"How to query data from ServiceNow" matching MCP servers:

MCP server that ingests PDF documents into pgvector for semantic search and RAG pipelines. It handles extraction, chunking, local embeddings, and storage, enabling agents to make PDFs searchable via natural language.

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

Enables semantic search and retrieval over local Markdown/MDX documentation using Node.js-based embeddings. Supports multi-language documentation with offline vector indexing and MCP tool exposure for AI assistants.

A lightweight RAG system that provides an MCP server for searching and interacting with vector-based knowledge bases. It enables users to perform retrieval-augmented generation and search across Qdrant collections through a standardized interface.

Indexes local files (PDF, TXT, CSV, Markdown) with embeddings for semantic search. Provides both CLI and MCP server interfaces so Claude Desktop can search and read your local documents.

A Model Context Protocol (MCP) server that enables LLMs to interact directly the documents that they have on-disk through agentic RAG and hybrid search in LanceDB. Ask LLMs questions about the dataset as a whole or about specific documents.

Provides persistent memory for AI coding agents via MCP, enabling teams to share and recall facts across sessions. Automatically captures, classifies, and curates knowledge from supported transcript sources.

Semantic memory for AI builders: capture the tacit engineering know-how that never reaches your docs, recall it the moment it applies. Built in Rust on Postgres and pgvector.

Enables LLM hosts to retrieve live, relevant documentation excerpts from official library docs sites via a search-and-RAG tool, avoiding reliance on training data.

askDB is an MCP server that retrieves relevant database schema (DDL) from a Pinecone index and provides it to LLMs to write SQL, without connecting to the database itself.

Enables natural-language search over locally indexed files such as markdown, text, images, videos, and PDFs, and retrieves indexed text or media metadata by path. It lets Cursor query a local embedding index built with Gemini and SQLite.

Enables Claude to scrape, crawl, extract data, and sync to vector databases using scrapedatshi's RAG pipeline, with support for multiple embedding and vector DB providers.

Enables MCP clients to ingest invoice PDFs into a hybrid-chunked FAISS index, semantically search them, and extract typed invoice data with deterministic arithmetic validation. It also provides listed-company financials and current web search results so agents can research vendors.

An MCP server that provides queryable access to Anti-Money Laundering (AML) red flag knowledge from regulatory documents. It enables compliance officers to ask natural-language questions and receive relevant, sourced red flags from a local vector database.

A personal memory MCP server that ingests AI agent conversation logs from multiple platforms into a searchable PostgreSQL+pgvector database, enabling cross-session recall of past reasoning and decisions.

Enables semantic retrieval over locally indexed PDF manuals and books, exposing tools to list available sources and search excerpts by query with page, chapter, source, and relevance metadata.

RAG MCP server for PDFs, YouTube, GitHub repos, and Discord exports. Index documents and query with citations via LangChain and Chroma.

MCP server for semantic code search with AST-aware chunking, hybrid vectors, and query syntax.

Enables agents to run semantic search across one or more local project directories by automatically maintaining a LAN-local Qdrant index with Ollama embeddings. Indexing, staleness checks, and incremental updates happen transparently, so users can query code by meaning without managing collections, chunks, or hashes.

Enables AI agents to query a local knowledge graph built from document collections using hybrid search (BM25 + vector fusion) and entity-relationship extraction. Supports privacy-first, offline operation with tools for semantic search, entity graph exploration, and corpus statistics.

Enables answering Formula 1 FAQ questions via vector search and web search using Bright Data.

MCP server for querying a page-citable research knowledge base built from PDFs, with exact filename and page citations.

A Model Context Protocol (MCP) server that provides a local-first RAG engine for your markdown documents. It uses a file-based Milvus vector database to index your notes, enabling LLMs to perform semantic search and retrieve relevant content from your local files.

RAG Vault lets your AI coding assistant search your private documents locally. Everything runs on your machine and your data stays private.

A semantic search system for Claude Code transcript history, exposed as an MCP server so Claude Code can query its own past sessions.

Enables MCP clients to understand song lyrics and meaning, find lyrically similar tracks, build mood playlists, and analyze artists and listening data across Spotify, LRCLIB, Last.fm, and ReccoBeats.

Python MCP server for vector search using Qdrant vector database and Ollama embeddings, with advanced query techniques like query expansion, HyDE, and reranking.

Local-first agent-memory MCP server with a why() tool: recall a fact together with its connected subgraph (multi-hop), so linked memories surface even when they share no words with the query. remember/recall/relate/forget/why over one fused vector + graph + columnar engine a single offline Rust binary.

A server that provides data retrieval capabilities powered by Chroma embedding database, enabling AI models to create collections over generated data and user inputs, and retrieve that data using vector search, full text search, and metadata filtering.

Provides Claude Code read-only access to PostgreSQL for relational queries, schema inspection, EXPLAIN plans, pgvector similarity searches, and Apache AGE graph traversals.