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

No annotations exist, so the description carries the full burden, and it does well: it discloses that the file is taken by reference (signed URL or local path), that ALL features are returned rather than one spectrum, and that the backend silently falls back from pyOpenMS FeatureFinderMetabo to a scipy binning method with method_used reporting which ran. It omits runtime/compute cost, file size limits, and whether api_key is required for this call.

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?

Three sentences, front-loaded with the action and payload, then the differentiator, then the implementation detail. No filler and every sentence adds decision-relevant information.

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?

An output schema exists, so return structure need not be explained, and the description usefully flags the method_used field. The remaining gap is the undocumented numeric tuning parameters, which matter for a feature-detection tool where sensitivity depends on them.

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% across 5 parameters, so the description must compensate and it only explains 'path' (signed URL vs local path). The three tuning knobs (noise_threshold, min_trace_length, mass_trace_mz_tolerance) and api_key get no guidance at all, leaving an agent unable to reason about units or sensible ranges.

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?

States a specific verb and resource ('Detect LC-MS features') plus the exact payload (m/z + retention time + intensity + charge) and input format (whole mzML/mzXML run). It explicitly distinguishes itself from the peak-list MS siblings and identifies its niche ('entry point for untargeted metabolomics'), so an agent can tell it apart from covams_process_spectrum or covams_analyze_unknown.

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?

Strong contextual routing: it says what it is for (untargeted metabolomics entry point) and contrasts itself against 'the peak-list MS tools' that collapse to one spectrum. It stops short of naming the specific alternative tool an agent should call instead when it already has a peak list.

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