uniprot_get_alphafold_confidence

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

Discloses that the tool calls a third-party API (https://alphafold.ebi.ac.uk) and references privacy implications in PRIVACY.md. The annotations (readOnlyHint, openWorldHint) are consistent. Description adds significant behavioral context beyond annotations.

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?

Compact 5-sentence description with clear front-loading: purpose, output details, usage guidance, and third-party disclosure. Every sentence serves a purpose without redundancy.

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 presence of an output schema, the description sufficiently covers all relevant aspects: purpose, output content, usage scenarios, and third-party sourcing. No gaps remain for an AI agent to make an informed invocation decision.

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 already describes both parameters clearly (100% coverage). The description adds value by explaining the output bands and their meaning, which helps in understanding the response_format parameter's utility. However, it doesn't add new semantic information to the parameters themselves.

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 it fetches per-residue confidence (pLDDT) summary for an entry's AlphaFold model, not just its existence. It distinguishes from the sibling tool uniprot_resolve_alphafold by specifying that the latter only provides model ID and viewer link.

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?

Explicitly advises to call uniprot_resolve_alphafold first if only the model ID and viewer link are needed. Provides concrete interpretation of the output bands (e.g., '95% very high' is publication-grade) to guide decision-making.

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