Convert audio waveforms to Mel spectrograms and latent representations for neuromorphic simulation. Configure source, channels, sample rate, frequency, and duration; simulate inputs to test the pipeline.
A-JEPA Audio Encoding (Waveform → Mel → Latent)
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Audio parameters |
Changes observed during successful MCP inspections.
v3.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations present, the description carries the burden of behavioral disclosure. It does reveal the transformation stages (Waveform → Mel → Latent), which is useful, but it does not disclose whether audio capture is real or simulated, whether the operation has side effects, whether it requires a microphone, or what the returned latent representation looks like.
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 concise and front-loads the core purpose and pipeline with no filler. It is more of a title than a full explanatory sentence, but every element earns its place and there is no redundancy.
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, no output schema, and no annotations, the description is too thin to be fully actionable. It explains the processing pipeline but omits critical context such as expected output format, capture behavior, simulation behavior, and relationship to sibling sensor or encoding tools.
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 input schema is well-structured with defaults, ranges, and descriptions for some parameters, so the schema carries most of the parameter semantics. The tool description does not add any explanation of how parameters like source, channels, durationMs, or sampleRate affect the encoding, but the high schema coverage keeps this at the baseline score.
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 a specific operation (A-JEPA audio encoding) and a clear processing pipeline from waveform to mel to latent. It clearly marks this as the audio sensor tool among siblings such as sensor_visual and sensor_olfactory, though it does not explicitly state what the encoded latent is used for.
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 no explicit guidance on when to use this tool versus alternatives like sensor_fuse, sensor_process, or wm_encode. Usage is only implied by the tool name and the phrase 'Audio Encoding', with no conditions, exclusions, or references to other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.