DevOps & Infra

phoenix-tracing - Claude MCP Skill

OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.

SEO Guide: Enhance your AI agent with the phoenix-tracing tool. This Model Context Protocol (MCP) server allows Claude Desktop and other LLMs to openinference semantic conventions and instrumentation for phoenix ai observability. use when implem... Download and configure this skill to unlock new capabilities for your AI workflow.

🌟2947 stars • 749 forks
📥0 downloads

Documentation

SKILL.md
# Phoenix Tracing

Comprehensive guide for instrumenting LLM applications with OpenInference tracing in Phoenix. Contains rule files covering setup, instrumentation, span types, and production deployment.

## When to Apply

Reference these guidelines when:

- Setting up Phoenix tracing (Python or TypeScript)
- Creating custom spans for LLM operations
- Adding attributes following OpenInference conventions
- Deploying tracing to production
- Querying and analyzing trace data

## Rule Categories

| Priority | Category        | Description                    | Prefix                     |
| -------- | --------------- | ------------------------------ | -------------------------- |
| 1        | Setup           | Installation and configuration | `setup-*`                  |
| 2        | Instrumentation | Auto and manual tracing        | `instrumentation-*`        |
| 3        | Span Types      | 9 span kinds with attributes   | `span-*`                   |
| 4        | Organization    | Projects and sessions          | `projects-*`, `sessions-*` |
| 5        | Enrichment      | Custom metadata                | `metadata-*`               |
| 6        | Production      | Batch processing, masking      | `production-*`             |
| 7        | Feedback        | Annotations and evaluation     | `annotations-*`            |

## Quick Reference

### 1. Setup (START HERE)

- `setup-python` - Install arize-phoenix-otel, configure endpoint
- `setup-typescript` - Install @arizeai/phoenix-otel, configure endpoint

### 2. Instrumentation

- `instrumentation-auto-python` - Auto-instrument OpenAI, LangChain, etc.
- `instrumentation-auto-typescript` - Auto-instrument supported frameworks
- `instrumentation-manual-python` - Custom spans with decorators
- `instrumentation-manual-typescript` - Custom spans with wrappers

### 3. Span Types (with full attribute schemas)

- `span-llm` - LLM API calls (model, tokens, messages, cost)
- `span-chain` - Multi-step workflows and pipelines
- `span-retriever` - Document retrieval (documents, scores)
- `span-tool` - Function/API calls (name, parameters)
- `span-agent` - Multi-step reasoning agents
- `span-embedding` - Vector generation
- `span-reranker` - Document re-ranking
- `span-guardrail` - Safety checks
- `span-evaluator` - LLM evaluation

### 4. Organization

- `projects-python` / `projects-typescript` - Group traces by application
- `sessions-python` / `sessions-typescript` - Track conversations

### 5. Enrichment

- `metadata-python` / `metadata-typescript` - Custom attributes

### 6. Production (CRITICAL)

- `production-python` / `production-typescript` - Batch processing, PII masking

### 7. Feedback

- `annotations-overview` - Feedback concepts
- `annotations-python` / `annotations-typescript` - Add feedback to spans

### Reference Files

- `fundamentals-overview` - Traces, spans, attributes basics
- `fundamentals-required-attributes` - Required fields per span type
- `fundamentals-universal-attributes` - Common attributes (user.id, session.id)
- `fundamentals-flattening` - JSON flattening rules
- `attributes-messages` - Chat message format
- `attributes-metadata` - Custom metadata schema
- `attributes-graph` - Agent workflow attributes
- `attributes-exceptions` - Error tracking

## Common Workflows

- **Quick Start**: `setup-{lang}` → `instrumentation-auto-{lang}` → Check Phoenix
- **Custom Spans**: `setup-{lang}` → `instrumentation-manual-{lang}` → `span-{type}`
- **Session Tracking**: `sessions-{lang}` for conversation grouping patterns
- **Production**: `production-{lang}` for batching, masking, and deployment

## How to Use This Skill

**Navigation Patterns:**

```bash
# By category prefix
rules/setup-*              # Installation and configuration
rules/instrumentation-*    # Auto and manual tracing
rules/span-*               # Span type specifications
rules/sessions-*           # Session tracking
rules/production-*         # Production deployment
rules/fundamentals-*       # Core concepts
rules/attributes-*         # Attribute specifications

# By language
rules/*-python.md          # Python implementations
rules/*-typescript.md      # TypeScript implementations
```

**Reading Order:**
1. Start with `setup-{lang}` for your language
2. Choose `instrumentation-auto-{lang}` OR `instrumentation-manual-{lang}`
3. Reference `span-{type}` files as needed for specific operations
4. See `fundamentals-*` files for attribute specifications

## References

**Phoenix Documentation:**

- [Phoenix Documentation](https://docs.arize.com/phoenix)
- [OpenInference Spec](https://github.com/Arize-ai/openinference/tree/main/spec)

**Python API Documentation:**

- [Python OTEL Package](https://arize-phoenix.readthedocs.io/projects/otel/en/latest/) - `arize-phoenix-otel` API reference
- [Python Client Package](https://arize-phoenix.readthedocs.io/projects/client/en/latest/) - `arize-phoenix-client` API reference

**TypeScript API Documentation:**

- [TypeScript Packages](https://arize-ai.github.io/phoenix/) - `@arizeai/phoenix-otel`, `@arizeai/phoenix-client`, and other TypeScript packages

Signals

Avg rating0.0
Reviews0
Favorites0

Information

Repository
Arize-ai/phoenix
Author
Arize-ai
Last Sync
3/12/2026
Repo Updated
3/12/2026
Created
1/26/2026

Reviews (0)

No reviews yet. Be the first to review this skill!