Ai Agents AI Agent Skills

The Web, Readable. Your AI agent spends 60,000 tokens just to look at a web page. Charlotte does it in 336. Charlotte is an MCP server that gives AI agents structured, token-efficient access to the web. Instead of dumping the full accessibility tree on every call, Charlotte returns only what the agent needs: a compact page summary on arrival, targeted queries for specific elements, and full detail

RAGMap is a lightweight MCP Registry-compatible subregistry + MCP server focused on RAG-related MCP servers. - Ingests the official MCP Registry, enriches records for RAG use-cases, and serves a subregistry API. - Exposes an MCP server (remote Streamable HTTP + local stdio) so agents can search/filter RAG MCP servers. MapRag is a discovery + routing layer for retrieval. It helps agents and humans

Access comprehensive NBA statistics via Model Context Protocol A Model Context Protocol (MCP) server that provides access to live and historical NBA data including player stats, game scores, team information, and advanced analytics. 1. Install the server: 2. Add to your Claude Desktop config file: MacOS: ~/Library/Application Support/Claude/claudedesktopconfig.json Windows: %APPDATA%/Claude/claude

OMOPHub MCP Server Medical vocabularies for AI agents. Search, map, and navigate 10M+ OMOP concepts: SNOMED CT, ICD-10, RxNorm, LOINC, and more. Directly from Claude, Cursor, VS Code, or any MCP-compatible client. Quick Start · Examples · Working with medical vocabularies today means downloading multi-gigabyte CSV files, loading them into a local database, and writing SQL to find what you need. Ev

Publish the package to npmjs by bumping version, building, and creating a GitHub release

mcp.apify.com The Apify Model Context Protocol (MCP) server at mcp.apify.com enables your AI agents to extract data from social media, search engines, maps, e-commerce sites, and any other website using thousands of ready-made scrapers, crawlers, and automation tools from Apify Store. It supports OAuth, allowing you to connect from clients like Claude.ai or Visual Studio Code using just the URL.

- MCP Server: code-to-tree - Using code-to-tree - Configure MCP Clients - Building (Windows) - Building (macOS) The code-to-tree server's goals are: 1. Give LLMs the capability of accurately converting source code into AST(Abstract Syntax Tree), regardless of language. 2. One standalone binary should be everything the MCP client needs. These goals imply: 1. The underlying syntax parser should be v

Please migrate to our new KnowledgeBase MCP Server, which provides enhanced capabilities and improved accuracy. Model Context Protocol (MCP) is a standardized protocol designed to manage context between large language models (LLMs) and external systems. The Chargebee MCP Server offers a robust set of tools to improve developer efficiency. It integrates with AI-powered code editors like Cursor, Win

DevDocs-MCP is a Model Context Protocol (MCP) server that provides version-pinned, deterministic documentation sourced from DevDocs.io to AI assistants (Claude, RooCode, Cline, Copilot etc.). It acts as a local Documentation Intelligence Layer, ensuring your agent always has the correct API context without network latency or training data drift. This server follows the proposed MCP server standard

The firewall for AI agents. Open-source policy enforcement for MCP. Website: policylayer.com Intercept is a deterministic enforcement proxy for the Model Context Protocol (MCP). It sits between an AI agent and an MCP server, evaluating every tools/call request against YAML-defined policies. Violating calls are blocked at the transport layer before reaching the upstream server. MCP gives AI agents

Hardware-accelerated persistent memory for AI agents. Local-first. No cloud. One-time payment. 66.9% on LoCoMo benchmark (adjusted). Under 1ms retrieval. Zero cloud dependency. Retrieval pipeline rebuilt from scratch. - bge-small-en-v1.5 bi-encoder + ms-marco cross-encoder reranker (spec-decode architecture) - BM25 + Porter-stemmed BM25 + named entity injection, fused via RRF - MAGMA graph layer —

A multi-model AI orchestration MCP server for automated code review and LLM-powered analysis. Multi-MCP integrates with Claude Code CLI to orchestrate multiple AI models (OpenAI GPT, Anthropic Claude, Google Gemini) for code quality checks, security analysis (OWASP Top 10), and multi-agent consensus. Built on the Model Context Protocol (MCP), this tool enables Python developers and DevOps teams to

Debug AI traces, find exceptions, analyze sessions, and manage prompts via Langfuse MCP. Also handles MCP setup and configuration.

MCPServers.org | ModelScope | （更多MCP平台陆续上架中……） CRIC物业AI 是 克而瑞 专为物业行业打造的智能 AI 助理，于2025年4月25日 正式发布。 CRIC物业AI 通过行业知识库建设，结合多模态大模型 + RAG 技术，集成五大核心能力模块：行业研究、法律法规、社区治理、项目经营、文案写作，并在行业垂类知识基础上，拓展了 资讯舆情 和 人才培训 两大智能体。 克而瑞通过三个能力来构建其自身在物业AI合作领域优势： - 数据资产转化能力： 将10亿字行业语料、TB级多模态数据转化为物业行业的高质量数据集，并构建了一套行业数据质量评估体系，保障准确率和可信度； - 场景穿透能力： 聚焦20+物业行业垂直业务场景，定向选用对应领域知识库，精准匹配； - 生态进化能力： 通过每日实时监测超过500+可信资讯和数据来源，处理10万+实时数据的自更新系

A powerful Go-based MCP server that enables seamless integration between AI assistants (like Claude Desktop, Cursor) and Kanboard project management system. Manage your Kanboard projects, tasks, users, and workflows directly through natural language commands. ⚠️ Warning: To avoid issues like these: We recommend using mcpproxy as a proxy solution. - ✨ Features - 🚀 Quick Start - ⚙️ Configuration -

Wren Engine - Google Cloud Storage - Local Files - MS SQL Server - MySQL Server - Oracle Server - PostgreSQL Server - Amazon S3 - Snowflake - Databricks - Apache Spark At the enterprise level, the stakes - and the complexity - are much higher. Businesses run on structured data stored in cloud warehouses, relational databases, and secure filesystems. From BI dashboards to CRM updates and compliance

You can run the MCP Server in a Docker container. This is useful if you want to avoid managing Python environments or dependencies on your local machine. See kestramcpdocker. Paste the following configuration into your MCP settings (e.g., Cursor, Claude, or VS Code): - Replace , , and with your actual credentials. - For OSS installations, you can use KESTRAUSERNAME and KESTRAPASSWORD instead of KE

An MCP Server to utilize Codelogic's rich software dependency data in your AI programming assistant. The server implements five tools: - codelogic-method-impact: Pulls an impact assessment from the CodeLogic server's APIs for your code. - Takes the given "method" that you're working on and its associated "class". - codelogic-database-impact: Analyzes impacts between code and database entities. - T

🚀 Try ApeRAG Live Demo - Experience the full platform capabilities with our hosted demo ApeRAG is a production-ready RAG (Retrieval-Augmented Generation) platform that combines Graph RAG, vector search, and full-text search with advanced AI agents. Build sophisticated AI applications with hybrid retrieval, multimodal document processing, intelligent agents, and enterprise-grade management feature

Store team knowledge, project conventions, and learnings from tasks. Use to remember what works and recall context before new tasks. Connects to Hindsight Cloud. (user)

Open-source memory backend for multi-agent systems. Agents store decisions, share causal knowledge graphs, and retrieve context in 5ms — without cloud lock-in or API costs. Works with LangGraph · CrewAI · AutoGen · any HTTP client · Claude Desktop Key capabilities for agent pipelines: - Framework-agnostic REST API — 15 endpoints, no MCP client library needed - Knowledge graph — agents share causal

Shodh-Memory Persistent memory for AI agents. Single binary. Local-first. Runs offline. We built this because AI agents forget everything between sessions. They make the same mistakes, ask the same questions, lose context constantly. Shodh-Memory fixes that. It's a cognitive memory system—Hebbian learning, activation decay, semantic consolidation—packed into a single ~17MB binary that runs offline

:deciduoustree: MCPJungle :deciduoustree: Self-hosted MCP Gateway for your private AI agents MCPJungle is an open source, self-hosted Gateway for all your Model Context Protocol Servers. 🧑‍💻 Developers use it to register & manage MCP servers and the tools they provide from a central place. 🤖 MCP Clients use it to discover and consume all these tools from a single "Gateway" MCP Server. MCPJungle

Your MCPs, supercharged. Find any tool instantly, execute with code mode, run on schedule, discover skills, load Photons, ready for any client. Smart loading saves tokens and energy. Instead of your AI juggling 50+ tools scattered across different MCPs, NCP gives it a single, unified interface with code mode execution, scheduling, skills discovery, and custom Photons. Your AI sees just 2-3 simple