This unichat-ts-mcp-server enables interaction with various AI models through the MCP protocol, offering:
- AI Model Access: Send chat requests to multiple vendors including OpenAI, MistralAI, Anthropic, xAI, Google AI, and DeepSeek using the
unichat
tool - Predefined Prompts: Execute specific code-related tasks like review, documentation, explanation, and rework using built-in templates
- Communication Options: Support for both STDIO and Server-Sent Events (SSE) transport mechanisms
- Configurable Settings: Customize model selection and API keys through environment variables (
UNICHAT_MODEL
,UNICHAT_API_KEY
) - Debugging Support: Includes tools like MCP Inspector for troubleshooting communication issues
Unichat MCP Server in TypeScript
Also available in Python
Send requests to OpenAI, MistralAI, Anthropic, xAI, Google AI or DeepSeek using MCP protocol via tool or predefined prompts. Vendor API key required.
Both STDIO and SSE transport mechanisms supported via arguments.
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"
Development
Install dependencies:
Build the server:
For development with auto-rebuild:
Running evals
The evals package loads an mcp client that then runs the index.ts file, so there is no need to rebuild between tests. You can load environment variables by prefixing the npx command. Full documentation can be found here.
Installation
Installing via Smithery
To install Unichat MCP Server for Claude Desktop automatically via Smithery:
Installing manually
To use with Claude Desktop, add the server config:
On MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
Run locally:
Run published:
Runs in STDIO by default or with argument
--stdio
. To run in SSE add argument--sse
Supported Models:
A list of currently supported models to be used as
"YOUR_PREFERRED_MODEL_NAME"
may be found here. Please make sure to add the relevant vendor API key as"YOUR_VENDOR_API_KEY"
Example:
Debugging
Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector, which is available as a package script:
The Inspector will provide a URL to access debugging tools in your browser.
If you experience timeouts during testing in SSE mode change the request URL on the inspector interface to: http://localhost:3001/sse?timeout=600000
This server cannot be installed
remote-capable server
The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.
Tools
Send requests to OpenAI, MistralAI, Anthropic, xAI, or Google AI using MCP protocol via tool or predefined prompts. Vendor API key required.
Both STDIO and SSE transport mechanisms are supported via arguments.
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