convolve

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

With no annotations, the description carries the full behavioral burden. Referencing numpy.convolve implicitly conveys standard convolution semantics, but the description says nothing about how the 'mode' affects output length, performance characteristics, or what is returned.

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?

A single front-loaded sentence with no filler. It is appropriately sized for a one-line operation, though brevity here shades into under-specification rather than elegance.

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 3-parameter tool with no annotations and no output schema, the description is too thin: it omits the accepted input forms, the meaning of 'mode', and any hint about the returned result. An agent could not reliably invoke it beyond the most literal case.

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 there are three parameters, so the description must compensate. It explains none of them: 'mode' is undocumented (no full/same/valid values), and the fact that signal1/signal2 accept either arrays or strings (implying identifiers or paths) is left entirely unexplained.

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 gives a specific verb ('Compute') and resource ('discrete convolution of two signals') and anchors it to numpy.convolve, so the agent knows the exact operation and library semantics. It does not, however, differentiate itself from adjacent siblings such as filter_signal or fft_analysis.

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?

There is no when-to-use guidance, no mention of alternatives (e.g., using FFT-based convolution via fft_analysis for large signals), and no stated preconditions. The agent must infer applicability 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.