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Arize-ai

@arizeai/phoenix-mcp

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by Arize-ai

list-annotation-configs

Retrieve all annotation configurations, including labels, scores, and freeform types, for human or automated annotations.

Instructions

List Phoenix annotation configs.

Annotation configs define the available human or automated labels, scores, and freeform annotation types.

Example usage: Show me all annotation configs

Expected return: Array of annotation config objects.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
Behavior3/5

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

No annotations provided. The description states it returns an array of config objects, implying a read operation, but no details on pagination, rate limits, or any side effects. It is adequate but minimal.

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

Conciseness4/5

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

The description is concise, including a definition, example, and expected return. Every sentence serves a purpose, though it could be slightly more structured.

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

Completeness3/5

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

Given the simple input schema and no output schema, the description covers the basic purpose and return type. However, it lacks details on default behavior, error cases, or sorting, which would aid completeness.

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

Parameters2/5

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

The input schema has one optional parameter 'limit', but the description does not mention or explain it at all. With 0% schema description coverage, the description adds no value for parameter understanding.

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 it lists annotation configs and defines what they are (labels, scores, freeform types). It distinguishes from sibling tools like list-datasets by focusing on annotation configs.

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

Usage Guidelines3/5

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

Includes an example usage and expected return, but no explicit guidance on when to use this tool versus alternatives or when not to use it. The sibling context helps, but the description itself lacks when-to-use/not-to-use info.

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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