MyAIServ MCP Server is a high-performance FastAPI server implementing the Model Context Protocol (MCP) for seamless integration with Large Language Models (LLMs). It uses modern technologies like FastAPI, Elasticsearch, Redis, Prometheus, and Grafana to provide REST, GraphQL, and WebSocket APIs with full MCP support.
The server is designed for developers and organizations looking to integrate LLMs efficiently. It offers features like vector search using Elasticsearch, real-time monitoring via Prometheus and Grafana, Docker-ready deployment, and comprehensive test coverage, making it suitable for scalable AI applications.
Developers, data scientists, and organizations working on AI-powered applications, especially those involving large language models, can benefit from this server. Its robust stack and support for MCP make it ideal for advanced AI integrations.
It can be deployed locally or in cloud environments using Docker and Docker Compose. The server supports various configurations, making it adaptable for development, testing, and production environments.
The initial commit for the MCP Server implementation was made on February 3, 2025, as indicated by the documentation updates.
MCP is a protocol implemented by MyAIServ MCP Server that supports tools, resources, prompts, and sampling, enabling seamless integration with large language models (LLMs).
To get started, clone the repository, set up a virtual environment, install dependencies, configure the .env file, and run the server using Uvicorn. Access the API docs at http://localhost:8000/docs.
The server uses FastAPI for backend, Elasticsearch and Redis for storage, Prometheus and Grafana for monitoring, Pytest for testing, and Docker/Docker Compose for deployment.
Yes, it is open-source and licensed under the MIT License, allowing free use and modification.
Yes, it provides real-time monitoring capabilities using Prometheus and Grafana, which help track performance and system health.
MCP (Model Context Protocol) is an open protocol designed to standardize how applications provide context information to large language models (LLMs). Like a 'USB-C port' for AI applications, MCP ensures AI models can seamlessly connect with various data sources and tools.
An MCP Server is a server that supports the MCP protocol, enabling the exchange of contextual information between applications and AI models in a standardized way. It provides developers with an easy way to integrate AI models with databases, APIs, or other data sources.
An MCP Server eliminates the complexity of developing custom adapters by unifying the connection between AI models and various data sources. Whether you're a developer, data scientist, or AI app builder, an MCP Server simplifies the integration process, saving time and resources.
An MCP Server acts as an intermediary bridge, converting contextual information from various data sources into a format that AI models can understand. By adhering to the MCP protocol, it ensures data is transmitted between applications and AI models in a standardized manner.
At mcpserver.shop, you can browse our MCP Server Directory. The directory is categorized by industry (e.g., finance, healthcare, education), and each server comes with detailed descriptions and tags to help you quickly find the option that suits your needs.
The MCP Server Directory on mcpserver.shop is free to browse. However, some servers are hosted by third-party providers and may involve usage fees. Check the detailed page of each server for specific information.
MCP Servers support a wide range of data sources, including databases, APIs, cloud services, and custom tools. The flexibility of the MCP protocol allows it to connect almost any type of data source to AI models.
MCP Servers are primarily designed for developers, data scientists, and AI app builders. However, mcpserver.shop provides detailed documentation and guides to help users of varying technical levels get started easily.
Yes, MCP is an open-source protocol that encourages community participation and collaboration. For more details or to contribute, visit the official MCP documentation.
On mcpserver.shop, each MCP Server’s detailed page includes the provider’s contact information or a link. You can directly reach out to the provider for more details or technical support.