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stamp_description

Read-only

Validate a client-produced scene reading and stamp provenance.

This is the interpretation boundary for the no-key kit path: you read the
picture ``describe_scene`` returned, produce
``{summary, observed_features, confidence, caveats}``, and pass it here.
The library checks the shape, stamps CC-BY and the AI-interpretation
note (the model never sets those), and — when ``image`` is the record
from the kit — appends a deterministic caveat if the picture was a
smaller baked preview. Until this returns, the reading is unvalidated
prose, not pipeline output.

Input Schema

NameRequiredDescriptionDefault
assetNoGEC
imageNo
modelNo
item_idNo
readingYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Adds substantial behavior beyond readOnlyHint: it discloses shape validation, that CC-BY and the AI-interpretation note are stamped by the library rather than by the model, and that a deterministic caveat is appended when the image is a smaller baked preview. The 'stamping' language sits in mild tension with readOnlyHint, but the text implies an enriched return value rather than persisted mutation, so it is not a contradiction.

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?

Front-loads the verb-plus-resource statement, then layers the workflow and side effects in a short paragraph. A few phrases ('the interpretation boundary for the no-key kit path') are jargon-heavy, but no sentence is redundant.

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 an output schema present, return values need not be explained, and the description covers the input contract, the validation behavior and the provenance rules. The remaining gap is the undocumented asset/item_id parameters, which the description never mentions.

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 carries the burden. It fully documents the required `reading` object's expected keys and meaningfully describes the conditional `image` argument, but leaves `asset`, `model` (only obliquely referenced) and `item_id` unexplained.

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?

States a specific verb and resource ('Validate a client-produced scene reading and stamp provenance') and frames the tool as the interpretation boundary that consumes what ``describe_scene`` produced. An agent can distinguish it from describe_scene, though it does not explicitly distinguish itself from stamp_narration, its nearest sibling.

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?

Gives a clear call order: read the picture describe_scene returned, formulate {summary, observed_features, confidence, caveats}, then pass it here. It also states the consequence of skipping it ('until this returns, the reading is unvalidated prose'). It stops short of naming exclusions or contrasting with stamp_narration.

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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