# Atla Mcp Server

> An MCP server implementation providing a standardized interface for LLMs to interact with the Atla API.

- **Type:** MCP server
- **Install:** `agentstack add mcp-atla-ai-atla-mcp-server`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [atla-ai](https://agentstack.voostack.com/s/atla-ai)
- **Installs:** 0
- **Category:** [Integrations](https://agentstack.voostack.com/c/integrations)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [atla-ai](https://github.com/atla-ai)
- **Source:** https://github.com/atla-ai/atla-mcp-server
- **Website:** https://docs.atla-ai.com

## Install

```sh
agentstack add mcp-atla-ai-atla-mcp-server
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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 The Atla API is no longer active.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).

  

## 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= 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": ""
      }
    }
  }
}
```

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": ""
      }
    }
  }
}
```

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.

## Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [atla-ai](https://github.com/atla-ai)
- **Source:** [atla-ai/atla-mcp-server](https://github.com/atla-ai/atla-mcp-server)
- **License:** MIT
- **Homepage:** https://docs.atla-ai.com

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** yes
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/mcp-atla-ai-atla-mcp-server
- Seller: https://agentstack.voostack.com/s/atla-ai
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.