classify_modulation

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

With no annotations, the description must fully disclose behavior. It mentions being rule-based and using specific features, but omits crucial details like required input format, output structure, or limitations. The tool's reliance on a loaded signal is only hinted at by the input schema.

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, efficient sentence that covers the core purpose. However, it could be restructured to front-load the most important information (e.g., the classification action) and optionally include parameter guidance.

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?

Despite having an output schema, the description fails to explain prerequisites (e.g., need to load an IQ file first), acceptable values for 'source', or what the output classification looks like. The tool's context requires more completeness for effective use.

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?

Schema description coverage is 0%, and the description does not explain the 'source' parameter or its values. The description adds no meaning beyond the schema, leaving the agent without guidance on how to specify the input signal.

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 identifies the tool as a modulation classifier using a specific algorithm and features. It distinguishes itself from sibling tools like compute_fft or render_spectrogram, which are not classifiers.

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 implies usage on a previously loaded signal via the 'source' parameter defaulting to 'last_loaded', but it does not explicitly state prerequisites or when to choose this over alternatives. No when-not-to-use guidance is given.

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