v2.1.0 · MIT
Search & analytics data as infrastructure — MCP server for Google Search Console, Bing Webmaster Tools, Google Adsense and GA4, designed for AI agents and automation.
— · Apache-2.0
An autonomous agent that conducts deep research on any data using any LLM providers
— · MIT
Kindly Web Search MCP Server: Web search + robust content retrieval for AI coding tools (Claude Code, Codex, Cursor, GitHub Copilot, Gemini, etc.) and AI agents (Claude Desktop, OpenClaw, Hermes, etc.). Supports Serper, Tavily, and SearXNG.
v1.0.0 · no license
A Playwright-based Node.js tool that bypasses search engine anti-scraping mechanisms to execute Google searches. Local alternative to SERP APIs with MCP server integration.
v1.0.0 · Apache-2.0
Search and discover Agent Skills from the skills.sh registry. Powered by HAPI MCP server.
v0.1.0 · MIT
Web search for AI agents — one tool across 6 engines, routed to the cheapest + cached.
— · MIT
ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works with Claude Code, Codex, OpenClaw, or any LLM agent.
v3.24.0 · MIT
🔥 Official Firecrawl MCP Server - Adds powerful web scraping and search to Cursor, Claude and any other LLM clients.
v0.32.0 · AGPL-3.0
Fast, lightweight Firecrawl/Tavily alternative in Rust. Web scraper, crawler & search API with MCP server for AI agents. Drop-in Firecrawl-compatible API (/scrape, /crawl, /search). 2.3x faster than Tavily, 1.5x faster than Firecrawl in 1K-URL benchmarks. 6 MB RAM, single binary. Self-host or use managed cloud.
v0.18.0 · MIT
Local-first RAG server for developers. Semantic + keyword search for code and technical docs. Works with MCP or CLI. Fully private, zero setup.
v2.9.2 · Apache-2.0
Search ClinicalTrials.gov trials, retrieve study details and results, and match patients to eligible trials via MCP. STDIO or Streamable HTTP.
v1.0.0 · MIT
Persistent visual cache for LLM-driven software development. Caches screenshots using perceptual hashing, vector search, and AX trees to prevent token overhead and visual hallucination loops.
v0.1.142 · MIT
AI ad studio and marketing MCP server with 681 tools. Research the ads already running in any market, generate finished image, video and UGC avatar ads, publish and schedule them to your own channels, build and manage the ad campaigns behind them, and rea
v2.0.0 · MIT
Private web search for AI assistants via SearXNG — supports Claude, Cursor, and any MCP client
v0.2.1 · AGPL-3.0-only
The go-to web for your AI coding agent — local-first search, fetch, crawl & research over MCP. No API keys, no cloud, $0/query. Public beta.
v2.0.0 · Apache-2.0
Turn local files into searchable context for AI agents.
— · MIT
Fast and Accurate Code Search for Agents. Uses 99% fewer tokens than grep+read
v1.0.26 · MIT
A structural code search engine for Al agents.
— · MIT
Nornicdb is a distributed low-latency, Graph+Vector, Temporal MVCC with all sub-ms HNSW search, graph traversal, and writes. Using Neo4j Bolt/Cypher and qdrant's gRPC means you can switch with no changes while adding intelligent features like schemas, managed embeddings, reranking+llm, GPU accel, Auto-TLP, Policy-based Memory Decay, and MCP server.
— · MIT
Give your AI agents persistent, collective memory — with deduplicating absorb, supersession lineage, semantic search, and a graph UI. Speaks MCP.
— · no license
CoexistAI is a modular, developer-friendly research assistant framework . It enables you to build, search, summarize, and automate research workflows using LLMs, web search, Reddit, YouTube, and mapping tools—all with simple MCP tool calls or API calls or Python functions.
v0.3.0 · MIT
Open source implementation and extension of Google Research’s PaperBanana for automated academic figures, diagrams, and research visuals, expanded to new domains like slide generation.
v0.0.749 · MIT
The ultimate RAG for your monorepo. Query, understand, and edit multi-language codebases with the power of AI and knowledge graphs
v13.1.0 · MIT
Neo.mjs is a self-evolving software organism: a professional end-to-end AI engineering team whose cross-model swarm inhabits live apps via Neural Link, Active Hybrid GraphRAG, DreamService, and self-healing loops.