sensor_fuse

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

With no annotations, the description must disclose behavior but only offers an abstract phrase. It does not indicate whether the operation is read-only, modifies state, requires permissions, or has side effects. The term 'attention fusion' hints at a machine learning process, but details are missing.

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 extremely short (three words), which is under-specification rather than conciseness. It does not provide enough information for an agent to understand or use the tool effectively.

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 three parameters, no annotations, and no output schema, the description is grossly incomplete. It fails to explain the fusion process, output format, constraints, or integration with sibling tools like sensor_visual or tcai_*.

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 three boolean parameters (includeVisual, includeAudio, includeOlfactory) are self-explanatory, but the description adds no additional meaning. With 0% schema description coverage, the description should at least confirm that these control which modalities to fuse, which it does not.

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 'Cross-Modal Attention Fusion' is a noun phrase lacking a verb, making it unclear what action the tool performs. It does not specify whether it combines, transforms, or analyzes sensor data, and fails to distinguish it from siblings like sensor_visual or sensor_audio.

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 such as sensor_visual, sensor_audio, or tcai_capability_model. The description omits context, prerequisites, or conditions for use.

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