vector_face_detection

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

With no annotations, the description carries the full behavioral burden. It usefully discloses that the output is a summary rather than raw image data, but it says nothing about side effects, whether this is a live snapshot, or how empty results are returned.

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?

A single, front-loaded sentence with no filler. The core return promise and the important 'no raw image data' exclusion are both present with minimal wording.

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?

For a zero-parameter read-style tool, the description is nearly sufficient: it tells the agent what it returns and what it omits. It lacks detail about the summary structure and does not differentiate from sibling tools, leaving minor ambiguity.

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 schema is an empty object with zero parameters, so the baseline for parameter semantics is 4. Since there are no parameters to document, the description need not add parameter-specific detail.

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 action ('Return a summary') and resource ('currently visible faces'), and explicitly excludes raw image data. It is slightly weakened by not naming or distinguishing the sibling tools like vector_list_visible_faces or vector_find_faces.

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?

There is no guidance about when to use this tool versus the many similar sibling vision tools. The 'no raw image data' qualifier gives an implicit selection clue, but no explicit when/when-not or alternative is provided.

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