M

Mymcpserv

A modern MCP server implementation that supports multi-AI providers, real-time response, conversation management, and monitoring functions, using a microservices architecture.
2 points
7

What is an MCP server?

The MCP server is an intelligent conversation management platform that can connect to multiple AI service providers (such as OpenAI, Google AI, etc.) and provides a unified API interface to manage the conversation process, store historical records, and support advanced functions such as function calls and semantic search.

How to use the MCP server?

You can quickly start the service through simple Docker commands and then use the provided REST API to interact with various AI services. The system will automatically manage the conversation history and context.

Use cases

Suitable for applications that need to use multiple AI services simultaneously, chatbots that require long-term conversation memory, and enterprise applications that need to monitor and analyze AI usage.

Main features

Multi-AI provider supportCan connect to mainstream AI services such as OpenAI, Anthropic, Google AI, and Azure simultaneously
Real-time streaming responseSupports a streaming conversation experience where results are returned as they are generated
Conversation managementAutomatically saves and restores conversation context and historical records
Function callingSupports AI calling predefined tools and functions
Advanced monitoringBuilt-in Prometheus metrics and Grafana dashboards

Advantages and limitations

Advantages
One-stop integration of multiple AI services without the need to connect to each provider separately
Complete conversation history management, facilitating the construction of AI applications with memory
Detailed usage monitoring and analysis functions
High-performance caching and search functions to improve response speed
Limitations
Requires certain technical knowledge for initial setup
Relies on Docker and multiple database services, consuming a large amount of resources
Advanced functions such as vector search require additional configuration

How to use

Prepare the environment
Ensure that Docker and Docker Compose are installed
Get the code
Clone the project repository to the local machine
Configure environment variables
Copy the example environment file and modify it as needed
Start the service
Use Docker Compose to start all services
Access the API
After the service starts, the API documentation will be automatically generated

Usage examples

Customer service robotBuild an intelligent customer service that can remember the customer's historical questions
Multi-AI comparison toolSend the same question to different AI providers simultaneously to compare the results

Frequently asked questions

Is the MCP server free?
How to add a new AI provider?
Where is the conversation history stored?

Related resources

Official documentation
Complete API reference and configuration guide
GitHub repository
Source code and issue tracking
Demo video
A 10-minute quick-start demonstration
Installation
Copy the following command to your Client for configuration
Note: Your key is sensitive information, do not share it with anyone.
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