"Understanding Vector Search" matching MCP servers:

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

A meta-MCP server that aggregates authoritative web search and browser research tools, providing a unified interface with a cost-efficient fetch ladder for web research.

An MCP server that exposes an Agno agent with Google Gemini reasoning and Serper web search, enabling MCP-compatible clients like Claude Desktop to perform agent-driven web searches and receive synthesized answers.

A multi-engine web search MCP server that integrates with coding agents, offering parallel search, page fetching, X/Twitter search, deep research, and configurable free/API layers.

A Model Context Protocol (MCP) server that provides intelligent search capabilities for discovering relevant Claude Agent Skills using vector embeddings and semantic similarity. This server implements the same progressive disclosure architecture that Anthropic describes in their Agent Skills enginee

An MCP server that enables web search capabilities using OpenAI's o3 model, allowing AI assistants to perform text-based web searches and return AI-powered results.

Central registry for AI tools with vector search and knowledge graph, enabling intelligent tool discovery, chaining, and execution via MCP.

Enables Claude Code environments lacking built-in web search to run web searches through the locally installed and authenticated Antigravity CLI, returning concise answers with real source links.

A Model Context Protocol Server search realtime flight detail with multiple fligh carrier, price , stop , time duration for any given date using simple prompt

A Model Context Protocol server providing vector database capabilities through Chroma, enabling semantic document search, metadata filtering, and document management with persistent storage.

Enables AI-powered web searches using Google's Gemini 2.5 models with Google Search Grounding. Supports multiple Gemini models (Flash, Flash-Lite, Pro) with configurable thinking budget and web search capabilities.

Exposes SearXNG web search and query autocomplete to AI clients, returning ranked results, instant answers, and suggestions with filters for category, language, time range, engines, page, and safe search. It is distributed via npx and requires a self-hosted SearXNG instance with the JSON format enabled.

MCP Compass is a discovery and recommendation service that assists AI assistants in finding and understanding Model Context Protocol servers through natural language queries.

Enables real-time web search with source citations by leveraging Perplexity AI's Sonar models through SAP AI Core. It integrates with Claude Code to provide deep analysis, reasoning, and up-to-date information from the web.

A service discovery and proxy for MCP servers that enables registration, discovery, and execution of tools on remote MCP servers. Uses vector-based similarity search through Alibaba Cloud services to intelligently route requests to appropriate MCP services.

A Model Context Protocol server that allows Claude to search and display Pokemon Trading Card Game cards with filtering by attributes like name, type, legality, and statistics.

A production-grade MCP server for integrating RAG into AI agents, supporting multiple vector databases with enterprise security and dynamic tool selection.

An advanced contextual memory server for agent orchestration providing chronological history, semantic vector search via Redis Stack, and temporary scratchpad storage. It also enables context compression through history rollups and facilitates inter-agent communication using a Redis-based Pub/Sub system.

Official Kimss MCP server — call your Kimss agents, models, files, and vector stores from Cursor and other MCP clients using your API key.

Enables free-first web search in English and Chinese with no API key required, returning compact multi-source evidence while keeping provider failures, stop reasons, and budget limits visible. Optional paid providers are used only when routing policy and credentials allow.

A standalone MCP gateway/proxy server that lets any MCP-compatible client search and call tools across multiple downstream MCP servers through a single mcp proxy tool.

Enables AI agents to perform web search, extraction, and research across multiple provider backends with persistent multi-key rotation and auditable routing. Supports MCP, CLI, and Agent Skill entry points.

Enables web search for the pi coding agent using z.ai GLM native search with a DuckDuckGo fallback.

Enables real-time communication and shared vector memory between Claude Code, OpenAI Codex CLI, and Google Gemini CLI. It allows different AI platforms to exchange messages, share context, and collaborate through a unified MCP interface.

Enables free-first English and Chinese web search through zero-key sources, returning bounded multi-source evidence with visible provider failures and optional paid escalation under policy.

A server that enables document searching using Vertex AI with Gemini grounding, improving search results by grounding responses in private data stored in Vertex AI Datastore.

Enables English and Chinese web search through zero-key sources plus optional paid providers, returning compact multi-source evidence with visible provider failures and bounded request and evidence budgets.

Enables free-first English and Chinese web search through zero-key engines plus optional paid providers, returning compact multi-source evidence with bounded token and request budgets. Each result exposes provenance, independent source counts, routing decisions, and visible provider failures so agents can verify claims rather than receive unexplained empty results.

Enables parallel research across multiple cheap AI worker models, splitting a question into sub-questions and synthesizing one sourced answer with references.

Enables clients of tool-heavy MCP aggregators to find and use upstream tools on demand through four fixed bridge tools (search, describe, call, refresh), cutting per-turn tool context from ~180k tokens to ~2–3k. It merges multiple upstream servers behind sticky sessions and a local lexical BM25 index, so only the schemas actually needed are retrieved.