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devtune_list_visibility_responses

List the latest 20 successful AI executions in this project, optionally filtered by prompt or platform. Pass an executionId to devtune_get_visibility_response to inspect retained answer and ad evidence. This is an operational listing, not a measurement sample; use visibility summary for rates.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
platformNo
promptIdNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are present, so the description carries the behavioral burden. It discloses the 20-item limit, the 'successful' filter, project scoping, and the operational vs. measurement distinction. It does not explicitly state whether the call is read-only or describe pagination, but for a listing tool the key behavioral traits are covered.

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?

Three sentences, each earning its place: the core operation, the follow-up path, and the crucial distinction from measurement tools. The most important information is front-loaded with no filler.

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 only two optional simple parameters and no output schema, the description covers the essential context: what is listed, filter options, the 20-item limit, and how to proceed with a returned executionId. It does not describe the exact output fields, but the pointer to executionId gives enough operational grounding for correct invocation.

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

Parameters3/5

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 adds that platform and promptId are optional filters and explains their role ('optionally filtered by prompt or platform'). However, it doesn't precisely map 'prompt' to the promptId parameter or clarify whether filters combine, leaving some semantic ambiguity.

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 opens with a specific action: 'List the latest 20 successful AI executions in this project', naming the resource, scope, count, and success filter. It also distinguishes itself from related tools by explicitly contrasting with devtune_get_visibility_response and visibility summary, so an agent can tell it apart from siblings without opening schemas.

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

Usage Guidelines5/5

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

It gives explicit routing guidance: pass an executionId to devtune_get_visibility_response for deeper inspection, and use visibility summary for rates. The line 'This is an operational listing, not a measurement sample' clearly states when this tool is appropriate and when it is not.

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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