"How to Build a Knowledge Base" matching MCP servers:

Provides semantic search over a self-building personal technical knowledge base that automatically collects and distills content from GitHub Trending, AI news, and arXiv.

Exposes the Grok Build CLI as MCP tools for Claude Code and other clients, enabling headless AI agents to perform tasks like code reviews, web research, and file operations with configurable permission levels.

Stop paying for your agent to rediscover what other agents already figured out. Prior is a shared knowledge base where agents exchange proven solutions — one search can save 10 minutes of trial-and-error and thousands of tokens. Your Sonnet gets access to solutions that Opus spent 20 tool calls discovering. Search is free with feedback, and contributing earns credits.

Enables coding agents to add a bounded semantic-judgment layer for routing, ranking, extraction, verification, and escalation, returning typed signals and review recommendations.

An MCP server that provides expert knowledge on the OpenAI API, powered by RAG. It enables users to ask technical questions and get accurate answers, with support for search and fetch tools.

Enables users to ingest documents into a PostgreSQL/pgvector knowledge base, run semantic search over them, and get grounded answers through a retrieval-augmented generation pipeline backed by a free LLM. It also lets clients spin up specialized AI agents on demand and exposes knowledge-base stats and configuration as resources for tutoring workflows.

MCP server for hybrid medical consultation integrating Western and Traditional Chinese Medicine, enabling symptom analysis, diagnosis, and treatment suggestions through two specialized models.

A local, fully vectorized computational engine for stock market analysis that enables AI to perform factor calculation, strategy backtesting, IC analysis, and GPU-based multi-dimensional visualization using local DuckDB data.

Enables Claude Code agents to perform structured decision-making through TypeSafe Jev, supporting yes/no checks, route selection, scoring, and batched multi-question judgments with confidence values.

Enables to build and query a knowledge base with retrieval-augmented generation, supporting document ingestion, hybrid search, and live data integration from external APIs via MCP tools.

An MCP server that lets LLM agents accumulate organizational knowledge across sessions, scoped by domain, backed by Neo4j with enforced structural invariants.

Dynamic multi-agent debate MCP server for structured argumentation and reasoning among AI agents.

Enables AI assistants to perform Google searches with real, resolvable sources and transparent search terms.

Provides local, deterministic scoring and detailed auditing of text for AI-writing patterns via two read-only tools, with no network calls or language models.

Predicts the outcome of Dutch court cases dismissed, partly granted, or granted using a model trained on 609,715 rulings, with the outcome leakage measured and removed: 92% of Dutch judgment texts state the outcome verbatim, 0.1% remains after the cut, giving 78.2% accuracy against a 43.7% majority baseline. The lekkage_check tool exposes that same measurement so anyone can test a datase

Provides AI assistants with real-time web search, reasoning, and research capabilities through Perplexity's Agent API and Search API, enabling tools for search, conversational Q&A, deep research, and advanced reasoning.

Enables AI agents to access Cloudflare Workers AI for serverless LLM chat completions, text embeddings, and image generation via MCP tools.

Review and rewrite LangChain, LlamaIndex, Ollama, and XRPL code against current APIs, plus runnable examples, import lookup, and XRPL tx skeletons that never submit. Live docs included. $0.001 USDC on Base or 1000 drops XRP.

Exposes the four Ejentum cognitive harnesses (reasoning, code, anti-deception, memory) as MCP tools any agentic client can call. Drop-in scaffolding that catches LLM failure modes like sycophancy, hallucination, and reasoning shortcuts.

Seven-layer agent memory over MCP. zenbrain_store routes each memory itself: steps become a procedure, emotionally weighted content an episode, high-confidence content a pinned core memory, anything else a semantic fact. zenbrain_recall tags every result with its layer; zenbrain_consolidate runs one sleep-like maintenance pass. Local SQLite file, no account, no network call.