%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)
Fastapi MCP Servers
An MCP server project based on FastAPI, including Qdrant vector database integration and OpenWebUI client support
rating : 2.5 points
downloads : 8.4K
What is FastAPI MCP Servers?
This is an MCP server implementation built using the FastAPI framework, specifically designed to manage and process the model context protocol. It supports integration with the Qdrant vector database and can provide a user - friendly interface through Open WebUI.How to use FastAPI MCP Servers?
You can quickly start the service using simple Docker commands and the uvicorn server, and then interact with it using the MCP client or Open WebUI.Applicable scenarios
Suitable for scenarios that require managing large - language model contexts, building AI application backends, or implementing complex dialogue systems.Main features
FastAPI backend
Built using the high - performance FastAPI framework, providing fast response and good scalability.
Qdrant integration
Supports integration with the Qdrant vector database, facilitating the storage and retrieval of vector data.
Open WebUI support
Can be seamlessly integrated with Open WebUI, providing a user - friendly interface.
SSE support
Supports the Server - Sent Events (SSE) protocol, enabling real - time data push.
Advantages
Built on FastAPI, with excellent performance
Supports Docker containerized deployment, facilitating expansion
Provides multiple client connection methods
Integrated with Open WebUI, with a good user experience
Limitations
Requires certain technical knowledge for initial configuration
Depends on external services such as Qdrant
Documentation may not be comprehensive enough
How to use
Clone the code repository
First, you need to clone the project code to your local machine
%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%230080ff;%20}%20%3c/style%3e%3c/defs%3e%3cpath%20class='st0'%20d='M16.2,11.1c.4.5.4,1.2,0,1.8l-4.7,7.1h-3.8l5.3-8L7.6,4h3.8l4.7,7.1Z'/%3e%3c/svg%3e)
Install dependencies
Use the uv tool to install the dependencies required for the project
Start the Qdrant service
Use Docker to start the Qdrant vector database
Run the FastAPI application
Use uvicorn to run the FastAPI application
Start Open WebUI
Optional step, start the Open WebUI interface
Usage examples
Set up a local development environment
Set up a complete development environment on a local computer, including the FastAPI backend, Qdrant database, and Open WebUI interface.
Deploy in a production environment
Use Docker to deploy a complete MCP service stack in a production environment.
Frequently Asked Questions
How to modify the service port?
What if Open WebUI cannot connect to the MCP server?
How to add support for a new model?
Related resources
FastAPI official documentation
Official documentation for the FastAPI framework
Qdrant official documentation
Official documentation for the Qdrant vector database
Open WebUI documentation
Official documentation for Open WebUI
GitHub repository
Project source code repository
Gitlab MCP Server
Certified
The GitLab MCP server is a project based on the Model Context Protocol that provides a comprehensive toolset for interacting with GitLab accounts, including code review, merge request management, CI/CD configuration, and other functions.
TypeScript
16.6K
4.3 points
Notion Api MCP
Certified
A Python-based MCP Server that provides advanced to-do list management and content organization functions through the Notion API, enabling seamless integration between AI models and Notion.
Python
14.8K
4.5 points
Markdownify MCP
Markdownify is a multi-functional file conversion service that supports converting multiple formats such as PDFs, images, audio, and web page content into Markdown format.
TypeScript
24.5K
5 points
Duckduckgo MCP Server
Certified
The DuckDuckGo Search MCP Server provides web search and content scraping services for LLMs such as Claude.
Python
44.7K
4.3 points
Unity
Certified
UnityMCP is a Unity editor plugin that implements the Model Context Protocol (MCP), providing seamless integration between Unity and AI assistants, including real - time state monitoring, remote command execution, and log functions.
C#
20.2K
5 points
Figma Context MCP
Framelink Figma MCP Server is a server that provides access to Figma design data for AI programming tools (such as Cursor). By simplifying the Figma API response, it helps AI more accurately achieve one - click conversion from design to code.
TypeScript
44.3K
4.5 points
Minimax MCP Server
The MiniMax Model Context Protocol (MCP) is an official server that supports interaction with powerful text-to-speech, video/image generation APIs, and is suitable for various client tools such as Claude Desktop and Cursor.
Python
30.2K
4.8 points
Context7
Context7 MCP is a service that provides real-time, version-specific documentation and code examples for AI programming assistants. It is directly integrated into prompts through the Model Context Protocol to solve the problem of LLMs using outdated information.
TypeScript
62.4K
4.7 points