Send requests to OpenAI, Anthropic, and OpenAI-compatible providers using MCP protocol via tool or predefined prompts. For OpenAI-compatible
Unichat mcp server connects Unichat to AI assistants that speak the Model Context Protocol. Send requests to OpenAI, Anthropic, and OpenAI-compatible providers using MCP protocol via tool or predefined prompts. For OpenAI-compatible providers such as MistralAI, xAI, Google AI, DeepSeek, Alibaba, or Inception, set UNICHAT_BASE_URL to the provider's compatible API endpoint. Vendor API key.
Send requests to OpenAI, Anthropic, and OpenAI-compatible providers using MCP protocol via tool or predefined prompts. For OpenAI-compatible providers such as MistralAI, xAI, Google AI, DeepSeek, Alibaba, or Inception, set UNICHAT_BASE_URL to the provider's compatible API endpoint. Vendor API key required
Once connected, the assistant can call these 4 tools directly:
unichat — Send a request to unichatTools — The server implements one tool: - unichat: Send a request to unichat - Takes "messages" as required string arguments - Returns a responsePrompts — The Prompts tool exposed by this serverDebugging — Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the [MCPThe server is distributed via npm as @smithery/cli, so most clients can run it without a manual build step. Add it to your MCP client's configuration and restart the client to pick it up — the copy-paste configs for Claude Desktop, Claude Code and Cursor are on this page.
Before the server will start you need to supply 7 environment variables: UNICHAT_MODEL, UNICHAT_API_KEY, UNICHAT_BASE_URL, YOUR_UNICHAT_API_KEY, YOUR_OPENAI_API_KEY, YOUR_PROVIDER_API_KEY, YOUR_PYPI_API_TOKEN. Keep credentials in your client's env block or a secrets manager rather than committing them.
AI-service servers chain other models into your assistant, turning a single chat into a small production pipeline. Unichat sits in that group, and the shape of its toolset — unichat, Tools, Prompts among others — tells you what it is really for. Worth comparing against the other ai services servers in this directory before you commit to one, since several overlap in scope but differ sharply in setup cost and permissions.
| Tool | What it does |
|---|---|
| unichat | Send a request to unichat |
| Tools | The server implements one tool: - unichat: Send a request to unichat - Takes "messages" as required string arguments - Returns a response |
| Prompts | The Prompts tool exposed by this server. |
| Debugging | Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the [MCP Inspector](https://github.com/modelcontextprotocol/inspector). |
"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"
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| UNICHAT_MODEL | Configuration value read at startup. | Optional |
| UNICHAT_API_KEY | Credential the server authenticates with. | Yes |
| UNICHAT_BASE_URL | Endpoint or connection string the server talks to. | Yes |
| YOUR_UNICHAT_API_KEY | Credential the server authenticates with. | Yes |
| YOUR_OPENAI_API_KEY | Credential the server authenticates with. | Yes |
| YOUR_PROVIDER_API_KEY | Credential the server authenticates with. | Yes |
| YOUR_PYPI_API_TOKEN | Credential the server authenticates with. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.