Commercial-truck detection corpus profile — image count, label coverage, de-identification posture. Free — evaluate the training corpus before purchasing for detection_or_ocr_model_training.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. The description discloses that the tool is 'Free' and specifies the type of information returned (image count, label coverage, de-identification posture). However, it does not explicitly state that it is a read-only operation or describe any limitations or side effects, leaving some ambiguity for an agent unfamiliar with the domain.
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 two concise sentences with no redundancy. It front-loads the core purpose ('commercial-truck detection corpus profile') and immediately provides key details (image count, label coverage, de-identification posture) and a usage context. Every word contributes.
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 simplicity (0 params, no output schema) and the sibling context, the description adequately covers the tool's purpose and content. It names the specific metrics returned and ties it to a practical scenario (evaluating before purchase). It could be more explicit about the response format, but that is not a major gap for a profile-type tool.
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?
The tool has 0 parameters, so the baseline is 4. The schema fully covers this with an empty properties object, and the description adds value by explaining what the profile contains, which is more useful than any parameter details would be.
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 provides a 'commercial-truck detection corpus profile' with specific content: image count, label coverage, and de-identification posture. The use of 'profile' distinguishes it from raw data tools like get_spotter_cv, and the phrase 'before purchasing' ties it to an evaluative purpose.
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 gives a clear context: 'Free — evaluate the training corpus before purchasing for detection_or_ocr_model_training.' This tells the agent when to use it (when evaluating a corpus prior to purchase). It does not explicitly name alternatives or exclusions, but the context is sufficiently clear for a decision.
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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