sensor_audio

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

No annotations provided, so description carries full burden. It states 'encoding' but does not disclose whether the operation is read-only, destructive, or has side effects. No mention of permissions, rate limits, or impact on system state.

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?

Description is extremely concise (one line), which is efficient but lacks structure. It front-loads the core purpose but omits any supplementary information. Not every detail is included, so it is not optimally informative.

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 tool with a nested input object and multiple parameters, the description is insufficient. No output schema, no explanation of what the tool returns (latent representation format), and no handling of edge cases (e.g., real audio vs simulation). The schema covers parameters, but the overall usage context is lacking.

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?

Input schema coverage is 100% with detailed descriptions for each parameter (e.g., sampleRate, durationMs, simulate). The tool description adds no additional meaning beyond the schema. Baseline 3 is appropriate since schema already documents parameters well.

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?

Description 'A-JEPA Audio Encoding (Waveform → Mel → Latent)' clearly indicates the tool converts audio waveform to latent representation via mel spectrogram. It distinguishes from sibling sensor tools by specifying audio processing. However, it assumes familiarity with A-JEPA jargon, slightly reducing clarity for general AI agents.

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 on when to use this tool versus alternatives like sensor_visual or tcai_*. The description does not specify context, prerequisites, or when not to use it. Agent must infer 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.