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Arize-ai

@arizeai/phoenix-mcp

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by Arize-ai

get-span-annotations

Fetch annotations for span IDs, including scores and labels, to analyze and categorize spans.

Instructions

Get span annotations for a list of span IDs.

Span annotations provide additional metadata, scores, or labels for spans. They can be created by humans, LLMs, or code and help in analyzing and categorizing spans.

Example usage: Get annotations for spans ["span1", "span2"] from project "my-project" Get quality score annotations for span "span1" from project "my-project"

Expected return: Object containing annotations array and optional next cursor for pagination. Example: { "annotations": [ { "id": "annotation123", "span_id": "span1", "name": "quality_score", "result": { "label": "good", "score": 0.95, "explanation": null }, "annotator_kind": "LLM", "metadata": { "model": "gpt-4" } } ], "nextCursor": "cursor_for_pagination" }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
cursorNo
span_idsYes
project_identifierNo
exclude_annotation_namesNo
include_annotation_namesNo
Behavior4/5

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

With no annotations, the description carries full burden. It reveals that the tool returns an object with annotations array and optional next cursor for pagination, and explains the nature of annotations. It implies read-only behavior without contradictions.

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 front-loaded with the main purpose and provides examples and return structure. It is slightly verbose but each sentence adds value; no wasted words.

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

Completeness2/5

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

Given 6 parameters, no output schema, and no annotations, the description is incomplete. It does not explain filtering options, pagination details beyond next cursor, or error behavior. The example return is helpful but not exhaustive.

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?

Schema description coverage is 0%, so the description must compensate. It mentions span_ids and project in examples but does not explain parameters like limit, cursor, exclude/include_annotation_names. The examples are insufficient to define semantics for all 6 parameters.

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 verb 'Get' and resource 'span annotations' with the required input 'list of span IDs'. It explains what span annotations are and provides examples, effectively distinguishing it from sibling tools like list-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 Guidelines4/5

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

The description includes example usage and expected return, giving context for when to use the tool. However, it lacks explicit guidance on when not to use it or alternatives, though the specificity of span IDs makes it 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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