Atla

# Atla MCP Server > [!CAUTION] > This repository was archived on July 21, 2025. The Atla API is no longer active. An MCP server implementation providing a standardized interface for LLMs to interact with the Atla API for state-of-the-art LLMJ evaluation. > Learn more about Atla [here](https://docs.atla-ai.com). Learn more about the Model Context Protocol [here](https://modelcontextprotocol.io). <a href="https://glama.ai/mcp/servers/@atla-ai/atla-mcp-server"> <img width="380" height="200" src="https://glama.ai/mcp/servers/@atla-ai/atla-mcp-server/badge" alt="Atla MCP server" /> </a> ## Available Tools - `evaluate_llm_response`: Evaluate an LLM's response to a prompt using a given evaluation criteria. This function uses an Atla evaluation model under the hood to return a dictionary containing a score for the model's response and a textual critique containing feedback on the model's response. - `evaluate_llm_response_on_multiple_criteria`: Evaluate an LLM's response to a prompt across _multiple_ evaluation criteria. This function uses an Atla evaluation model under the hood to return a list of dictionaries, each containing an evaluation score and critique for a given criteria. ## Usage > To use the MCP server, you will need an Atla API key. You can find your existing API key [here](https://www.atla-ai.com/sign-in) or create a new one [here](https://www.atla-ai.com/sign-up). ### Installation > We recommend using `uv` to manage the Python environment. See [here](https://docs.astral.sh/uv/getting-started/installation/) for installation instructions. ### Manually running the server Once you have `uv` installed and have your Atla API key, you can manually run the MCP server using `uvx` (which is provided by `uv`): ```bash ATLA_API_KEY=<your-api-key> uvx atla-mcp-server ``` ### Connecting to the server > Having issues or need help connecting to another client? Feel free to open an issue or [contact us](mailto:support@atla-ai.com)! #### OpenAI Agents SDK > For more details on using the OpenAI Agents SDK with MCP servers, refer to the [official documentation](https://openai.github.io/openai-agents-python/). 1. Install the OpenAI Agents SDK: ```shell pip install openai-agents ``` 2. Use the OpenAI Agents SDK to connect to the server: ```python import os from agents import Agent from agents.mcp import MCPServerStdio async with MCPServerStdio( params={ "command": "uvx", "args": ["atla-mcp-server"], "env": {"ATLA_API_KEY": os.environ.get("ATLA_API_KEY")} } ) as atla_mcp_server: ... ``` #### Claude Desktop > For more details on configuring MCP servers in Claude Desktop, refer to the [official MCP quickstart guide](https://modelcontextprotocol.io/quickstart/user). 1. Add the following to your `claude_desktop_config.json` file: ```json { "mcpServers": { "atla-mcp-server": { "command": "uvx", "args": ["atla-mcp-server"], "env": { "ATLA_API_KEY": "<your-atla-api-key>" } } } } ``` 2. **Restart Claude Desktop** to apply the changes. You should now see options from `atla-mcp-server` in the list of available MCP tools. #### Cursor > For more details on configuring MCP servers in Cursor, refer to the [official documentation](https://docs.cursor.com/context/model-context-protocol). 1. Add the following to your `.cursor/mcp.json` file: ```json { "mcpServers": { "atla-mcp-server": { "command": "uvx", "args": ["atla-mcp-server"], "env": { "ATLA_API_KEY": "<your-atla-api-key>" } } } } ``` You should now see `atla-mcp-server` in the list of available MCP servers. ## Contributing Contributions are welcome! Please see the [CONTRIBUTING.md](CONTRIBUTING.md) file for details. ## License This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.