Comet Opik
About
Query and analyze your [Opik](https://github.com/comet-ml/opik) logs, traces, prompts and all other telemtry data from your LLMs in natural language.
Details
- License
- Apache-2.0
Explore
- Six outcome-oriented tools: read, list, ask_ollie, write, schema, run_experiment
- Universal read and list by id, name, or opik:// URI
- In-workspace investigation via Ollie with YOLO-mode auto-approval
- Write operations: log traces, spans, scores, comments, prompt versions, test suites, experiments
- Schema introspection for constructing valid write payloads
- End-to-end experiment execution via Ollie on Comet Cloud
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Comet OpikCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Install
opik-mcp is a Python package (requires Python 3.13+). The recommended way to
run it is uvx, which fetches and runs the latest published version on demand —
no global install, no virtualenv juggling.
Install uv once:
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh # macOS / Linux
opik-mcp exposes a small, outcome-oriented surface — six tools that cover
the full lifecycle (read → annotate → curate → author → iterate).
| Tool | Purpose |
|---|---|
| read | Universal read by id / name / opik:// URI |
| list | Universal list with optional name filter + pagination |
| ask_ollie | Investigate / synthesize via the Opik in-product assistant |
| write | Universal write — log traces/spans, score, comment, save prompts, manage test suites & experiments |
| schema | Introspect write-operation schemas (used by the LLM to construct valid payloads) |
| run_experiment | Run an evaluation experiment end-to-end via Ollie |
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"comet opik": {
"opik-mcp": {
"command": "npx",
"args": [
"@modelcontextprotocol/inspector",
"uvx",
"opik-mcp"
]
}
}
}
}McpServers
{
"opik-mcp": {
"command": "npx",
"args": [
"@modelcontextprotocol/inspector",
"uvx",
"opik-mcp"
]
}
}opik-mcp
> Migrating from the old npx opik-mcp? The TypeScript server is deprecated
> and sunsets on 2026-11-15. Swap npx -y opik-mcp for uvx opik-mcp@latest
> in your MCP client config. Full guide: legacy/typescript/MIGRATION.md.
Model Context Protocol server for Opik + Ollie.
Plug your AI host (Claude Code, Cursor, VS Code Copilot, MCP Inspector) directly
into your Opik workspace — read traces, log scores, save prompt versions, and
ask Ollie investigative questions, all from the chat.
Built for LLM engineers who already run Opik and want to drive it from the same
AI assistant they code with.
You: "Why did the experiment 'gpt-4o-rerank-v3' regress on factuality?"
Claude: → ask_ollie → reads experiment + traces → "Three traces failed because…"
You: "Score trace 7f2e… 0.9 on helpfulness with reason 'great recovery'."
Claude: → write(score.create) → done
---
Install
opik-mcp is a Python package (requires Python 3.13+). The recommended way to
run it is uvx, which fetches and runs the latest published version on demand —
no global install, no virtualenv juggling.
Install uv once:
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh # macOS / Linux
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