Unichat MCP Server
Unichat MCP Server in Python
Also available in TypeScript
<h4 align="center"> <a href="https://github.com/amidabuddha/unichat-mcp-server/blob/main/LICENSE.md"> <img src="https://img.shields.io/github/license/amidabuddha/unichat-mcp-server" alt="Released under the MIT license." /> </a> <a href="https://smithery.ai/server/unichat-mcp-server"> <img src="https://smithery.ai/badge/unichat-mcp-server" alt="Smithery Server Installations" /> </a> </h4>Send requests to OpenAI, MistralAI, Anthropic, xAI, Google AI or DeepSeek using MCP protocol via tool or predefined prompts. Vendor API key required
Tools
The server implements one tool:
unichat
: Send a request to unichat- Takes "messages" as required string arguments
- Returns a response
Prompts
code_review
- Review code for best practices, potential issues, and improvements
- Arguments:
code
(string, required): The code to review"
document_code
- Generate documentation for code including docstrings and comments
- Arguments:
code
(string, required): The code to comment"
explain_code
- Explain how a piece of code works in detail
- Arguments:
code
(string, required): The code to explain"
code_rework
- Apply requested changes to the provided code
- Arguments:
changes
(string, optional): The changes to apply"code
(string, required): The code to rework"
Quickstart
Install
Claude Desktop
On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
Supported Models:
A list of currently supported models to be used as
"SELECTED_UNICHAT_MODEL"
may be found here. Please make sure to add the relevant vendor API key as"YOUR_UNICHAT_API_KEY"
Example:
Development/Unpublished Servers Configuration
Published Servers Configuration
Installing via Smithery
To install Unichat for Claude Desktop automatically via Smithery:
Development
Building and Publishing
To prepare the package for distribution:
- Sync dependencies and update lockfile:
- Build package distributions:
This will create source and wheel distributions in the dist/
directory.
- Publish to PyPI:
Debugging
Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the MCP Inspector.
You can launch the MCP Inspector via npm
with this command:
Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.
Send requests to OpenAI, MistralAI, Anthropic, xAI, or Google AI using MCP protocol via tool or predefined prompts. Vendor API key required