Multi-agent swarm planner and project sprint allocator.
Premium SEO and Answer Engine Optimization (AEO) oracle for AI agents.
LLM token optimization gateway implementing semantic pruning.
LLM query cost optimizer minimizing context waste.
DeFi protocol simulation model scoring tokenomic structures.
Hallucination validator and factual grounding checker for LLM outputs.
让 AI 与 Chrome 浏览器实时交互：获取历史请求、控制台日志、RUM/埋点事件，并动态设置 Mock 数据。.
Drafts Google & TripAdvisor review replies; agents can never post themselves. Free rate check.
Connect AI Assistant to Fibery — operating system for orgs run by nerds.
Zero-Ops deploy of a private AI workspace to your own VPS — from your AI chat. Free and open-source.
Build, solve, and analyze RASON optimization, simulation, data science, and decision models.
Know whether an AI agent should REUSE or REFETCH a URL before retrieving it again.
Track, analyze, and act on your streaming and SaaS subscriptions from any AI agent.
Send SMS, MMS and WhatsApp, manage contacts and webhooks, from AI agents.
Human-in-the-loop for AI coding agents — ask questions, get approvals via Slack.
U.S. college ROI data: 30-yr NPV for 500 colleges, lifetime ROI for 134 majors. Keyless.
Coordinate multiple AI agents over MCP: atomic claims, leases, shared ledger, handoffs, tasks.
Your mail, chats and documents indexed into a local SQLite corpus, served to AI clients over MCP.
Architecte IA agentique (Paris) : profil, compétences, articles, réservation — pour agents IA.
Search peer-reviewed papers and research methodology guidance from your AI agent.
Lusha MCP server for authorized business profile, usage, and buying-signal insights.
A personal RAG database you build from chat, so AI creates work that sounds like you.
Read any public web page as a clean AIDocument (Markdown + structure) through Lyrenth.
AI-powered test automation: run tests, analyze failures, query results across mabl workspaces.
About AI & Agents MCP servers
This category collects servers that make agents better at being agents. You will find orchestration layers that run multi-step pipelines, routers that hand a task to the right model or sub-agent, evaluation and tracing tools, prompt and skill libraries, and bridges that let one assistant call another. Some wrap commercial model APIs; others are thin coordination layers around open-source frameworks.
Because these servers often sit between your agent and other systems, look closely at where data flows and what is logged. Prefer servers that document their tool contracts clearly, support streaming where relevant, and can run locally when the workload is sensitive. Many are new and move quickly, so check the repository's recent activity before relying on one in production.