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Image / OCR / Vision (placeholder)

image-ocr-vision-placeholder

Image / OCR / Vision (placeholder) — PARTIALLY SUPERSEDED. Live image generation now exists: use grok-image or grok-image-hq for image creation. OCR and broader vision analysis still do not have a real backing capability yet, so this placeholder remains an honest signpost and is not Shop-Graded. (2 MESH/call, a tool · media)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesCapability-specific payload, e.g. agent-brain: {think:'...'}; agent-memory: {action:'store'|'recall', content|query}

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations are sparse (readOnlyHint false, openWorldHint true, destructiveHint false). The description adds cost (2 MESH/call) and 'not Shop-Graded' status, which is useful, but it does not disclose what happens when the tool is invoked (e.g., returns a placeholder message, side effects). The honest placeholder framing partially covers this, but the actual call behavior remains unspecified.

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The description is concise and front-loaded with the superseded status and alternatives. The phrase 'a tool · media' is slightly cryptic, but overall the text is efficient and well-structured.

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?

For a placeholder tool, the description gives context about its status and alternatives, but it fails to provide any guidance on how to invoke it (input format) or what response to expect. With no output schema and a confusing input schema, an agent cannot successfully use this tool without external knowledge.

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

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has one required parameter 'input' whose description is generic and appears copy-pasted from other tools (agent-brain, agent-memory). It does not explain what payload this placeholder expects, and the tool description offers no clarification. With 100% schema coverage but misleading content, the agent cannot determine correct usage.

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 is a placeholder for Image/OCR/Vision, explicitly notes it is partially superseded, and names the live alternatives (grok-image, grok-image-hq) for image generation. This distinguishes it from siblings and leaves no ambiguity about what the tool represents.

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 explicitly tells the agent to use grok-image or grok-image-hq for image creation, and notes that OCR/vision analysis still lacks a real backing capability, so the placeholder remains an honest signpost. This is clear when-to-use/when-not-to-use guidance with named alternatives.

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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