sensor_visual

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

With no annotations and only a brief label, the description gives no insight into side effects, return values, or state changes. The agent cannot anticipate what the tool actually does beyond the name, which is a significant transparency gap.

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 concise phrase with no redundant words, making it easy to parse. However, it is so brief that it borders on under-specification rather than being a well-structured description.

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 nested parameter structure and many sibling sensor tools, the description is far too sparse. It does not explain the V-JEPA 2 model, how to handle image vs. video inputs, or what output to expect, leaving the agent to rely on the schema and name.

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 already describes all parameters (input object and videoFrames) with 100% coverage, so the baseline is 3. The description adds no extra parameter context, but the schema adequately documents the parameters.

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 identifies the tool as 'V-JEPA 2 Visual Encoding' for images/videos, indicating it handles visual data. However, it does not explicitly state what 'encoding' produces or how it differs from sensor_process, so it lacks precise verb-object clarity.

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?

No guidance is provided on when to use this tool versus alternatives like sensor_audio or sensor_process. The name suggests visual input, but there is no explicit context, exclusions, or recommended scenarios.

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