LLM & AI Agent Skills

Product Hunt MCP Server connects Product Hunt's API to any LLM or agent that speaks the Model Context Protocol (MCP). Perfect for AI assistants, chatbots, or your own automations! - 🔍 Get posts, collections, topics, users - 🗳️ Get votes, comments, and more - 🛠️ Use with Claude Desktop, Cursor, or any MCP client - Get detailed info on posts, comments, collections, topics, users - Search/filter b

A Mattermost integration that connects to Model Context Protocol (MCP) servers, leveraging a LangGraph-based AI agent to provide an intelligent interface for interacting with users and executing tools directly within Mattermost. - 🤖 Langgraph Agent Integration: Uses a LangGraph agent to understand user requests and orchestrate responses. - 🔌 MCP Server Integration: Connects to multiple MCP serve

Transform how you work with Bitbucket by connecting Claude, Cursor AI, and other AI assistants directly to your repositories, pull requests, and code. Get instant insights, automate code reviews, and streamline your development workflow. - Ask AI about your code: "What's the latest commit in my main repository?" - Get PR insights: "Show me all open pull requests that need review" - Search your cod

Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory Use when: conversation memory, remember, memory persistence, long-term memory, chat history.

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 —

This repository is an example of how to create a MCP server for Qdrant, a vector search engine. An official Model Context Protocol server for keeping and retrieving memories in the Qdrant vector search engine. It acts as a semantic memory layer on top of the Qdrant database. 1. qdrant-store - Store some information in the Qdrant database - information (string): Information to store - metadata (JSO

Model Context Protocol (MCP) Server for Langfuse Prompt Management. This server allows you to access and manage your Langfuse prompts through the Model Context Protocol. Quick demo of Langfuse Prompts MCP in Claude Desktop (unmute for voice-over explanations): This server implements the MCP Prompts specification for prompt discovery and retrieval. - prompts/list: List all available prompts - Optio

Opik MCP Server Model Context Protocol (MCP) server for Opik , with both local stdio and remote streamable-http transports. Website • Slack community • Twitter • Documentation Opik MCP Server gives MCP-compatible clients one interface for: - Prompt lifecycle management - Workspace, project, and trace exploration - Metrics and dataset operations - MCP resources and resource templates for metadata-a

通过 uvx 安装并运行： 安装完成后，直接运行： 运行后你可以直接使用工具，例如： 或（如果是 uvx 安装）： 好的，这是转换后的 Markdown 版本： 好的，这是转换后的 Markdown 版本： 1. 配置环境变量（可选） 你可以通过 .env 文件或系统环境变量配置 DolphinDB 的连接信息： 也可以不设置，系统将使用默认值。 该命令会启动 MCP 插件服务，供外部调用。 3. FastMCP Agent 使用示例 启动后，你的工具将通过 FastMCP 对外暴露以下函数接口： listtbs(dbName: str) querytablediskusage(database: str, tableName: str) querydolphindb(script: str) 可通过 MCP 前端界面或对接 LLM 工具链来进行访问。

An MCP (Model Context Protocol) server that brings live prediction market data into AI coding environments like Cursor and Claude Desktop. - 🆓 No API Keys – Works out of the box, zero configuration - 📈 Multi-Platform – Polymarket, PredictIt, and Kalshi in one interface - ⚡ Real-time Data – Current odds and prices from live markets - 🎯 Easy Setup – One-click install in Cursor or simple manual se

An AI coding assistant built in Rust that provides both command-line and graphical interfaces for autonomous code analysis and modification. Multi-Modal Tool Execution: Adapts to different LLM capabilities with pluggable tool invocation modes - native function calling, XML-style tags, and triple-caret blocks - ensuring compatibility across various AI providers. Real-Time Streaming Interface: Advan

Nile MCP Server Learn more ↗️ A Model Context Protocol (MCP) server implementation for Nile database platform. This server allows LLM applications to interact with Nile platform through a standardized interface. - Database Management: Create, list, get details, and delete databases - Credential Management: Create and list database credentials - Region Management: List available regions for databas

Every multi-step tool workflow burns an LLM round-trip per step. The agent calls tool A, waits, sends the full context back to the model, gets a decision to call tool B, calls it, sends everything back again. Each round-trip re-transmits 2K–10K tokens on what is essentially plumbing. For a session with 20 two-step workflows, that's 20 wasted model calls, ~100K wasted tokens, and 20–40 seconds of a

- 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

MongoDB Lens is a local Model Context Protocol (MCP) server with full featured access to MongoDB databases using natural language via LLMs to perform queries, run aggregations, optimize performance, and more. - Quick Start - Installation - Configuration - Client Setup - Data Protection - Test Suite - Disclaimer - Install MongoDB Lens - Configure MongoDB Lens - Set up your MCP Client (e.g. Claude D

An MCP server that delivers crypto ETF flow data to power AI agents' decision-making. - Unified Tool: The getetfflow tool dynamically fetches historical ETF flow data for BTC or ETH. - Markdown Table Output: Leverages pivot tables to present data with ETF tickers as columns, dates as rows, and a total column for summed flows. - Prompt Guidance: Includes a prompt (etfflowprompt) to streamline LLM i

OpenClaw plugin that intercepts media-only inbound messages before they reach the LLM. If the channel provides readable local attachment paths, the plugin stages them through obsidian-cli-plugins so a later text message can create one Obsidian file-mode record with all staged media. This is an OpenClaw runtime plugin, not a general-purpose npm library. It depends on OpenClaw typed hooks (inboundcl

- About the pgEdge Postgres MCP Server - pgEdge Postgres MCP Server - Choosing the Right Solution - Best Practices - Querying the Server - Installing the MCP Server - Quick Start - Quickstart Demo with Northwind - Deploying on Docker - Deploying from Source - Testing the MCP Server Deployment - Configuring the MCP Server - Specifying Configuration Preferences - Using Environment Variables to Speci

MCP (Model Context Protocol) server for GPTZero AI detection API. Detect AI-generated text directly from Claude, ChatGPT, or any LLM that supports MCP. If you find this MCP server useful, please consider supporting its development! 👉 Click here to support this project Your support helps maintain and improve this tool for everyone. Thank you! 🙏 - 🤖 Detect AI-generated text with confidence scores

MCP Server for the Notion API, enabling LLM to interact with Notion workspaces. Additionally, it employs Markdown conversion to reduce context size when communicating with LLMs, optimizing token usage and making interactions more efficient. Here is a detailed explanation of the steps mentioned above in the following articles: - English Version: https://dev.to/suekou/operating-notion-via-claude-des

audit-prompt-caching is a portable Codex/agent skill for finding why LLM cache reuse fails across the request path: prompt/prefix caches, provider cache telemetry, cache-aware routing, agent tool stability, Bedrock checkpoints, OpenRouter routing drift, provider migration risk, and vLLM/SGLang KV reuse. LLM cache reuse usually fails silently. A timestamp in the system prompt, shuffled tool schemas

Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.

The Mermaid MCP Server enables AI assistants like GitHub Copilot, Claude, and custom LLM applications to generate professional architecture diagrams, flowcharts, sequence diagrams, and more using natural language. It provides a Model Context Protocol interface for seamless integration with AI coding assistants. - 🤖 AI-Powered Generation: Create diagrams using natural language with GitHub Copilot

An MCP (Model Context Protocol) server that provides comprehensive access to Ethereum Virtual Machine (EVM) JSON-RPC methods for AI coding environments like Cursor and Claude Desktop. - 🌐 Any EVM Network – Ethereum, Polygon, Arbitrum, Optimism, BSC, Avalanche, and more - 🔌 Any Node Provider – Infura, Alchemy, QuickNode, local nodes, or custom RPC - 📊 20+ RPC Methods – Complete access to blockch