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list_evaluators

Retrieve all workspace Evaluators with each current version's ID and declared Metrics to select which Evaluators an Experiment runs.

Instructions

Lists the Evaluators in the workspace.

An Evaluator is a versioned Python function that judges a Session and reports named Metrics. Each entry carries its newest version's id and the Metrics that version declares (name, shape, grain and, for a closed Label, its vocabulary), but not the source. Evaluators belong to the workspace, not to a pipeline; an Experiment picks which ones run on its pipeline. :returns: The Evaluators, or an error message.

The output is automatically stored and can be referenced in other functions. Returns a formatted preview with an object ID (e.g., @obj_123). Use the object store tools in combination with the object ID to view nested properties of the object. Use the returned object ID to pass this result to other functions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.29

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does disclose a genuinely useful behavioral trait: each entry carries the newest version's id and declared Metrics but explicitly NOT the source. It also discloses the side effect that output is auto-stored and returned as an object ID preview. It omits any note on ordering, pagination, or size of the result set.

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?

It is front-loaded with the purpose and the object definition before the operational detail, and most sentences earn their place. The trailing object-store instructions are somewhat repetitive (two sentences both say to reuse the returned object ID), but they are structured rather than padded.

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?

With no output schema and no annotations, the description does the heavy lifting by describing the shape of each returned entry (version id, metric name/shape/grain/label vocabulary) and how the result is surfaced as an object ID. It is close to complete for a no-parameter list tool, lacking only scope details such as ordering and pagination.

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?

The tool takes zero parameters, so per the rubric the baseline is 4. There is nothing parameter-wise for the description to compensate for.

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

Purpose4/5

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

The first sentence gives a specific verb+resource ("Lists the Evaluators in the workspace") and the following definition of what an Evaluator is clarifies the domain object. It does not explicitly differentiate itself from siblings like get_evaluator or try_evaluator, but the plural/list framing makes the distinction inferable.

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?

Usage is only implied: it explains that Evaluators belong to the workspace rather than a pipeline, and that Experiments select which run, which gives context for when the list is relevant. It never states when to call this versus get_evaluator or try_evaluator, nor any prerequisites.

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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