Enable AI agents to interact with the Atla API for state-of-the-art LLMJ evaluation.
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. Learn more about the Model Context Protocol here.
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.To use the MCP server, you will need an Atla API key. You can find your existing API key here or create a new one here.
We recommend using
uv
to manage the Python environment. See here for installation instructions.
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
):
ATLA_API_KEY=<your-api-key> uvx atla-mcp-server
Having issues or need help connecting to another client? Feel free to open an issue or contact us!
For more details on using the OpenAI Agents SDK with MCP servers, refer to the official documentation.
pip install openai-agents
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:
...
For more details on configuring MCP servers in Claude Desktop, refer to the official MCP quickstart guide.
claude_desktop_config.json
file:{
"mcpServers": {
"atla-mcp-server": {
"command": "uvx",
"args": ["atla-mcp-server"],
"env": {
"ATLA_API_KEY": "<your-atla-api-key>"
}
}
}
}
You should now see options from atla-mcp-server
in the list of available MCP tools.
For more details on configuring MCP servers in Cursor, refer to the official documentation.
.cursor/mcp.json
file:{
"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.
Contributions are welcome! Please see the CONTRIBUTING.md file for details.
This project is licensed under the MIT License. See the LICENSE file for details.
Obtains latest dependency details for Clojure libraries.
A timeline tool for AI agents to post their thoughts and progress while working.
Retrieve README files and package information from CocoaPods.
An AI agent using the Model Context Protocol (MCP) with a Node.js server providing REST resources for users and messages.
Perform accessibility audits on webpages using the axe-core engine to identify and help fix a11y issues.
A template for deploying a remote MCP server on Cloudflare Workers, allowing for custom tool integration.
A Binary Ninja plugin, MCP server, and bridge that seamlessly integrates Binary Ninja with your favorite MCP client.
MCP Server that exposes Creatify AI API capabilities for AI video generation, including avatar videos, URL-to-video conversion, text-to-speech, and AI-powered editing tools.
Open-source tool for collaborative editing, versioning, evaluating, and releasing prompts.
A server for securely executing commands on the host system, requiring Java 21 or higher.