"Search for information about 可灵" matching MCP servers:

Enables secure read-only access to Tencent Lexiang knowledge bases via MCP, including listing directories, RAG search, and reading entries.

A local web search MCP server supporting parallel, API, and model-based search modes, integrating Zhipu Web Search API and online models for real-time information retrieval.

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

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

提供基于阿里云函数计算的MCP远程网页搜索服务，支持Exa、Tavily、豆包和Bocha多个搜索引擎的自动回退。

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

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.

Read-only MCP server for a content contract layer: protocol, modules, pipelines, assets, library. 一句话 / One-liner（ZH）：给任何 AI agent 一个可校验的内容规范与资产底座——先查契约，再落笔。 描述 / Description：只读 MCP 服务器：把长内容的验收标准（协议 / 模块 / 管线 / 资产 / 馆藏）变成 agent 可调用的检索与取件工具面。10 个只读工具 + 1 个 prompt（assemble_guide）；支持 modern 2026-07-28 与 legacy 2025-11-25 双协议；零第三方依赖（Python 标准库）；无写路径。 仓库 / Repo：https://github.com/Monyeah777/NinFenz 站点

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.

将 pgvector 知识库检索能力封装为标准 MCP 服务器，支持 Tools、Resources 和 Prompts，使 Claude Desktop 等客户端可通过自然语言查询知识库。

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.

Enables AI agents to search, browse, and book hotels from a database of 2 million properties worldwide. Provides comprehensive hotel search capabilities with location lookup, filtering by amenities, detailed property information, and integrated booking functionality.

Provides AI agents with 12 real-time tools for search synthesis, grounded research dossiers, claim verification, citation generation, sentiment analysis, expert ranking, timeline creation, and competitive landscape mapping over Streamable HTTP or stdio.

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

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 semantic search over ByJG's documentation, letting LLMs answer questions about ByJG libraries and cite the pages where answers were found.

A stdio MCP server that provides a single tool to query Gemini with Google Search grounding for current web information, returning synthesized answers with source links.

Universal search engine for AI agents. Discover products, services, and businesses across every category. 10 MCP tools, zero LLM calls, millisecond responses.

Local semantic recall MCP server for citation-integrity workflows. Enables semantic search over a text corpus, returning file paths and page numbers for verification, with 100% local processing using ONNX embeddings and LanceDB.

Enables natural-language semantic search over your own local files through an MCP server, fully offline without API keys or a server daemon, with optional LLM-grounded answers and exact file:line sources.

Enables AI assistants to search a library of over 20,000 open legal skills by keyword, by meaning, or a hybrid of both, filtering by jurisdiction, language, practice area, or tag. It also returns facet counts for the collection and retrieves the full text of any selected skill.

Enables searching and retrieving documents from a local folder to ground LLM answers in your files.

An MCP server that indexes a repository locally and provides keyword, semantic, hybrid, and SQL search tools, enabling coding agents to answer questions about the codebase efficiently without reading files one by one.

Connect AI assistants to Manticore Search. Execute SQL queries, list tables, get schemas, and fetch documentation. Perfect for building RAG applications and search-powered AI agents.

The Search MCP Server enables seamless integration of network and local search capabilities in tools like Claude Desktop and Cursor, utilizing the Brave Search API for high-concurrency and asynchronous requests.

Enables AI assistants to search and ask questions about documents stored in AnythingLLM workspaces through MCP tools.

Enables natural-language search over locally indexed files such as markdown, text, images, videos, and PDFs, and retrieves indexed text or media metadata by path. It lets Cursor query a local embedding index built with Gemini and SQLite.

An MCP server that provides persistent memory capabilities for AI agents using Mem0, enabling storage, search, and management of contextual information across conversations with support for multiple backends and LLM providers.

Federated, local-first search for AI agents: one query fans across transcripts, files, a knowledge graph, a vector store, the live web, and YouTube, fused by trust-weighted RRF into one ranked answer. Five modes (grep/lexical/semantic/hybrid/rerank), runs entirely on your machine over MCP.