apps/opik-documentation/documentation/fern/docs-v2/prompt_engineering/mcp-server.mdx
One command connects your coding assistant to your traces — and teaches it how to create them in the first place:
opik mcp configure
Things you can ask for and get in one turn, without leaving your editor:
<CardGroup cols={2}> <Card title="Instrument this project" icon="fa-solid fa-wand-magic-sparkles"> Your assistant adds tracing in the right places for your framework, runs the app, and confirms the traces arrived. </Card> <Card title="Why did this get worse?" icon="fa-solid fa-magnifying-glass-chart"> It reads the failing traces and their scores directly, instead of you pasting screenshots into chat. </Card> <Card title="Build me a test suite" icon="fa-solid fa-vial"> From traces you already have, so the cases are real ones your app hit. </Card> <Card title="Keep an eye on this" icon="fa-solid fa-gauge-high"> Every later change can be checked against real traces as you make it. </Card> </CardGroup>The CLI detects your AI client (Claude Code, Cursor, VS Code Copilot, Codex, opencode), picks the right server for your Opik deployment, configures it, and then checks that the configuration it just wrote actually works.
<Tip> Prefer not to use the CLI? You can wire up any client by hand — skip to [Manual setup](#manual-setup). </Tip> <Note> **Asking your coding agent to do this works.** Setup writes into your AI client's own configuration, so it never happens on a run that did not ask for it — but naming what you want is asking, and that works without a terminal:opik configure --install-mcp --install-skills # Opik + MCP + skill pack
opik mcp configure --ai-client cursor # just the server, one client
A run that names nothing and has no terminal — a CI job, a Docker build — writes nothing, which is the behaviour you want there. </Note>
<Steps> <Step title="Install the Opik CLI"> The CLI ships with the `opik` Python package. `--upgrade` installs the latest release, which is what you want — these commands gain clients and flags often:```bash
pip install --upgrade opik
```
This reuses your existing Opik configuration (`~/.opik.config`); if you
haven't configured Opik yet, the wizard offers to do it for you first.
<Tip>
You'll choose your AI client from a list, then confirm the MCP server and
the Opik skill pack for it.
</Tip>
<Note>
If your client isn't detected, see [Manual setup](#manual-setup).
</Note>
Each AI client keeps its own copy of the MCP configuration, which isn't updated automatically when your Opik configuration changes. To see what every detected client points at — and whether it still matches your current Opik configuration — run:
opik mcp status
It prints your active Opik configuration, then each AI client that has the Opik MCP server configured: the config file it lives in, the server it reports to (hosted or local), its workspace, and whether it has drifted from your Opik configuration.
Your Opik configuration
File ~/.opik.config
Environment https://www.comet.com/opik/api
Workspace my-workspace
Opik MCP server — configured for 1 AI client:
Claude Code
Config ~/.claude.json
Connection Hosted (HTTP + OAuth)
Reports to https://www.comet.com/opik/api/v1/mcp
Status ✓ in sync with your Opik configuration
A client that has drifted is flagged ✗ OUT OF SYNC — re-run
opik mcp configure to fix it.
To view just your active Opik configuration (file path, environment, workspace):
opik configure status
Paste any of these into your assistant. Start with the first — it exercises the whole loop, so if it works, everything is wired up.
Add Opik tracing to this project, then run it and show me the trace you created.
List my Opik projects and tell me which one this repo is logging to.
Look at the last 20 traces in Opik and tell me what's slowest and what's failing.
Build an Opik test suite from my recent traces, then run it and show me the scores.
From then on your assistant can check its own work against real traces every time you change something.
Your assistant gets six tools. It picks between them on its own — this is here so you know what it can reach for:
| Tool | What your assistant can do with it |
|---|---|
read | Fetch one thing by id, name, or opik:// URI — a trace, span, project, experiment, prompt, or test suite. |
list | Page through any of those, optionally filtered by name. |
write | Log traces and spans, score, comment, save prompt versions, manage test suites and experiments. |
schema | Look up the exact payload shape for a write, so it constructs valid ones. |
ask_ollie | Investigate or synthesize across entities via Opik's in-product assistant. |
run_experiment | Run an evaluation experiment end to end. |
To see a payload shape yourself, ask "show me the schema for trace.create" — or read the full list.
opik mcp configure works the same whether you're on Opik Cloud, self-hosted,
or a local install — it sets up the right server for your deployment
automatically.
On Opik Cloud, the CLI registers the hosted MCP server over HTTP. Your AI client signs in with a browser-based OAuth flow on first connect, so:
uv is not required — there is no local process to run.opik mcp status.If no hosted server is available for your environment, the CLI sets up the
local server, which runs on demand via uvx opik-mcp. This requires
uv; if it isn't on your PATH the CLI stops and
prints the exact command to install it for your platform.
For the local server your workspace is written into the client's config, so it has
to be the right one. If your Opik configuration doesn't name a workspace and your
account has more than one, opik mcp configure refuses to continue rather
than falling back to your account default:
Your Opik configuration does not name a workspace, but this account has 3:
acme-ai, acme-research, sandbox. The MCP server would fall back to your default
workspace and silently read from the wrong place. Run `opik configure` and choose
a workspace, then re-run `opik mcp configure`.
Guessing here is the one failure this CLI can produce that doesn't look like a
failure: your agent would read real traces from the wrong workspace and report
them confidently. Run opik configure, pick a workspace, and re-run.
Prefer to wire it up yourself, or your client wasn't detected? Configure any client by hand below.
For the skill pack on a client the CLI doesn't cover, the community
skills CLI knows the skill directories
for 76+ agents (needs Node.js):
npx skills add comet-ml/opik-skills
uvx opik-mcp, stdio) — runs on your machine with your
credentials in the client's env block.
</Note>
The hosted server connects over HTTP and signs in with a browser-based OAuth
flow on first connect — no API key is stored in the client config. Point your client
at your deployment's MCP endpoint, which is your Opik API base plus /v1/mcp. On
Opik Cloud that is https://www.comet.com/opik/api/v1/mcp.
Add the server with one command:
```bash
claude mcp add --transport http opik-mcp https://www.comet.com/opik/api/v1/mcp
```
Or edit `~/.claude.json` directly:
```json
{
"mcpServers": {
"opik-mcp": {
"type": "http",
"url": "https://www.comet.com/opik/api/v1/mcp"
}
}
}
```
Restart Claude Code and complete the browser sign-in when prompted, then ask
in the chat: **"list my Opik projects"**.
</Tab>
<Tab title="Cursor">
Edit `~/.cursor/mcp.json` (global) or `.cursor/mcp.json` (project):
```json
{
"mcpServers": {
"opik-mcp": {
"type": "http",
"url": "https://www.comet.com/opik/api/v1/mcp"
}
}
}
```
Reload Cursor and complete the browser sign-in when prompted.
</Tab>
<Tab title="VS Code Copilot">
Create or open `.vscode/mcp.json` in your workspace:
```json
{
"servers": {
"opik-mcp": {
"type": "http",
"url": "https://www.comet.com/opik/api/v1/mcp"
}
}
}
```
Reload the window and complete the browser sign-in when prompted.
</Tab>
The local server runs on demand via uvx opik-mcp (requires
uv), with your credentials passed through the
client's env block.
Add the server with one command:
```bash
claude mcp add --transport stdio opik-mcp \
--env OPIK_API_KEY=<your-key> \
--env OPIK_WORKSPACE=<your-workspace> \
-- uvx opik-mcp
```
Or edit `~/.claude.json` directly:
```json
{
"mcpServers": {
"opik-mcp": {
"type": "stdio",
"command": "uvx",
"args": ["opik-mcp"],
"env": {
"OPIK_API_KEY": "<your-key>",
"OPIK_WORKSPACE": "<your-workspace>"
}
}
}
}
```
Restart Claude Code, verify with `/mcp` (`opik-mcp` should appear as
connected), and then ask in the chat: **"list my Opik projects"**.
</Tab>
<Tab title="Cursor">
Edit `~/.cursor/mcp.json` (global) or `.cursor/mcp.json` (project), or open
**Cmd+Shift+J → Features → Model Context Protocol**:
```json
{
"mcpServers": {
"opik-mcp": {
"type": "stdio",
"command": "uvx",
"args": ["opik-mcp"],
"env": {
"OPIK_API_KEY": "<your-key>",
"OPIK_WORKSPACE": "<your-workspace>"
}
}
}
}
```
Reload Cursor; the green dot next to `opik-mcp` in the MCP panel confirms
the connection. Ask in chat: **"list my Opik projects"**.
<Tip>
**Cursor 60s timeout.** Cursor enforces a hard tool-call timeout that does
not reset on progress notifications. Long `ask_ollie` turns will fail on
Cursor — see [Known client limits](#known-client-limits).
</Tip>
</Tab>
<Tab title="VS Code Copilot">
Create or open `.vscode/mcp.json` in your workspace (or run the
**MCP: Open User Configuration** command to add it globally):
```json
{
"servers": {
"opik-mcp": {
"type": "stdio",
"command": "uvx",
"args": ["opik-mcp"],
"env": {
"OPIK_API_KEY": "<your-key>",
"OPIK_WORKSPACE": "<your-workspace>"
}
}
}
}
```
Reload the window. The Copilot Chat **MCP** indicator shows `opik-mcp` once
the server is reachable. Ask in chat: **"list my Opik projects"**.
</Tab>
<Tab title="Codex">
Add the server with one command:
```bash
codex mcp add opik-mcp \
--env OPIK_API_KEY=<your-key> \
--env OPIK_WORKSPACE=<your-workspace> \
-- uvx opik-mcp
```
Or add an `[mcp_servers.opik-mcp]` table to `~/.codex/config.toml`:
```toml
[mcp_servers.opik-mcp]
command = "uvx"
args = ["opik-mcp"]
[mcp_servers.opik-mcp.env]
OPIK_API_KEY = "<your-key>"
OPIK_WORKSPACE = "<your-workspace>"
```
Confirm with `codex mcp get opik-mcp`, then ask in the chat:
**"list my Opik projects"**.
<Note>
`opik mcp configure --ai-client codex` drives the `codex` CLI rather than editing
`config.toml`, so your comments and formatting are left alone. If the `codex`
CLI isn't on your `PATH` it tells you to add the table by hand instead of
rewriting your TOML.
</Note>
</Tab>
<Tab title="opencode">
Edit `~/.config/opencode/opencode.json` (or the file in
`$OPENCODE_CONFIG_DIR`). Note that opencode uses its own vocabulary: `local`
rather than `stdio`, one `command` list holding the executable and its
arguments, and `environment` rather than `env`:
```json
{
"mcp": {
"opik-mcp": {
"type": "local",
"command": ["uvx", "opik-mcp"],
"environment": {
"OPIK_API_KEY": "<your-key>",
"OPIK_WORKSPACE": "<your-workspace>"
},
"enabled": true
}
}
}
```
Restart opencode, then ask in the chat: **"list my Opik projects"**.
</Tab>
<Tab title="MCP Inspector">
For manual testing or debugging, run the inspector against `opik-mcp`:
```bash
OPIK_API_KEY=<your-key> OPIK_WORKSPACE=<your-workspace> \
npx @modelcontextprotocol/inspector uvx opik-mcp
```
The inspector opens in your browser and lets you call each tool directly.
</Tab>
By default, writes that Ollie performs mid-stream (scores, comments, prompt
versions, test-suite items) execute without a per-action confirmation step.
Each auto-approved write is logged as a JSON audit row on the opik_mcp.audit
Python logger.
To require manual confirmation instead, set OPIK_MCP_AUTO_APPROVE=disabled in
the server's env block. Ollie's confirmation requests then surface as typed
errors that you can re-issue manually.
ask_ollie and run_experiment are available on Comet Cloud only — on
self-hosted those calls fail at dispatch; use read / list / write
directly.
ask_ollie turns will fail on Cursor. For
long-running investigations, use Claude Code or VS Code Copilot.A typical investigative loop using Claude Code:
You: Why did the experiment "gpt-4o-rerank-v3" regress on factuality?
Claude: (calls
ask_ollie) Three traces failed because the reranker dropped the system message. The remaining 12 traces scored above 0.8…You: Score the bottom 3 traces 0.2 with reason "dropped system message".
Claude: (calls
writewithscore.create×3) Done — three scores recorded on traces<id-1>,<id-2>,<id-3>.