"General or unclear search query" matching MCP servers:

A federation MCP server that sits in front of multiple memory backends and presents a unified search surface to AI agents, allowing a single query to search across knowledge graphs, session history, and web search.

Read-only MCP server that exposes your local Notion backups to AI agents — query databases, read pages, search a backed-up workspace. Offline by default: no LLM calls, no telemetry, no cloud.

Lets users manage master data through natural-language conversations with AI agents, enabling schema creation, record capture, validation, search, and query answering via 16 MCP tools backed by local JSON storage.

Gives your AI persistent memory across conversations. Stores facts automatically, finds them by meaning using hybrid search with query expansion, and organizes everything into topics without manual tagging.

Local MCP server that manages a job search using Markdown files, enabling skills tracking, gap analysis, and offer pipeline management via Claude.

Enables Claude chats to communicate across machines or sessions via shared encrypted rooms, with tools to create, join, send, read, wait, search, and delete room messages.

An MCP server that reads all your Claude Code project memory files and exposes them as tools. Lets any Claude instance — in any project, or via Claude.ai — query your full project history and preferences.

Memory vaults on your own machine, one per life domain, retrieved with a local BM25 index fused with vector search, where the human decides which vaults a model may answer from. Requires the Mnemosyne OS desktop app running locally; it also reports what other coding agents on the same machine are doing.

Local-first memory engine for AI-agent teams: private/team/project ACL, associative recall, and federated sync across nodes. One SQLite file, no LLM required.

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.

Shared memory MCP server for AI coding agents, enabling context sharing across sessions with local SQLite or cloud-based semantic search, compatible with Claude Code and Cursor.

Enables keyword search, filtering, and retrieval of Claude Code memory files, reducing context overhead by fetching only relevant memories on demand.

Enables code agents to persist and recall durable project knowledge — rules, decisions, procedures, learnings and secrets — across sessions at low context cost, with accent-insensitive PT-BR search, scoped per repository/workspace, and secrets that stay out of the model's context.

Enables AI assistants to query your local browsing history using keyword search, RAG Q&A, and activity stats.

Enables semantic search over markdown files to find related notes by meaning rather than keywords, and automatically detect duplicate content before creating new notes.

Structured knowledge API for AI agents — fractal knowledge graphs, signal datasets, authority evidence reviews, open problems, and capability specs. Pay-per-query or subscription via Lightning, Stripe, or USDC.

Enables AI assistants to query end-to-end encrypted memories from Cognia workspace via MCP. Supports search, retrieval, and action execution against connected services like Slack, Notion, and GitHub.

Enables AI assistants to query, modify, and reason over OWL/TTL/RDF ontology files using natural language, without needing SPARQL or OWL syntax.

A general-purpose MCP server providing web search, persistent memory storage, and secure code execution capabilities. It enables AI agents to search the web, store and retrieve data, and run Python/JavaScript code in sandboxed environments.

MCP server for Yandex Wiki with full-text search. Read and write pages, comments, attachments, and dynamic tables (grids); optional server-side read-only mode for agents. Docker-ready.

Enables MCP-compatible agents to query beliefs, search documents, and explore reasoning chains from expert-service knowledge bases.

A Model Context Protocol server that enables AI agents to query a Graphiti knowledge graph and pgvector document store for evidence-backed responses via hybrid search and RAG.

Enables AI agents to persist, search, and query shared memories in PostgreSQL through hybrid retrieval, preset answers, and AI-to-AI consultation via MCP.

Enables AI agents to gain persistent, structured memory that is semantically searchable and graph-traversable, allowing them to store and query records and relationships without managing embeddings or schemas.

MCP server for NaN Mesh, enabling AI agents to search entities, cast trust reviews, register agents, post content, and query trust scores from the AI trust network.

Enables Claude to search, query, and interact with an Enterprise Knowledge Management System (EKMS). Supports semantic search, knowledge recommendations, relationship graphs, and feedback recording for enterprise knowledge bases.

A Model Context Protocol server that enables AI models to perform real-time internet and knowledge searches through Higress, enhancing model responses with up-to-date information from Google, Bing, Arxiv, and internal knowledge bases.

An MCP server that provides graph-aware tools for interacting with Little Big Brain knowledge graphs, enabling search, query, commit, and configuration operations via natural language.

Enables AI coding assistants to query and store a team's historical development experience through semantic search, so lessons and decisions persist across conversations.

Enables Claude to build and query a local, folder-based knowledge base with hybrid vector and BM25 search, save and edit notes, ingest Markdown/text folders with source citations, and browse or restore version history — all running embedded on a laptop with no server or API key.

Enables MCP clients to query project-scoped Graphify graphs, search, read, and write Obsidian Markdown notes, and recall or store persistent ICM memories over authenticated private HTTPS. It serializes access per project and supports slim and standard Docker deployments without embeddings or extra memory services.

Grafema builds a queryable knowledge graph of your codebase and allows you to enforce structural invariants and query the system using Datalog or Cypher.

Provides a scalable knowledge graph implementation for Model Context Protocol using Elasticsearch, enabling AI models to store and query information with advanced search capabilities, memory-like behavior, and multi-zone architecture.

Integrates RAGFlow's knowledge base API with Claude Desktop for document retrieval, semantic search across multiple datasets, and intelligent query refinement using DSPy.

An MCP server that enables interaction with Markdown knowledge bases, allowing users to search and retrieve content by tags, text, URL, or date range from their local markdown files.

MCP bridge for PDF Content Search — full-text PDF search with Apple Vision OCR across thousands of documents in under a second from Claude, Cursor, or any MCP client. Advanced filters (date, category, sender, amount), wildcards, boolean operators. Bridge open-source (MIT), PDF Content Search app is commercial with free iOS+Android companion scanner apps.

Enables interaction with the RAGFlow API to manage knowledge base datasets and conduct AI-driven chat sessions. Users can list datasets, create chat assistants, and retrieve or query information from specialized knowledge bases.

Integrates Dify's external knowledge base API with the Model Context Protocol to enable AI agents to retrieve and query relevant information. It supports relevance scoring, metadata filtering, and flexible configuration through environment variables or command-line arguments.

Provides a comprehensive interface to a personal knowledge base, including tools to query professional skills, project history, and business strategy. It enables natural language search across personal profile data, resumes, learning roadmaps, and goal metrics.

Enables AI agents to read and write a bitemporal context graph per end-customer through five MCP tools, so they can query current or historical facts, explore relations, and record learned information with provenance.