get_alphafold_prediction

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

No annotations are present, so the description carries the full burden. It does disclose that the response is a list of model objects and includes a detailed example output, but it omits behavioral details such as what happens when no models are found, potential rate limits, or authentication requirements. The provided response structure adds some transparency but not comprehensive behavioral disclosure.

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 well-structured with headings for arguments, example input, and response structure. The purpose is front-loaded, and the large example output is informative but adds verbosity. Overall, every section earns its place, though the example output could be trimmed without losing essential 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?

Given the lack of annotations, output schema, and low schema coverage, the description compensates by providing a comprehensive response example and field listing, making the return structure clear. It still misses edge-case behavior and the `output_json` parameter documentation, but for a simple retrieval tool, it is largely complete.

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%, so the description must compensate. It explains `qualifier` as a UniProt accession and `sequence_checksum` as a CRC64 checksum, with examples. However, it entirely omits the `output_json` parameter, leaving it undocumented. Thus, it covers two of three parameters but fails to explain the functional effect of `output_json`.

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 states the tool's function: 'retrieves all available AlphaFold models for a specified UniProt accession.' It specifies the resource (AlphaFold models) and the query key (UniProt accession), and this distinctly differentiates it from sibling tools like get_uniprot_summary and get_annotations.

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 explains the two query modes (UniProt accession or CRC64 checksum) but provides no explicit guidance on when to use this tool versus the sibling tools. It does not mention alternatives, exclusions, or appropriate use cases beyond the basic retrieval functionality.

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