"A command-line fuzzy finder" matching MCP servers:

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.

MCP server for the DeepGHS anime AI ecosystem. Dataset discovery, tag search, character dataset finder, and training pipeline code generation.

A fast, private, read-only search for your second brain or any folder of Markdown notes. Your AI agent finds the right note and the exact line, with the note, heading and date as the source.

Enables AI assistants to search a curated database of 200+ AI tool cards with pricing, platforms, use cases, and source links, so users can ask which tool to use for a task and get grounded recommendations.

Provides a read-only interface to audit and continue coding agent sessions by extracting plans, intents, and edit authorship from history across multiple agents (Claude, Codex, OpenCode, Antigravity, Pi) via MCP, CLI, and Python SDK.

Lets MCP clients search vetted public listings and their own private library of coding-agent sessions, load a saved session as bounded starting context, and save the current transcript privately, reusing an existing command-line sign-in. Sessions are scrubbed and framed as untrusted reference material, and acquiring a paid listing never completes a purchase automatically.

Enables agents to find lines in local text files by meaning rather than exact keywords, judging each nonblank line against a natural-language intent and returning scores for every line. It complements lexical search by running locally over stdio and calling a hosted model for the semantic comparison.

Provides a compiled knowledge substrate, extracting atomic claims from an append-only capture log and querying a bitemporal claim graph over MCP. Read-only by default, with opt-in writes for trusted sources.

Enables AI coding agents to intelligently index and search codebases with sub-20ms retrieval, 8x memory compression, and cross-encoder reranking via MCP stdio.

A local MCP server that indexes files in a directory using Gemini Embedding 2, enabling AI agents to perform semantic search over local documents.

Automatically synchronizes meeting transcripts from Otter.ai to a local SQLite database for easy browsing and management. It enables semantic and keyword-based search across meeting history through integrated MCP tools for Claude and Cursor.

Serves a domain's knowledge as a routing table: the agent reads one line per area saying when it is relevant, picks, and only then opens documents. Tools walk the table, read a document, and read another backbone over a temporary link.

A Model Context Protocol server that generates prompts based on Git repository content, including a command to generate PR descriptions from diffs.

Enables interactive AI chat with document retrieval and command-based tool integrations via the Model Control Protocol.

Enables AI assistants to intelligently select and switch between different AI models (OpenAI, Anthropic, etc.) within the same conversation based on task requirements. Provides a unified interface for accessing multiple AI providers through a single MCP tool.

Provides read-only hybrid RAG search and discovery over a local-first AI knowledge corpus, enabling semantic and keyword search, browse, digest, and status tools.

An MCP server for indexing and searching Commodore 64 documentation using full-text, semantic, and fuzzy search across multiple file formats. It enables RAG-based question answering, entity extraction, and interactive timeline or knowledge graph visualizations.

Provenance-first agent memory MCP server with a real corrections API (assert/retract/supersede with tombstones) and a one-command junk audit for memory stores.

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 an MCP protocol interface for interacting with Elasticsearch 7.x databases, supporting comprehensive search functionality including aggregations, highlighting, and sorting.

Enables AI agents to persist, search, and evolve knowledge through a Markdown vault with a typed knowledge graph and MCP interface.

Pre-trade risk validation and position sizing for AI trading agents via G-formula and Iron Fist.

Gives AI agents a persistent long-term memory store over MCP, letting them remember facts and preferences, recall ranked memories with a one-line explanation of why each scored, mark which recalled memories were used or ignored so retrieval learns over time, and forget or list memories per namespace.

缔零法则MCP是基于LLM和RAG技术搭建的实现完全替代人力的全自动化风险识别的内容安全审查平台，致力于通过代理AI技术减少人力成本，高效高精度为用户提供分钟级接入的内容风控解决方案，破解安全威胁，提供从风险感知到主动拦截策略执行的全链路闭环与一体化解决方案。This MCP tool is an AI-powered content security review platform built on LLM and RAG technologies, designed to achieve fully automated risk identification that completel

MCP server for Vectros, a typed multi-tenant record store with hybrid search and citation-grounded RAG, enabling agents to query, search, and ask questions over their own indexed data.

Enables Codex to list and read saved Claude Code conversations, retrieve incremental context updates before replies, and optionally ask Claude text-only follow-up questions within a selected session without disrupting the workflow.

Enables coding agents to search a locally indexed codebase with hybrid dense, lexical, and symbol-graph retrieval, returning ranked passages with exact file:line-line citations plus graph context and index status. It keeps its index in sync via a file watcher and runs as a local sidecar with no hosted service to deploy.

Enables MCP clients to query previously analyzed GitHub repositories for architecture analysis, code review findings, and single-turn code Q&A with verifiable file and line references.

Provides local-first retrieval over long design conversations, enabling users to find where a decision was made, why, and what replaced it, with turn and line range citations through hybrid retrieval, typed judgment rerank, exact search, and five MCP tools.

A command-line tool that transforms any OpenAPI service into a Model Context Protocol (MCP) server, enabling seamless integration with AI agents and tools that support the MCP specification.