Unichat▌

by amidabuddha
Unichat lets you interact with multiple LLM chat APIs easily through a unified interface. Simplify your workflows with U
Interact with multiple LLM chat APIs through a unified interface.
best for
- / Developers wanting to compare responses across LLM providers
- / Code review and documentation workflows
- / Teams using multiple AI models without switching tools
capabilities
- / Send messages to 8+ LLM providers via one interface
- / Review code for best practices and issues
- / Generate code documentation and comments
- / Explain code functionality in detail
- / Apply requested changes to code
what it does
Sends chat requests to multiple LLM APIs (OpenAI, Anthropic, Google, etc.) through a single unified interface. Includes built-in prompts for code review, documentation, and explanation tasks.
about
Unichat is a community-built MCP server published by amidabuddha that provides AI assistants with tools and capabilities via the Model Context Protocol. Unichat lets you interact with multiple LLM chat APIs easily through a unified interface. Simplify your workflows with U It is categorized under ai ml, developer tools.
how to install
You can install Unichat in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
license
MIT
Unichat is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
Unichat MCP Server in Python
Also available in TypeScript
<h4 align="center"> <a href="https://mseep.ai/app/amidabuddha-unichat-mcp-server"> <img src="https://mseep.net/pr/amidabuddha-unichat-mcp-server-badge.png" alt="MseeP.ai Security Assessment Badge" /> </a> </h4> <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://archestra.ai/mcp-catalog/amidabuddha__unichat-mcp-server"> <img src="https://archestra.ai/mcp-catalog/api/badge/quality/amidabuddha/unichat-mcp-server" alt="Trust Score" /> </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> <h4 align="center"> <a href="https://mcphub.com/mcp-servers/amidabuddha/unichat-mcp-server"> <img src="https://img.mcphub.com/_next/image?url=%2Flogo-dark.png&w=48&q=75" alt="Hosted at MCPHub" /> </a> </h4>Send requests to OpenAI, MistralAI, Anthropic, xAI, Google AI, DeepSeek, Alibaba, Inception 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:
"env": {
"UNICHAT_MODEL": "gpt-4o-mini",
"UNICHAT_API_KEY": "YOUR_OPENAI_API_KEY"
}
Development/Unpublished Servers Configuration
"mcpServers": {
"unichat-mcp-server": {
"command": "uv",
"args": [
"--directory",
"{{your source code local directory}}/unichat-mcp-server",
"run",
"unichat-mcp-server"
],
"env": {
"UNICHAT_MODEL": "SELECTED_UNICHAT_MODEL",
"UNICHAT_API_KEY": "YOUR_UNICHAT_API_KEY"
}
}
}
Published Servers Configuration
"mcpServers": {
"unichat-mcp-server": {
"command": "uvx",
"args": [
"unichat-mcp-server"
],
"env": {
"UNICHAT_MODEL": "SELECTED_UNICHAT_MODEL",
"UNICHAT_API_KEY": "YOUR_UNICHAT_API_KEY"
}
}
}
Installing via Smithery
To install Unichat for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install unichat-mcp-server --client claude
Development
Building and Publishing
To prepare the package for distribution:
- Remove older builds:
rm -rf dist
- Sync dependencies and update lockfile:
uv sync
- Build package distributions:
uv build
This will create source and wheel distributions in the dist/ directory.
- Publish to PyPI:
uv publish --token {{YOUR_PYPI_API_TOKEN}}
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:
npx @modelcontextprotocol/inspector uv --directory {{your source code local directory}}/unichat-mcp-server run unichat-mcp-server
Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.
FAQ
- What is the Unichat MCP server?
- Unichat is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for Unichat?
- This profile displays 10 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.
Ratings
4.5★★★★★10 reviews- ★★★★★Shikha Mishra· Oct 10, 2024
Unichat is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Piyush G· Sep 9, 2024
We evaluated Unichat against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Chaitanya Patil· Aug 8, 2024
Useful MCP listing: Unichat is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Sakshi Patil· Jul 7, 2024
Unichat reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Ganesh Mohane· Jun 6, 2024
I recommend Unichat for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Oshnikdeep· May 5, 2024
Strong directory entry: Unichat surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Dhruvi Jain· Apr 4, 2024
Unichat has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Rahul Santra· Mar 3, 2024
According to our notes, Unichat benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Pratham Ware· Feb 2, 2024
We wired Unichat into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Yash Thakker· Jan 1, 2024
Unichat is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.