Brev is a system that includes an MCP server implementation, designed to work with the Brev CLI and its API access token. It facilitates development workflows through tools like uv and integrates with Claude Desktop for configuration. Brev provides a structured way to manage configurations and dependencies for Python projects, supports quick debugging via the MCP Inspector, and enables seamless publishing to PyPI. Developers who need to manage Python projects with specific dependencies, leverage the Brev CLI for API access, or debug MCP servers can benefit from using Brev. It can be used locally on MacOS or Windows systems and is ideal for setting up local repositories, managing dependencies, preparing packages for distribution, and debugging MCP server implementations.
Penrose MCP Server is a Model Context Protocol (MCP) server designed for Penrose, a tool that enables the creation of beautiful mathematical diagrams through natural language. It supports three key components: Domain DSL for defining types and relationships, Substance for describing objects, and Style for specifying visual rules. This makes it easier for users to design complex diagrams without deep programming knowledge. Ideal for mathematicians, educators, researchers, and developers, Penrose MCP Server can be used in academic research, educational settings, and software development. The project includes reference implementations and official protocol documentation, and is governed by the MIT License.
mcp-clickup is an MCP Server designed to integrate with the ClickUp API, allowing users to authenticate and manage tasks within their ClickUp workspaces. It offers pre-built tools for task retrieval and management, making it ideal for applications like Claude Desktop. Developed and maintained by Mike Hoang and Henry Mao, mcp-clickup is available on GitHub under the repository mikah13/mcp-clickup. The latest version, v1.0.4, was released on February 18, 2025. Users can set up mcp-clickup by obtaining their ClickUp API token and Workspace ID, and it can also be run using Docker. This open-source tool is licensed under the MIT License, ensuring flexibility for users to modify and distribute it.
MyAIServ MCP Server is a high-performance FastAPI server that implements the Model Context Protocol (MCP) for seamless integration with Large Language Models (LLMs). It leverages modern technologies such as FastAPI, Elasticsearch, Redis, Prometheus, and Grafana to offer REST, GraphQL, and WebSocket APIs. Designed for developers and organizations, it supports efficient LLM integration with features like vector search, real-time monitoring, Docker-ready deployment, and comprehensive test coverage. Ideal for scalable AI applications, it can be deployed locally or in cloud environments. MyAIServ MCP Server is open-source, licensed under the MIT License, and provides real-time monitoring capabilities.
Sandbox MCP Server is a powerful tool that provides isolated Docker environments for secure and reproducible code execution. It allows users to create containers with any Docker image, write and execute code in multiple programming languages, install packages, and set up development environments. Ideal for developers, data scientists, and DevOps engineers, it supports creating persistent containers, saving container states as Docker images, and generating Dockerfiles. Sandbox MCP Server can be used locally or integrated into larger systems like Claude Desktop, ensuring security by executing code in isolated containers. It is particularly useful for testing, debugging, and deploying applications, and for teams requiring consistent development environments. To get started, users need Python 3.9+, Docker, and optionally the uv package manager.
The AWS Well-Architected Framework MCP Server is a project that provides an MCP (Model Context Protocol) server, offering guidance based on the six pillars of the AWS Well-Architected Framework: Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, and Sustainability. This server allows users to interact with these principles programmatically via specific endpoints. It helps developers and architects evaluate their architecture against AWS best practices by providing detailed recommendations and tools like architecture analysis and review templates. Designed for cloud architects, developers, DevOps engineers, and IT professionals, it can be deployed locally or in any environment supporting Python 3.10 or higher. The server runs as an HTTP service on port 3001, making it accessible via API calls. It is particularly useful during the planning, development, and review phases of AWS architectures.
Perplexity MCP Server is an advanced MCP server that integrates with the Claude Desktop App to provide web search capabilities using Perplexity's API. It leverages powerful AI models to deliver real-time, relevant information, enhancing the app's functionality for tasks like news, research, and general inquiries. Ideal for developers and users seeking efficient information retrieval within their application environment, it can be installed via Smithery or manually by cloning its repository. The server supports various models, including 'sonar-reasoning-pro' and 'sonar', and is governed by the MIT License, allowing for flexible usage and modification.
mcp-snowflake-server is a Model Context Protocol (MCP) server implementation designed for seamless integration with Snowflake databases. It allows users to execute SQL queries, manage tables, and append data insights, maintaining an aggregated memo of analysis results in real-time. This tool is particularly beneficial for data analysts, developers, and organizations using Snowflake for data storage and analytics. Developed with contributions from Isaac Wasserman, mcp-snowflake-server can be installed via Smithery or UVX for Claude Desktop, or locally by configuring the `claude_desktop_config.json` file. It offers six core tools including read_query, write_query, create_table, list_tables, describe_table, and append_insight, enabling comprehensive database interaction and management.
RAG Documentation MCP Server is an advanced implementation of an MCP server designed to enhance AI-driven interactions with structured knowledge. It leverages vector search to retrieve and process documentation, enabling AI assistants to provide more contextually relevant responses. Key features include vector-based document search, source management, and queue handling. Developed as a fork of qpd-v/mcp-ragdocs and enhanced by Rahul Retnan, it can be deployed locally or in containerized environments using Docker Compose. The web interface, accessible at `http://localhost:3030`, offers real-time monitoring, source management, and query testing, making it ideal for building documentation-aware AI assistants and implementing semantic documentation search.
StockScreen MCP Server is a Model Context Protocol (MCP) server that offers advanced stock screening capabilities through Yahoo Finance. It enables Large Language Models (LLMs) to filter stocks based on technical, fundamental, and options criteria. Key features include watchlist management, result storage, and seamless integration with Claude Desktop. Traders, investors, financial analysts, and developers can benefit from its robust functionalities and customizability. StockScreen MCP Server can be installed locally or integrated into an existing development environment, requiring Python 3.12+. It is ideal for detailed stock screenings, daily trading activities, and long-term investment research.
MCP Server for OpenSearch is a Model Context Protocol (MCP) server designed to store and retrieve memories in the OpenSearch engine. It serves as a semantic memory layer, seamlessly integrating Large Language Models (LLMs) with OpenSearch, enhancing applications like AI-powered IDEs, chat interfaces, and custom workflows. This tool benefits developers and organizations needing to integrate LLMs with OpenSearch for data storage, retrieval, and analytics. It can be used in various environments, including local setups, cloud-hosted instances, and enterprise systems. MCP Server for OpenSearch is ideal for projects requiring a semantic memory layer and using or planning to use OpenSearch. Installation is straightforward via Smithery or `uv`, and it integrates well with Claude Desktop.
The Anti-Bullshit MCP Server is a Model Context Protocol (MCP) server designed to combat misinformation by analyzing claims, validating sources, and detecting manipulation. Developed by Teglon Labs, it uses various epistemological frameworks such as empirical, responsible, harmonic, and pluralistic approaches. Key features include `analyze_claim`, `validate_sources`, and `check_manipulation` to assess the credibility, accuracy, and ethical implications of information. The server can be integrated into applications like Claude Desktop for MacOS or VSCode extensions by configuring specific JSON files. It is available under the MIT License, ensuring flexible usage. Setting up the server locally requires Node.js >= 18.0.0 and npm/yarn, with installation and build steps provided.