"Answer" matching MCP servers:

Provides tools for Bazi (Chinese astrology) chart calculation and analysis, enabling LLMs to generate accurate birth charts, determine patterns, and answer follow-up questions based on actual calculations rather than model knowledge.

Enables AI assistants to answer high-trust sales questions with cited, vetted facts or explicit refusals, grounded in a local knowledge base.

MCP server exposing memory search, index, and stats tools for agents, with honesty guards to prevent re-litigation of settled decisions.

Durable hybrid memory for AI agents. Combines vector search, BM25, temporal retrieval, and optional Memgraph knowledge graph via reciprocal rank fusion. 6 MCP tools: health, search, answer, feedback, graph_query, graph_add_triple. Self-hosted with Qdrant backend.

Enables querying the DPC Zettelkasten knowledge base with full-text search, note retrieval, and structural GraphQL queries, each answer backed by pinned GitHub source citations.

Enables deterministic, token-minimal multi-hop retrieval from a vault of [[wikilink]] notes via the Model Context Protocol, allowing LLMs to answer questions by following authored links without vector databases or LLM calls during retrieval.

Enables AI assistants to search, retrieve, and browse Markdown documentation in a local folder, then answer questions with cited sources.

Provides a compressed knowledge graph of the NVIDIA AI developer stack for deterministic traversal, enabling agents to answer questions about dependencies and prerequisites with minimal tokens.

Exposes tools to ingest FigJam boards, retrieve context summaries, and answer free-form questions about the board content.

Enables AI agents to search and submit solutions, ask and answer questions, share findings, and vote on content via DebugBase's collective knowledge base.

Connects AI assistants like Cursor and Claude to your local Daylens timeline so they can answer questions about your past work sessions, no cloud or API keys needed.

Enables AI agents to share, search, and learn from structured lessons, ask and answer questions asynchronously, and contribute to a shared knowledge commons via MCP.

MCP server that gives AI coding assistants persistent memory, structural code graph analysis, and safe multi-agent coordination, enabling them to answer architectural questions, track decisions across sessions, and coordinate safely in multi-agent workflows.

A local document processing server that can index various document formats (PDF, DOCX, TXT, HTML) and answer questions based on their content using the Model Context Protocol.

MCP server for creating and managing Mindola lenses, which are grounded AI pages that answer questions from your own material with citations. Enables AI assistants to create lenses, add knowledge, and monitor status through natural language.

A local-first RAG engine that ingests documents (PDF, Markdown, images, etc.) and provides hybrid search, reranking, and LLM answer synthesis via MCP for AI agent integration.

Turns warehouse/lakehouse tables into a governed entity-relationship knowledge graph exposed through MCP, enabling AI agents to answer multi-table business questions without hard-coded SQL or large schema prompts.

A local-first MCP server that gives AI coding agents durable project memory, dependency graphs, and impact analysis to answer team knowledge and cross-file change questions before editing.

Enables AI clients to record research-process artifacts (agents, sources, claims, uncertainty, disagreement, tool usage) and compile a hosted process X-Ray report to inspect what a polished final answer may hide.

Provides semantic search and listing over a unified personal knowledge graph aggregating LinkedIn, GitHub, course completions, and more, enabling MCP clients to answer questions about skills and experience with evidence-backed results.