LLM & AI Agent Skills

Make your meetings accessible to AI Agents 🤖 joinly.ai is a connector middleware designed to enable AI agents to join and actively participate in video calls. Through its MCP server, joinly.ai provides essential meeting tools and resources that can equip any AI agent with the skills to perform tasks and interact with you in real time during your meetings. - Live Interaction: Lets your agents exec

-brightgreen) A Model Context Protocol (MCP) server for analyzing technical debt across multiple programming languages. Designed to integrate with GitHub Copilot and other MCP-compatible tools. - Multi-language support: JavaScript, TypeScript, Python, Java, Swift, Kotlin, Objective-C, C++, C, C, Go, Rust, Ruby, PHP - Comprehensive analysis: Detects various types of tech debt including code quality

Chroma - the open-source embedding database . The fastest way to build Python or JavaScript LLM apps with memory! The Model Context Protocol (MCP) is an open protocol designed for effortless integration between LLM applications and external data sources or tools, offering a standardized framework to seamlessly provide LLMs with the context they require. This server provides data retrieval capabili

A Model Context Protocol (MCP) server for interacting with Meta Ads. Analyze, manage and optimize Meta advertising campaigns through an AI interface. Use an LLM to retrieve performance data, visualize ad creatives, and provide strategic insights for your ads on Facebook, Instagram, and other Meta platforms. mcp-name: co.pipeboard/meta-ads-mcp - Discord. Join the community. - Email Support. Email u

Holistic understanding of requirements & stack

Project Synopsis

Holistic understanding of requirements & stack

A Model Context Protocol (MCP) server that integrates with Tilt to provide programmatic access to Tilt resources and logs through LLM applications. Imagine prompting like this: The key insight is you no longer need to tell your LLM how to build and deploy your code. Instead, you can simply ask it to what to build and deploy. Tilt is a powerful tool for working with Docker/Kubernetes workloads. Wit

A Model Context Protocol (MCP) server that enables AI assistants to get human input when needed. This tool creates tasks on Amazon Mechanical Turk that let real humans answer questions from AI systems. While primarily a proof-of-concept, it demonstrates how to build human-in-the-loop AI systems using the MCP standard. See limitations for current constraints. - Node.js 16+ - AWS credentials with MT

The CockroachDB MCP Server is a natural language interface designed for LLMs and agentic applications to manage, monitor, and query data in CockroachDB. It integrates seamlessly with MCP (Model Content Protocol) clients, such as Claude Desktop or Cursor, enabling AI-driven workflows to interact directly with your database. - Cluster Monitoring - Database Operations - Table Management - Query Engin

Model Context Protocol server for YDB. It allows to work with YDB databases from any LLM that supports MCP. This integration enables AI-powered database operations and natural language interactions with your YDB instances. uvx, which is an allias for uv run tool, allows you to run various python applications without explicitly installing them. Below are examples of how to configure YDB MCP using u

The Redis MCP Server is a natural language interface designed for agentic applications to efficiently manage and search data in Redis. It integrates seamlessly with MCP (Model Content Protocol) clients, enabling AI-driven workflows to interact with structured and unstructured data in Redis. Using this MCP Server, you can ask questions like: - "Store the entire conversation in a stream" - "Cache th

           CentralMind Gateway: Create API or MCP Server in Minutes 🚀 Interactive Demo avialable here: https://centralmind.ai Simple way to expose your database to AI-Agent via MCP or OpenAPI 3.1 protocols. This will run for you an API: Which you can use inside your AI Agent: Gateway will generate AI optimized API. AI agents and LLM-powered applications need fas

An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context. - Vector-based documentation search and retrieval - Support for multiple documentation sources - Semantic search capabilities - Automated documentation processing - Real-time context augmentation f

Search and retrieve biomedical data — genes, variants, clinical trials, articles, drugs, diseases, pathways, proteins, adverse events, pharmacogenomics, and phenotype-disease matching. 15 sources including PubMed, ClinicalTrials.gov, ClinVar, OncoKB, Reactome, UniProt, PharmGKB, OpenFDA, Monarch. Use when asked about gene function, variant pathogenicity, trial matching, drug safety, resistance mechanisms, hereditary syndromes, or literature evidence.

A Model Context Protocol (MCP) server that provides access to the MongoDB Atlas API. This server wraps the mongodb-atlas-api-client package to expose MongoDB Atlas functionality through MCP tools. This MCP server exposes most of the mongodb-atlas-api-client surface as MCP tools. - userget — Get a database user by username - usergetall — List all database users - usercreate — Create a database user

An MCP server that gives AI assistants safe, read-only access to SQL Server databases. Every query is parsed into a full AST using Microsoft's official T-SQL parser — not regex — so comment injection, string literal tricks, and encoding bypasses are blocked at the syntax level. Prerequisite: .NET 10.0 runtime 2. Configure — create ~/.config/sqlaugur/appsettings.json (Linux/macOS) or %APPDATA%\sqla

CRUD operations for knowledge base docs — add, delete, update metadata, embed. Requires GNOSIS_MCP_WRITABLE=true.

A Model Context Protocol (MCP) server that provides agentic tools for interacting with the Trieve API. This server enables AI agents to search and interact with Trieve datasets through a standardized interface. - Search across Trieve datasets using semantic search - List and access dataset information - Support for both environment variables and command-line arguments - Built with TypeScript for t

- 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

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

A tool similar to cloc, sloccount and tokei. For counting the lines of code, blank lines, comment lines, and physical lines of source code in many programming languages. Goal is to be the fastest code counter possible, but also perform COCOMO calculation like sloccount, LOCOMO estimation for LLM-based development costs, estimate code complexity similar to cyclomatic complexity calculators and prod

Screenpipe brand style guide. Reference this when designing UI components, writing copy, or making visual decisions.

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.