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Hivechat
HiveChat is an AI chat application designed for small and medium - sized teams. It supports multiple large - model service providers and provides functions such as group management, Token limits, and third - party login.
rating : 5 points
downloads : 932
What is the MCP server?
The MCP server is a lightweight, high - performance middleware used to manage and optimize the context interaction of large AI models. It allows users to call multiple AI models through a unified interface, simplifies complex configurations, and improves work efficiency.How to use the MCP server?
You can complete the configuration and startup in just a few steps: install dependencies, initialize the database, configure models and service providers, and finally run the service. You can quickly get started without in - depth knowledge of underlying technologies.Applicable scenarios
It is suitable for small and medium - sized teams, educational institutions, or individual developers, especially for scenarios that require cross - model collaboration and unified management of AI resources.Main features
Multi - model supportIt is compatible with mainstream AI models such as OpenAI, Claude, Gemini, etc., and supports custom addition of other models.
Context managementIntelligently save and restore conversation context to ensure continuity and consistency.
Permission controlAllocate model access permissions and Token limits by user groups to flexibly meet team needs.
MCP protocolBased on the standardized MCP protocol, it enables efficient data transmission and model interaction.
Advantages and limitations
Advantages
Easy to integrate and expand
Unified management of multiple AI models
Support for custom configuration
Efficient context processing capabilities
Limitations
Optimization may be required for high - concurrency requests
Some advanced features require additional payment
A certain learning cost is required for initial configuration
How to use
Install dependencies
Clone the project code and install the required dependency libraries.
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Initialize the database
Run the initialization script to create the database table structure.
Configure models and service providers
Edit the.env file and fill in the API key and relevant configurations.
Start the service
Run in development mode or build for the production environment.
Usage examples
Query weather forecastsCall an AI model through MCP to obtain the weather conditions of a specified city.
Chain - of - thought reasoningLeverage DeepSeek's chain - of - thought ability to solve complex problems.
Frequently Asked Questions
Which models does the MCP server support?
How to add a new AI model?
Does it support multi - language input?
Related resources
GitHub repository
Project source code and documentation
Online demonstration
Experience the actual effect of the MCP server
User manual
Detailed operation guide
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