Opik MCP Server

The official Model Context Protocol (MCP) server for Opik, the open-source LLM observability and evaluation platform, built by Comet. Plug your AI host (Claude Code, Cursor, VS Code Copilot, Codex, opencode, or any MCP client) directly into your Opik workspace: read traces, log scores, and save prompt versions, 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.

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.

You:    "Which traces in project 'demo' failed today?"
Claude: → list(entity_type="trace", project_name="demo") → "Three traces failed…"

You:    "Score trace 7f2e… 0.9 on helpfulness with reason 'great recovery'."
Claude: → write(score.create) → done

---

Quick start

One command registers the server with the AI clients on your machine, installs the Opik skill pack, and verifies the connection. It needs uv and no Opik SDK:

uvx opik mcp configure

It detects Claude Code, Cursor, VS Code Copilot, Codex and opencode, and uses the hosted server on Opik Cloud (browser sign-in, no API key stored) or this local server elsewhere. Any other MCP client can take the hosted URL directly:

npx add-mcp https://www.comet.com/opik/api/v1/mcp --name opik-mcp

Setup guide, troubleshooting and FAQ: comet.com/docs/opik/mcp-server. The rest of this README covers the local server, which the command above sets up for self-hosted and open-source Opik, and which you can also configure by hand.

---

Manual 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:

curl -LsSf https://astral.sh/uv/install.sh | sh   # macOS / Linux
# or: brew install uv

You'll need two things from your Opik workspace:

• OPIK_API_KEY — get it from comet.com/api/my/settings/.
• OPIK_WORKSPACE — your workspace name (lowercase, as it appears in the URL). E.g. https://www.comet.com/acme-ai/... → OPIK_WORKSPACE=acme-ai. COMET_WORKSPACE is accepted as a deprecated alias.

Cloud, with an API key: set it unless your account default is the one you want. Left out, the server sends default, which Comet resolves to your account's default workspace. That works, but if you actually work in a named workspace you will be pointed at a different one with nothing to tell you — your reads come back from the wrong place rather than failing.

Cloud, over OAuth: leave it unset. The workspace comes from the token you authorized, and the server ignores this setting entirely.

Local / open source: leave it unset. Open source Opik has a single workspace named default and no way to create others, which is exactly what the fallback gives you.

Self-hosted Comet: set it. Unlike open source, these deployments have real named workspaces, and the same silent-wrong-workspace risk applies.

Whichever applies, make sure the value is actually substituted. Snippets in the wild ship placeholders like <your-workspace> or ${input:OPIK_WORKSPACE}; pasted as-is, those are not workspace names. The server now refuses them outright rather than letting the backend answer with an auth error that explains nothing.

Claude Code

Add the server with one command:

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:

{
  "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. Then, in the chat, ask: "list my Opik projects" — Claude will call the list tool and you'll see your workspace's projects.

Cursor

Edit ~/.cursor/mcp.json (global) or .cursor/mcp.json (project), or open Cmd+Shift+J → Features → Model Context Protocol:

{
  "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".

Cursor 60s timeout. Cursor enforces a hard tool-call timeout that doesn't reset on progress notifications. See Known host limits.

VS Code Copilot

.vscode/mcp.json in your workspace (or User Settings 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".

MCP Inspector (manual testing)

OPIK_API_KEY=<your-key> OPIK_WORKSPACE=<your-workspace> \
  npx @modelcontextprotocol/inspector uvx opik-mcp

Self-hosted Opik

Add COMET_URL_OVERRIDE (and OPIK_URL if Opik lives at a non-default path) to the same env block in your host config:

{
  "mcpServers": {
    "opik-mcp": {
      "type": "stdio",
      "command": "uvx",
      "args": ["opik-mcp"],
      "env": {
        "OPIK_API_KEY": "<your-key>",
        "OPIK_WORKSPACE": "<your-workspace>",
        "COMET_URL_OVERRIDE": "https://opik.your-company.com",
        "OPIK_MCP_ANALYTICS_SOURCE": ""
      }
    }
  }
}

Omit OPIK_WORKSPACE on an open-source deployment, where default is the only workspace; keep it on a self-hosted Comet, which has real named ones.

Setting OPIK_MCP_ANALYTICS_SOURCE="" opts your install out of the cloud-Comet source label on telemetry events.

---

Tools

opik-mcp exposes a small, outcome-oriented surface that covers 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
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)
read_skill	Read one of the Opik agent skills bundled with this server

read

One tool for any "show me X" question. Takes an entity_type plus an id (UUID or, for nameable types, a name) or a full opik:// URI. Composite reads (trace, prompt, thread, agent_insights_issue) inline their children so a single call returns the full picture.

The record you name comes back whole. Inlined children do not: their bodies are fetched with the backend's truncate=true, so a field over ~10 KB is cut in ClickHouse and base64 images are replaced with "[image]" — one attachment echoed across 200 spans would otherwise cost more than everything else in the read. The answer says so in spanBodies / messageBodies, and any child is whole again through its own read("span", id) or read("trace", trace_id), which hit endpoints that have no truncate parameter at all.

An inlined collection is also bounded in length: 200 spans, 200 turns, 100 prompt versions. Past that, spansTruncated / messagesTruncated / versionsTruncated is true and a moreSpans / moreMessages / moreVersions line beside it carries the count and the exact list(...) call that continues from where the inlined part stopped.

Supported entities: project, trace, span, test_suite, experiment, prompt, thread, agent_insights_issue. Name-based lookup is available for project, experiment, prompt, test_suite (slower — two API calls — and may return multiple matches). thread and agent_insights_issue are project-scoped: pass project_id or project_name, or a link/URI that carries the project.

read(entity_type="trace", id="7f2e3c8a-…")
read(entity_type="project", id="demo")          # name lookup
read(entity_type="trace", id="opik://traces/7f2e3c8a-…")
read(entity_type="agent_insights_issue", id="<issue-uuid>", project_id="<project-uuid>")
read(entity_type="agent_insights_issue", id="https://www.comet.com/opik/<ws>/projects/<pid>/diagnostics?issue=<id>")

A link copied from the Opik UI works as the id: a thread link or a Diagnostics page link carries the project, so no project_id is needed and the entity type is taken from the link.

A project read answers "how is my project doing" in one call. It returns {project, summary, vocabulary, contains, url}: the record, then the four figures the Logs page shows as cards (trace count, error rate, average duration, total cost) for the last 7 days against the 7 before, SDK traffic only, as on screen. since / until move that window; since="30d" is what the UI opens on. A rate or an average over a period with no traces comes back as null, because 0% errors on a week with no traffic reads as a healthy week.

vocabulary is the map you need before you can ask anything else: the project's feedback score names, its token usage keys, and the automation rules scoring its traces. These are the names that go into a filter or into series= below, and guessing them returns an empty page that reads like good news. Score names and rules are capped, always report the true total, and name the call that returns the rest; usage keys are listed in full, since nothing else enumerates them. contains names the freshest experiment, test suite, prompt version and optimization run, so "what has been happening here" does not need four more calls. A part that failed to load says so instead of looking empty, and an empty one is omitted.

An agent_insights_issue read returns {issue, example_trace_ids, details}: the Diagnostics issue record (name, description, cause, suggested fix, severity, status), the deduplicated ids of the traces that exhibit it (the same sample the Diagnostics page shows — open one with read("trace", id)), and the per-day breakdown. Trace bodies are not inlined, so the read stays one backend call. since / until narrow the per-day rows; the default is all-time. When the server knows the Opik URL and the session's workspace, the read also carries url (the issue's Diagnostics page) and trace_url_template (a deep link for any of the example traces), so the assistant can hand you something clickable; under an OAuth session whose workspace could not be resolved the links are omitted rather than guessed.

Traces themselves carry no URL — a link for one is not derivable from the fields a read or list returns, and a guessed shape 404s. The session instructions name a template for it instead, .../v1/session/redirect/projects/?trace_id={trace_id}&path=..., so the assistant fills in an id and hands you a link. It goes through opik-backend's redirect, which resolves the project and the workspace from the trace, so it works where a direct project URL cannot, an OAuth session with an unresolved workspace included. It is the same link the Python SDK prints for a trace.

list

Browse or search a collection with pagination. Project-scoped types (trace, span, thread, agent_insights_issue, test_suite_item, prompt_version) need their parent: a project UUID or name, a suite UUID, or a prompt UUID.

list(entity_type="experiment", page=1, size=25)
list(entity_type="experiment", name="rerank")          # name substring filter
list(entity_type="agent_insights_issue", project_name="demo")             # open Diagnostics issues
list(entity_type="agent_insights_issue", project_id="<uuid>", status="resolved")
list(entity_type="trace", project_name="demo")         # latest traces of one project
list(entity_type="trace", project_name="demo",
     filters='error_info is_not_empty AND duration > 5000')
list(entity_type="span", project_name="demo",          # spans across the whole project
     filters='type = "llm" AND usage.total_tokens > 10000')
list(entity_type="thread", project_name="demo",
     filters='number_of_messages > 20 AND feedback_scores.helpfulness < 0.5')
list(entity_type="experiment",
     filters='dataset_id = "<dataset-uuid>" AND tags contains "baseline"')

Filters. trace, span, thread and experiment take an OQL string, the same grammar as the SDK's search_traces(filter_string=…):

<field>[.<key>] <op> <value> [AND ...]
ops: = != > >= < <= contains not_contains starts_with ends_with is_empty is_not_empty in not_in

Strings go in double quotes, numbers are bare, duration is in milliseconds, dates are ISO-8601 instants with a timezone ("2026-09-08T10:00:00Z"). Scores and dictionaries take a key: feedback_scores.accuracy < 0.5, metadata.environment = "prod". AND is the only connector.

Like the UI's Logs page, trace, span and thread lists add source = "sdk" so evaluator, playground and experiment traces stay out of the way; name source yourself to see them. The first output line echoes the filter that was applied.

A bad filter fails before reaching the backend with what is needed to fix it: the position of a syntax error, the closest field name, the valid operators for the field's type, or the expected value format. Fields with a closed set of values (source, span type, thread status, visibility_mode) are checked against it too, every element of an in list included. source is the one the backend validates itself, and it answers an unknown value with a 500 rather than a 400, so source = "SDK" would otherwise be an opaque server error for a capital letter. The rest are compared as strings and answer with an empty page, which reads as "no matches" when it means "no such value". Ask schema("list.trace") (or list.span, list.thread, list.experiment) for the full field reference, accepted values included.

Sort. The same four types take sort="<field> [asc|desc]", desc by default and one field only: sort="duration desc", sort="total_estimated_cost", sort="feedback_scores.accuracy asc", sort="usage.total_tokens". The field is checked against the entity's sortable list before the call, because the backend silently ignores fields it cannot sort by. On very large workspaces the backend drops sorting altogether; the header says so when that happens.

Time window and search. trace, span and thread take since and until, each a relative span ("30m", "1h", "7d") or an ISO-8601 instant with a timezone, so "the last hour" needs no clock arithmetic. The window is by record creation time, which is cheap for the backend and agrees with start_time within seconds for live traffic. For an exact bound, put start_time in filters. The same three types take search, free text matched anywhere in id, name, input, output, metadata, tags and thread id. Search scans the whole project on the backend, so the first call on a large project can take tens of seconds. Those calls get a 60-second timeout. Adding since makes them fast again.

Reading the table. Durations are labelled duration_ms / ttft_ms and shown as whole milliseconds; the field stays duration in filters and sort. Timestamps are shown to the second and costs as plain decimals. Project rows carry last_updated_trace_at so you can see which project has live traffic; thread rows carry the first message. An empty page under a time window says when the project's last trace landed, and an empty page under the default source = "sdk" says how to see the other sources. A misspelled project_name comes back with the closest existing name.

list(entity_type="trace", project_name="demo", since="1h",
     filters="error_info is_not_empty", sort="duration desc")
list(entity_type="trace", project_name="demo", search="order-42")

Diagnostics issues. agent_insights_issue is the Diagnostics page over the MCP: the recurring failures Opik's Diagnostics job grouped for a project, ranked as the UI ranks them (most recently seen first). Columns are severity, status, total_occurrences (all-time sum), latest_count (the most recent report day, the number the issue's own description refers to) and last_seen. Open issues are listed by default; pass status="resolved" or "closed" for the rest. read and list also answer to issue, which is what the UI calls these; the long name is the one in the entity_type enum, so that one entity does not appear there twice. Counts are all-time so they match the UI; the same since / until as for traces narrow the window, truncated to UTC report days because Diagnostics aggregates per day.

An empty list says why it is empty, because "nothing is broken" and "nobody turned Diagnostics on" read the same otherwise. There are five states: Diagnostics is unavailable on this deployment, not enabled for this project, turned off, enabled but not scanned recently, or enabled and clean with the time of the last scan. The ones you can act on name the call to make, and every state links the project's Diagnostics page.

A non-empty list dates itself. The issues are whatever the last scan grouped, so the reply ends with Report covers data through <time>, and when the window you asked about runs past that, it names the uncovered tail and how to close it: a trigger when a rescan reaches back far enough, otherwise raw traces with the since it gives you. Ask for a week on a project scanned nightly and the last day is missing from the grouped answer; this is what says so.

write("agent_insights_job.enable", {"project_name": "demo"}) turns Diagnostics on. It scans daily from then on, and calling it again is safe. write("agent_insights_job.trigger", …) scans the last 24 hours now, without waiting for the nightly run. Both take the permission that reading issues takes, and both refuse where the deployment has no Diagnostics.

An issue moves through its lifecycle with write("agent_insights_issue.resolve", {"issue_id": "<uuid>", "project_name": "demo"}) — dealt with — or …close for one not worth acting on, and …reopen to put either back on the open list. All three take the same permission and answer with a link to the view the issue moved to, since a resolved issue is no longer on the default page. Whether a failure is fixed is a judgment call, so these are for when you ask: the assistant has no business tidying the list while triaging it.

Metrics over time. project_metric charts one metric for a project as a table of time buckets: trace, span and thread counts, durations, error rates, costs, token usage and feedback scores. It answers the question that follows the overview, which is when something changed.

list(entity_type="project_metric", project_name="demo", metric_type="trace_count")
list(entity_type="project_metric", project_name="demo", metric_type="trace_error_rate",
     since="14d", interval="daily")
list(entity_type="project_metric", project_name="demo", metric_type="span_count",
     breakdown="model")                      # one column per model
list(entity_type="project_metric", project_name="demo", metric_type="span_duration",
     breakdown="model", series="p99")        # the p99 of each model

Rows are time buckets, not records, so page, size and sort are refused rather than ignored. interval is hourly, daily, weekly or total; left out, it follows the window the way the Metrics tab does — hourly up to 3 days, daily up to 30, weekly beyond — so a default chart is a few dozen rows whatever the range, and an hourly month (721 rows) is something you ask for. since / until take the same forms as everywhere else and default to the last 7 days. filters uses the fields of whichever entity the metric is about, so a span metric is filtered by span fields.

breakdown splits each bucket by tags, name, error_info, error_type, model, provider, span_type, guardrail_name or metadata.<key>. Not every metric accepts every one of those, and seven accept none at all; the tool knows which and says so before calling the backend, naming a metric that does answer the same question where one exists. Three families come back as several series at once (a duration as p50/p90/p99, a feedback score per name, token usage per key), and the backend charts one of them at a time when grouping, so series= picks it: a percentile, a score name, or a usage key. Duration defaults to p50 and token usage to total_tokens, and whichever was used is echoed on the first line.

Empty buckets are left out and counted underneath, so a quiet month is a few rows instead of a column of zeros, and a rate over a bucket with no traces is absent rather than reported as zero.

Ask schema("list.project_metric") for the metric table, the intervals and the per-metric grouping matrix.

A project's names. score_name lists the feedback score names recorded in a project and online_rule the automation rule evaluators configured on it, which is where most of those names come from. Both are the same lists read("project", …) carries, in full and paginated, for when the capped version in the overview is not enough.

list(entity_type="score_name", project_name="demo")
list(entity_type="online_rule", project_name="demo")

write

Universal write dispatcher. Pass operation + data and the dispatcher validates the payload, applies the right REST verb, and returns the backend response.

Operations: