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@arizeai/phoenix-mcp

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by Arize-ai

phoenix-support

Get expert guidance on using and integrating Arize Phoenix for AI application tracing, dataset management, experiments, prompts, evals, and annotations.

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 mentions expected return ('expert guidance') but lacks details about limitations (e.g., whether it's a knowledge base or live agent) or side effects. Adequate but could be more transparent.

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?

Concise two-part structure: bullet points listing topics and a clear usage statement. Every sentence adds value, no fluff.

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

Completeness5/5

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

For a simple help tool with one parameter, the description covers purpose, usage context, and expected return. No output schema needed as return is described adequately.

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

Parameters4/5

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

Schema coverage is 100% with a clear parameter description. The tool description adds context about the scope of questions (topics listed), enhancing understanding beyond the schema.

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 provides help with Phoenix and OpenInference, listing specific topics like tracing, datasets, experiments, and evals. It distinguishes from sibling tools which are all data operations, making this the only support tool.

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?

Explicitly states when to use this tool: 'when you need assistance with Phoenix features, troubleshooting, or best practices.' It doesn't explicitly name alternatives, but siblings are all specific operations, so the context is clear.

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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