opik-mcp
MCP ServerFreeModel Context Protocol (MCP) implementation for Opik enabling seamless IDE integration and unified access to prompts, projects, traces, and metrics.
Capabilities9 decomposed
mcp server protocol implementation for opik integration
Medium confidenceImplements the Model Context Protocol (MCP) server specification, exposing Opik's core functionality (prompts, projects, traces, metrics) as standardized MCP resources and tools. Uses TypeScript/Node.js to handle MCP transport layer (stdio, SSE, or WebSocket), request routing, and resource serialization, enabling any MCP-compatible client (Claude Desktop, IDEs, agents) to interact with Opik without custom integrations.
Purpose-built MCP server for Opik's observability platform, exposing prompts, traces, and metrics as first-class MCP resources rather than generic API wrappers. Implements Opik-specific resource schemas and filtering semantics native to the MCP protocol.
Tighter integration than generic HTTP-to-MCP adapters because it understands Opik's domain model (traces, spans, metrics) and exposes them as structured MCP resources with native filtering and pagination.
prompt management and retrieval via mcp resources
Medium confidenceExposes Opik's prompt library as queryable MCP resources, allowing clients to list, search, and retrieve prompts by name, version, or metadata. Implements resource handlers that call Opik's prompt API endpoints, serialize prompt definitions (template, variables, metadata) into MCP resource format, and support filtering/pagination for large prompt libraries.
Exposes Opik's versioned prompt library as MCP resources with native filtering by version, tags, and metadata. Implements lazy-loading and pagination to handle large prompt libraries efficiently without overwhelming the MCP transport.
More efficient than copying prompts into context manually because it provides live access to Opik's prompt library with version control and metadata, reducing context bloat in agent systems.
trace and span data retrieval with filtering
Medium confidenceImplements MCP tools and resources to query Opik's trace database, returning structured trace hierarchies (spans, metadata, metrics) filtered by project, time range, status, or custom attributes. Uses Opik's trace query API to fetch paginated results and serializes nested span structures into MCP-compatible JSON, enabling agents and IDEs to inspect LLM execution history.
Exposes Opik's hierarchical trace structure (traces → spans → metadata) as queryable MCP resources with native filtering by project, time, status, and custom attributes. Handles nested span serialization and pagination to work within MCP message constraints.
More accessible than raw Opik API because it integrates trace querying directly into IDE and agent workflows via MCP, eliminating the need for separate observability dashboards or API clients.
project and workspace enumeration
Medium confidenceProvides MCP resources to list and browse Opik projects and workspaces, returning metadata (name, description, creation date, trace count) for each project. Implements resource handlers that call Opik's project listing API and serialize results into MCP resource format, enabling clients to discover and select projects for trace/prompt queries.
Exposes Opik's project hierarchy as browsable MCP resources, enabling IDE-native project discovery and context switching without requiring users to navigate the web UI or memorize project IDs.
Simpler than managing project context via environment variables or config files because it provides live, interactive project enumeration integrated into the IDE/agent workflow.
metrics and aggregation data exposure
Medium confidenceImplements MCP tools to retrieve aggregated metrics from Opik (latency percentiles, token usage, error rates, cost estimates) grouped by project, span type, or time bucket. Calls Opik's metrics API to compute aggregations and returns structured metric objects with time-series data, enabling agents and IDEs to analyze performance trends without manual dashboard inspection.
Exposes Opik's pre-computed metrics (latency, tokens, cost, errors) as queryable MCP resources with flexible grouping and time-range filtering. Enables real-time metric queries from IDE/agents without requiring separate analytics tools.
More integrated than checking Opik's web dashboard because metrics are available directly in the IDE/agent context, enabling data-driven decisions without context switching.
ide and claude desktop client integration
Medium confidenceImplements MCP server transport handlers (stdio, SSE, WebSocket) and client discovery mechanisms to integrate Opik with Claude Desktop, VS Code, and other MCP-compatible IDEs. Handles MCP protocol handshake, capability negotiation, and resource/tool registration, allowing IDEs to automatically discover and use Opik's prompts, traces, and metrics without manual configuration.
Implements full MCP server lifecycle (handshake, capability negotiation, resource registration) to enable seamless IDE integration without requiring IDE-specific plugins. Supports multiple transport mechanisms (stdio, SSE, WebSocket) for flexibility across different client environments.
More maintainable than IDE-specific plugins because it uses the standard MCP protocol, reducing the need for separate integrations for Claude Desktop, VS Code, and other tools.
tool-based function calling for opik operations
Medium confidenceExposes Opik operations (query traces, retrieve prompts, fetch metrics) as MCP tools with JSON schema definitions, enabling LLM agents to invoke these operations via function calling. Implements tool handlers that parse tool invocation payloads, call corresponding Opik API endpoints, and return structured results, allowing agents to autonomously interact with Opik without explicit API knowledge.
Exposes Opik operations as MCP tools with JSON schema definitions, enabling LLM agents to invoke Opik queries via standard function-calling mechanisms. Implements tool handlers that bridge MCP tool invocations to Opik API calls with proper error handling and result serialization.
More ergonomic for agents than raw API calls because tool schemas provide structured input/output contracts, reducing the need for agents to understand Opik API details.
authentication and credential management
Medium confidenceImplements credential handling for Opik API access, supporting API key-based authentication and optional OAuth token exchange. Stores credentials securely (environment variables, config files, or secure storage) and injects them into all Opik API requests made by the MCP server, ensuring authenticated access without exposing credentials to clients.
Implements server-side credential management where MCP server holds Opik credentials and injects them into API requests, preventing credential exposure to MCP clients. Supports both API key and OAuth authentication methods.
More secure than client-side credential management because credentials are never exposed to MCP clients, reducing the attack surface in multi-user or untrusted environments.
error handling and diagnostic logging
Medium confidenceImplements comprehensive error handling for MCP protocol errors, Opik API failures, and network issues, returning structured error responses to clients with diagnostic information. Includes optional debug logging to help troubleshoot integration issues, with configurable log levels and output destinations (stdout, files, external logging services).
Implements MCP-aware error handling that returns structured error responses to clients while maintaining detailed diagnostic logs for server-side troubleshooting. Supports configurable log levels and multiple output destinations.
More helpful than generic HTTP error codes because it provides MCP-specific error context and diagnostic information, enabling faster troubleshooting of integration issues.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
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@modelcontextprotocol/server-everything
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Best For
- ✓Teams using Opik for LLM observability who want IDE-native access
- ✓Developers building multi-tool AI agents that need unified observability context
- ✓Organizations standardizing on MCP for AI tool integration
- ✓Teams managing large prompt libraries who want IDE-native access
- ✓Developers building agents that need to dynamically load prompts from Opik
- ✓Non-technical users who want to browse prompts without CLI or web UI
- ✓Developers debugging LLM agent behavior in real-time
- ✓Teams analyzing trace data for performance optimization
Known Limitations
- ⚠MCP transport overhead adds ~50-100ms per request compared to direct HTTP calls
- ⚠Requires MCP-compatible client; not compatible with non-MCP tools or older IDE versions
- ⚠No built-in caching of Opik responses — each MCP request hits Opik API directly
- ⚠Limited to read-only operations for most resources; write operations may be restricted by MCP schema
- ⚠Read-only access; prompt creation/editing must happen in Opik UI or API
- ⚠No real-time sync — prompt updates in Opik may take seconds to reflect in MCP
Requirements
Input / Output
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Repository Details
Last commit: Mar 17, 2026
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Model Context Protocol (MCP) implementation for Opik enabling seamless IDE integration and unified access to prompts, projects, traces, and metrics.
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