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Arize-ai

@arizeai/phoenix-mcp

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by Arize-ai

phoenix-support

Get expert help with Arize Phoenix and OpenInference. Troubleshoot issues, learn best practices for tracing, datasets, evals, and prompt management.

Instructions

Get help with Phoenix and OpenInference.

  • Tracing AI applications via OpenInference and OpenTelemetry

  • Phoenix datasets, experiments, and prompt management

  • Phoenix evals and annotations

Use this tool when you need assistance with Phoenix features, troubleshooting, or best practices.

Expected return: Expert guidance about how to use and integrate Phoenix

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesYour question about Arize Phoenix, OpenInference, or related topics
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses the expected return type ('Expert guidance') but does not mention any behavioral traits like whether it is read-only, authentication needs, rate limits, or how the response is generated (e.g., static knowledge base vs. live query). For a support tool, the lack of deeper behavioral context is acceptable but still minimal.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured: purpose first, then bullet points of supported areas, usage guidance, and expected return. Every sentence adds value with no repetition or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that this is a support/help tool with one parameter and no output schema, the description adequately covers what the tool does and when to use it. It could optionally mention response format or expected latency, but as is it is reasonably complete for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has one parameter 'query' with description 'Your question about Arize Phoenix, OpenInference, or related topics'. Schema description coverage is 100%. The description adds no extra meaning beyond the schema, so a baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose ('Get help with Phoenix and OpenInference') and lists specific areas of assistance (tracing, datasets, experiments, prompt management, evals, annotations). It distinguishes from siblings which are more specific data retrieval tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use the tool: 'Use this tool when you need assistance with Phoenix features, troubleshooting, or best practices.' It does not provide explicit when-not-to-use or alternatives, but the context of siblings implies the boundary.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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