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Arize-ai

@arizeai/phoenix-mcp

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by Arize-ai

get-experiment-by-id

Retrieve experiment metadata, run results, and evaluator annotations by ID to review performance and scoring.

Instructions

Get an experiment by its ID.

The tool returns experiment metadata in the first content block and a JSON object with the experiment data in the second. The experiment data contains both the results of each experiment run and the annotations made by an evaluator to score or label the results, for example, comparing the output of an experiment run to the expected output from the dataset example.

Example usage: Show me the experiment results for experiment RXhwZXJpbWVudDo4

Expected return: Object containing experiment metadata and results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
experiment_idYes
Behavior4/5

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 transparently describes the response structure (two content blocks: metadata and JSON with results/annotations). This is sufficient for a read operation, but lacks details on potential side effects or required permissions.

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 well-structured with a clear statement of purpose, expected return, and an example. It is slightly verbose due to the example and expected return block, but every sentence adds value. No wasted words.

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 the tool's simplicity (1 parameter, no output schema, no annotations), the description adequately covers what, how, and what is returned. It includes an example and explains the response contents. Missing are error handling and prerequisites, but overall it is complete enough for a fetch operation.

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 sole parameter 'experiment_id' is documented only in the schema with no description. The tool's description adds no semantics beyond 'by its ID', leaving the agent to guess the format or origin of the ID. Schema coverage is 0%, so the description should compensate but does not.

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 action: 'Get an experiment by its ID', specifying the resource (experiment) and the identifier (ID). It distinguishes itself from sibling tools like list-experiments-for-dataset by focusing on a single experiment retrieval.

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?

The description provides an example usage but does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusions or prerequisites. Sibling tools exist for listing experiments, but no guidance is given on choosing between them.

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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