opik-mcp MCP — Use Cases, Install & Live Demo

Why use it

Key features

Official, maintained by Comet
Prompt lifecycle: list, get, version, promote
Workspace / project / trace exploration
Dataset + metric operations for evals
Cloud (comet.com) or self-hosted; HTTP bearer auth supported

Live Demo

What it looks like in practice

opik.replay ▶ ready

0/0

Install

Pick your client

~/Library/Application Support/Claude/claude_desktop_config.json · Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "opik": {
      "command": "npx",
      "args": [
        "-y",
        "opik-mcp"
      ],
      "_inferred": true
    }
  }
}

Open Claude Desktop → Settings → Developer → Edit Config. Restart after saving.

~/.cursor/mcp.json · .cursor/mcp.json

{
  "mcpServers": {
    "opik": {
      "command": "npx",
      "args": [
        "-y",
        "opik-mcp"
      ],
      "_inferred": true
    }
  }
}

Cursor uses the same mcpServers schema as Claude Desktop. Project config wins over global.

VS Code → Cline → MCP Servers → Edit

{
  "mcpServers": {
    "opik": {
      "command": "npx",
      "args": [
        "-y",
        "opik-mcp"
      ],
      "_inferred": true
    }
  }
}

Click the MCP Servers icon in the Cline sidebar, then "Edit Configuration".

~/.codeium/windsurf/mcp_config.json

{
  "mcpServers": {
    "opik": {
      "command": "npx",
      "args": [
        "-y",
        "opik-mcp"
      ],
      "_inferred": true
    }
  }
}

Same shape as Claude Desktop. Restart Windsurf to pick up changes.

~/.continue/config.json

{
  "mcpServers": [
    {
      "name": "opik",
      "command": "npx",
      "args": [
        "-y",
        "opik-mcp"
      ]
    }
  ]
}

Continue uses an array of server objects rather than a map.

~/.config/zed/settings.json

{
  "context_servers": {
    "opik": {
      "command": {
        "path": "npx",
        "args": [
          "-y",
          "opik-mcp"
        ]
      }
    }
  }
}

Add to context_servers. Zed hot-reloads on save.

claude mcp add opik -- npx -y opik-mcp

One-liner. Verify with claude mcp list. Remove with claude mcp remove.

Use Cases

Real-world ways to use opik-mcp

Pull a production trace into your IDE to debug a bad LLM response

👤 LLM app developers ⏱ ~15 min intermediate

When to use: A user reports a wrong answer; the trace is in Opik; you want to inspect it without leaving Cursor.

Prerequisites

Opik API key — comet.com/site > API Keys (or self-hosted admin)

Flow

Find the trace

Search traces in project 'prod-chatbot' where output contains 'I cannot help with that'. Last 24h.✓ Copied

→ Matching trace IDs + timestamps
Inspect

Open trace ID abc123. Show me the full message chain, tools called, and intermediate reasoning.✓ Copied

→ Full trace object
Form hypothesis

Why might the model have refused? Compare this trace to a successful one on the same prompt template.✓ Copied

→ Diff + hypothesis

Outcome: Faster trace-driven debugging without app-switching.

Pitfalls

PII in traces — Configure Opik's redaction before enabling MCP access broadly

Iterate on a prompt template with version tracking

👤 Prompt engineers ⏱ ~25 min advanced

When to use: You're tuning a system prompt and want each version saved to Opik for rollback.

Flow

Pull current version

Get latest version of prompt 'support-agent-system'.✓ Copied

→ Current prompt body
Edit and commit

Propose a change to handle escalations better. Show diff. Commit as a new version with message 'add escalation path'.✓ Copied

→ Diff + new version ID
Eval against dataset

Run this new version against dataset 'support-eval-v1'. Compare pass rate vs previous version.✓ Copied

→ Metric comparison

Outcome: Data-driven prompt changes, version-controlled.

Pitfalls

No guardrails — a regressive prompt becomes prod — Use Opik's experiment framework: don't promote until pass rate ≥ baseline

Generate a weekly LLM app health report

👤 Eng leads, LLM app PMs ⏱ ~30 min intermediate

When to use: You want a Monday-morning digest of cost, latency, error rate, and top failure categories.

Flow

Pull last week's metrics

For project 'prod-chatbot': total traces, total tokens, avg latency p50/p95, error count — over last 7 days.✓ Copied

→ Metrics block
Classify failures

Sample 20 failed traces. Cluster by failure mode. Rank clusters by frequency.✓ Copied

→ Failure taxonomy
Write the digest

Compose a Markdown digest with the metrics and top 3 failure modes, ready for Slack.✓ Copied

→ Shareable report

Outcome: Weekly LLM ops awareness without manual dashboard time.

Pitfalls

Metric drift as your app evolves — Version the report template; compare apples to apples week over week

Combine with: notion

Combinations

Pair with other MCPs for X10 leverage

opik + github

When a prompt regresses, open a GitHub issue with the failing trace

If pass rate drops >5% on 'support-eval-v1' vs last week, create a GitHub issue with the top 3 failing trace IDs.✓ Copied

opik + notion

Publish weekly LLM health digest to Notion

Compose a Monday digest from last week's Opik metrics and create a Notion page in 'LLM Weekly'.✓ Copied

Tools

What this MCP exposes

Tool	Inputs	When to call	Cost
list_projects	workspace_id?	Navigate your workspace	1 API call
list_traces	project, filter?, start?, end?, limit?	Find traces by time range or content	1 API call
get_trace	trace_id	Deep-dive a single trace	1 API call
get_prompt	name, version?	Read a prompt for editing or use in code	1 API call
create_prompt_version	name, template, message?	Commit a new prompt iteration	1 API call
create_dataset	name, items[]	Build an eval dataset	1 API call
get_metrics	project, metric, window	Monitor cost / latency / quality	1 API call

Cost & Limits

What this costs to run

API quota: Opik Cloud has per-plan limits; self-hosted is unlimited
Tokens per call: Trace listings 1k-5k tokens; single traces 500-3000
Monetary: Opik has a generous free tier; paid plans for scale. MCP itself is free (Apache 2.0).
Tip: Use list_traces with a time window; never call without a range on a busy project.

Security

Permissions, secrets, blast radius

Minimum scopes: Opik API key scope the workspace you intend to expose

Credential storage: OPIK_API_KEY env var; HTTP transport uses Authorization: Bearer

Data egress: Traces may contain prompts/responses with PII — understand your Opik region and redaction setup

Never grant: An admin-scope key to a shared dev machine

Trace data is often PII-heavy (user prompts). Redact upstream before Opik ingests.
For self-hosted, the MCP needs network reach to your Opik backend — include it in your VPN/firewall plan.

Troubleshooting

Common errors and fixes

401 Unauthorized (Bearer)

Check OPIK_API_KEY. For self-hosted, also set --apiUrl http://host:5173/api.

Verify: curl -H 'Authorization: Bearer $KEY' $URL/api/v1/workspaces

Empty trace list despite traffic

Wrong project / workspace. List projects first and confirm UUID.

Self-hosted MCP can't reach backend

Use container networking (same docker network) or map --apiUrl to an externally-reachable URL.

Alternatives

opik-mcp vs others

Alternative	When to use it instead	Tradeoff
LangSmith MCP	You use LangSmith for tracing	Different platform; similar capabilities
Langfuse MCP	You use Langfuse (OSS)	Also OSS + self-hostable; different schemas
Arize / Phoenix	You want focus on evals + drift detection	Richer ML-monitoring features; steeper learning curve

More

Resources

📖 Read the official README on GitHub

🐙 Browse open issues

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