UK Data: Protein data (UniProt)

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

The annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint, so the description is free to add value elsewhere, and it does: data source (UniProtKB cross-references to the PDB), result ordering (best resolution first), aggregation behavior (counts by method), the experimental-only scope boundary, and a cost of 1 credit.

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 content is front-loaded: verb, resource, ordering and returned fields come first, then use case, then source and scope exclusions. The attribution phrase 'UniProt Consortium' and the cost suffix are low-value but short, and no sentence is redundant.

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?

There is no output schema, so the description carries the burden of describing return values, and it does so explicitly: method, resolution in angstroms, chain coverage, and per-method counts. Scope boundaries (experimental only, no ligands), ordering and cost are all stated, leaving nothing an agent needs before calling it.

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 100%, with both limit (1-100, default 25) and accession documented, including a pointer to protein_for_gene inside the accession field. The description adds no parameter-level detail beyond the ordering note ('best resolution first'), so the baseline of 3 applies.

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 opens with a specific verb and resource: 'List the experimentally solved 3D structures (Protein Data Bank entries) for a protein'. It also names the exact returned fields (method, resolution in angstroms, chain coverage, counts by method), which separates it cleanly from siblings like protein_sequence or protein_for_gene.

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?

It gives a concrete use case ('is there a crystal structure of X' for structural biology and drug-discovery work) and an explicit exclusion: AlphaFold predicted models and ligand details are not included. It also routes the agent to protein_for_gene for gene-symbol-to-accession resolution, though it does not name any alternative tool for retrieving predicted models.

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

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

Beyond the readOnly/idempotent annotations, the description discloses that it returns only the top reviewed match, that species defaults to human (9606), that TrEMBL/unreviewed entries are excluded, the source (UniProtKB), and a cost of 1 credit. These are meaningful behavioral constraints an agent couldn't infer from annotations alone. Minor gap: no mention of behavior when the symbol has no reviewed match.

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?

Front-loaded with the core mapping and return fields, followed by usage context, constraints, and source/cost. Slightly dense with parenthetical asides (example, cost), but every sentence carries information relevant to invoking the tool correctly.

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 simple 2-parameter lookup with no output schema, the description covers purpose, source, filtering rules, defaults, and return fields. The main omission is failure behavior (no reviewed match, multiple species) and output format specifics, but overall it is sufficient for an agent to call it correctly.

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 coverage is 100%, so both parameters (symbol, organism) are already documented in the schema, including examples and the default 9606. The description reinforces species defaulting and gives a gene-symbol example, but adds no format details beyond the schema. Baseline 3 is appropriate.

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 uses a specific verb and resource ('Map a gene symbol to its expert-reviewed protein') and enumerates the returned fields (accession, entry name, protein name, synonyms, length, function summary). The worked example (TP53 to P04637) makes the transformation unambiguous and distinguishes it from siblings like protein_sequence or protein_3d_structures.

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?

It frames the tool as 'the standard gene-to-protein step for bioinformatics pipelines and coursework,' which gives clear context on when to reach for it. It also states the species scope and that only reviewed Swiss-Prot entries are considered. It doesn't explicitly name an alternative sibling or state when not to use it, so it falls short of a 5.

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

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

Annotations already declare read-only, idempotent and open-world behavior, so the description correctly spends its words elsewhere: it discloses the credit cost, the data source (UniProtKB, UniProt Consortium), and that the returned sequence is the canonical one UniProt displays. The explicit negative scope (no variant, transcript, gene or DNA-derived output) is the kind of behavioral boundary annotations cannot express.

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?

Front-loaded with the primary value (sequence ready for BLAST/alignment), then input form, source, exclusions, and cost in a single dense closing clause. No sentence is redundant; even the parenthetical cost is actionable.

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?

There is no output schema, yet the description covers the return format (plain text and FASTA plus length, organism, gene, evidence level), the input forms, the data source, and the limits. That is everything an agent needs to call it correctly and interpret the result.

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 coverage is 100% and the schema already carries the accession example (P04637), the isoform form (P04637-2) and the protein_for_gene hint. The description restates those examples without adding format rules, edge cases, or error behavior, so it earns only the baseline.

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 ('Get a protein's amino-acid sequence') plus the output form (plain text and FASTA) and provenance (UniProtKB). It clearly separates itself from protein_3d_structures and protein_for_gene by naming what it does not do (no DNA translation, no variants, no gene/mRNA sequences).

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?

Gives an explicit routing rule to a sibling ('use protein_for_gene to turn a gene symbol into an accession', repeated in the schema) and enumerates the adjacent tasks it should not be used for. An agent can decide correctness of invocation from the description alone.

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