"A search for the term 'Prompt'" matching MCP servers:

Provides custom prompts for Amazon Q by serving markdown files from the local filesystem as prompt templates through the Model Context Protocol.

Enables semantic and full-text search over the official Model Context Protocol registry to discover MCP servers for integration.

Give long term memory to your agents without building the RAG pipelines.

Enables MCP-compatible agents to retrieve Chinese expert documents, prompt examples, and task skeletons before writing Chinese prompts.

Retrieval-grounded prompt refinement engine that repairs and polishes prompts via a single LLM call, exposing a refine_prompt tool for integration with MCP-compatible hosts.

Provides a Google search tool via the Serper API, returning structured results and a summary answer for LLMs like Claude.

Enables AI agents to provide e-commerce customer service with product search, RAG-based FAQ answers, human handoff, and automated security auditing with a review workflow.

A local MCP server that gives AI assistants persistent long-term memory using a biological neural architecture with cortex, synapses, and hippocampus.

Provides versioned, structured memory for AI agents, allowing them to store facts, detect conflicts, and track knowledge history via a hosted SaaS platform. It enables efficient hierarchical information retrieval and semantic search while keeping token usage constant as memory scales.

Your coding agent remembers how it solved past tasks and reuses it next time: trigger, steps, outcome, lessons. Hooks recall the right skill before a task; skills strengthen with confirmed use and unused ones decay. Local SQLite, no API key, nothing leaves your machine. 9 MCP tools, works with Claude Code, Codex and Cursor. Agent-written memory stays untrusted until you approve it.

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.

Local long-term memory shared by every AI coding assistant on one machine: a single SQLite file with FTS5 search and optional multilingual semantic search, no daemon and no cloud.

Exposes an internal engineering knowledge base to AI assistants, allowing users to search and retrieve standards, runbooks, and architecture decisions. It supports RAG-enhanced search, document scraping, and specialized prompts for incident investigation and code reviews.

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.

Enables semantic search across documentation stored in Gemini FileSearchStores, returning AI-generated answers with source citations.

MCP server for Doubao Search that enables real web search in any MCP client, backed by ByteDance-exclusive Chinese sources and the open web.

Self-hosted long-term memory gateway for AI clients, deployed to your own Cloudflare account (Workers + D1 + Vectorize). It captures conversations, consolidates them overnight, and exposes MCP tools to search, recall and write memories from any MCP client.

Persistent long-term memory for AI coding assistants. Local SQLite, no cloud - 16 MCP tools to store, search, relate, and consolidate typed memories with a confidence lifecycle, hybrid BM25 + semantic search, namespaces, a knowledge graph, and a built-in OS keychain credential vault.

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.

An intelligent memory MCP server that provides AI applications with semantic search, entity extraction, and knowledge graph capabilities using local Redis caching and optional cloud sync. It enables LLMs to store and retrieve long-term context across sessions with high-performance multi-tier storage.