qr_data_extract

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

With no annotations, the description carries full behavioral disclosure burden. It explains the extraction process (grid identification, exclusion of function patterns) but does not detail output format, error handling, or side effects. It is adequate but not comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, front-loaded with the primary action, and contains no filler. Every sentence adds value: purpose, process detail, and use case. It is concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of output schema, the description should clarify the return format (e.g., array of pixel values). It mentions 'raw pixel values' but is vague. For a specialized tool, this is somewhat incomplete for an AI agent to fully understand the output.

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

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

Schema coverage is 100% with descriptive parameter texts for file_path and threshold. The tool description adds no extra meaning beyond what the schema already provides, so baseline score of 3 is appropriate.

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

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

The description clearly states the tool extracts QR data region pixels, identifies the grid, excludes function patterns, and returns raw pixel values. This specific verb-resource combination distinguishes it from sibling tools like qr_detect or qr_structure, which have different purposes.

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

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

The description implies usage for manual analysis of modified data regions, but does not explicitly state when to use this tool over siblings. It provides a use case but lacks direct comparisons or when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.