Enable AI agents to interact with the Atla API for state-of-the-art LLMJ evaluation.
Atla mcp server connects Atla to AI assistants that speak the Model Context Protocol. 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.
Once connected, the assistant can call these 4 tools directly:
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 dictionaryevaluate_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 ofInstallation — The Installation tool exposed by this serverCursor — You should now see atla-mcp-server in the list of available MCP serversThe server is distributed via PyPI as atla-mcp-server, 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 one environment variable: ATLA_API_KEY. 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. Atla sits in that group, and the shape of its toolset — evaluate_llm_response, evaluate_llm_response_on_multiple_criteria, Installation 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 |
|---|---|
| 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 critiq |
| 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 |
| Installation | The Installation tool exposed by this server. |
| Cursor | You should now see atla-mcp-server in the list of available MCP servers. |
{
"mcpServers": {
"atla": {
"command": "uvx",
"args": ["atla-mcp-server"],
"env": {
"ATLA_API_KEY": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| ATLA_API_KEY | Credential the server authenticates with. | Yes |
Kill hallucinated APIs — version-accurate, up-to-date library documentation injected straight into context.
Microsoft's official browser automation server — drive a real browser through the accessibility tree, no screenshots needed.
GitHub's official server — repos, issues, pull requests, Actions and code security, straight from your assistant.
Issue tracking at the speed of conversation — Linear's official hosted server with OAuth and zero install.
Local repository surgery — status, diffs, commits, branches and history for any repo on disk.
Timezone sanity for AI — current time anywhere and correct conversions, without the model doing date math.