDB using the Model Context Protocol, enabling database operations and integration with tools like Claude and VSCode extensions for streamlined data management.

Enables AI agents to maintain persistent, searchable two-layer memory with 37 tools, hybrid search, knowledge graphs, and enterprise features like authentication and backups.

Enables MCP clients to search ingested documents and ask citation-backed questions through the search_docs and ask tools.

Enables loading CSV text, filtering and aggregating datasets over Streamable HTTP, and optionally using Amazon Bedrock to turn query results into concise answers suitable for speech.

Enables querying PDF documents using natural language with grounded answers and source citations via a local RAG pipeline.