Compression MCP server implementation using Model Context Protocol.
HDF5 FastMCP - Scientific Data Access for AI Agents | CLIO Kit MCP Server.
Lmod MCP - Environment Module Management for LLMs with comprehensive module operations.
Node Hardware MCP - Comprehensive Hardware Monitoring and System Analysis for LLMs with...
Pandas MCP - Advanced Data Analysis for LLMs with comprehensive pandas operations.
Parallel Sort MCP - High-Performance Log File Processing for LLMs with advanced sorting and analysis.
Operator-owned scientific dataset discovery for remote agents.
Structured Spack discovery and installation tools for scientific agents.
跨客户端 ECharts 图表 MCP server：数据 -> 可交互 HTML 与内联图片.
The agent eval standard for MCP. Score every agent output for quality, safety, and cost.
Write secrets into .env without the agent ever seeing the value - Windows masked dialog (繁中 UI).
Self-improving tool discovery for AI agents. Find, install, and use new MCP servers automatically.
Official MCP server for Scorezilla — add a leaderboard to your game from your AI coding assistant.
Give brains to your small models. MCP server enforcing step-by-step Chain-of-Thought.
Dynamic context loading for AI assistants with TDD workflow and AST analysis.
Connect AI assistants to Itcons.app work orders, reports, projects, users, and resources.
Connect AI assistants to ITM Platform projects, tasks, budgets, risks, and team workload.
Access your Convo meetings, transcripts, summaries, and action items from any AI assistant.
Hiring-intent signals for B2B sales/GTM: turn live job postings into buying signals.
GPT Image 2 MCP server backed by Polza media and storage APIs.
Three-model MCP server returning Grok, Gemini, and DeepSeek outputs for Claude.
Nano Banana MCP server backed by Polza media and storage APIs.
MCP server for managing Allure TestOps tests, launches, plans, defects, and metadata.
Self-hosted memory for AI agents with hybrid search, knowledge graphs, tasks, and document sync.
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.