Model Provenance Scan

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

The description adds no behavioral details beyond the annotations, which already indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description simply restates the purpose, so it does not contradict annotations but also does not enrich the agent's understanding of side effects or constraints.

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 a single sentence of 8 words, with no redundancy or filler. It is front-loaded and immediately clear, earning a top score for conciseness.

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?

The tool is simple (2 parameters, output schema present, no nested objects), and the description covers its core function. While it could mention that results are returned via the output schema or that it queries external sources, the combination of annotations and schema provides sufficient context for an agent to use it correctly.

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?

With 100% schema description coverage, the input schema already documents both parameters (model_id and source) with clear examples and default values. The description adds no further semantic information, so it meets the baseline expectation for parameter clarity.

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's purpose: 'Check ML model provenance and supply chain metadata.' It uses a specific verb ('Check') and resource ('ML model provenance'), and the tool name itself differentiates it from sibling tools like model_file_scan or license_compliance_scan, which focus on other aspects.

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 provides no guidance on when to use this tool versus alternatives, nor does it mention prerequisites or limitations. It only states what the tool does, leaving the agent to infer usage context from the name alone.

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