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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the method ('diagnostic fragment rules') but says nothing about authentication (the api_key parameter), input format expectations, limitations, or what happens if no class is matched. This is a significant gap for a prediction tool with zero structured behavioral hints.

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 front-loaded sentence with no wasted words. Its brevity is appropriate in form, though it is arguably too sparse given the tool's complexity – but the conciseness dimension itself is well satisfied.

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 undocumented parameters, no annotations, and a non-trivial prediction task, the description is far too thin. It does not explain input formats, required api_key semantics, or limitations. The presence of an output schema reduces the need to describe return values, but the input side remains largely unexplained.

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 schema description coverage is 0% and the description does not mention any of the three parameters (peaks, precursor_mz, api_key). Critical details such as the expected format of 'peaks', whether precursor_mz is in Da or m/z, and the purpose of api_key are entirely absent. The description adds no meaning beyond the bare parameter names.

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 states a specific verb ('Predict'), resource ('compound class'), and input ('MS/MS spectrum'), plus a method ('diagnostic fragment rules'). It distinguishes itself from siblings like covams_identify or covams_fragment_annotate by focusing on classification rather than identification or annotation, though it does not name an alternative explicitly.

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 guidance on when to use this tool versus siblings such as covams_identify or covams_analyze_unknown. It implies the input is an MS/MS spectrum but provides no context about appropriate use cases, prerequisites, or exclusions.

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