get_protein

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

No annotations are provided, so the description carries the full burden. It does establish that this is a read/lookup operation and discloses the returned content categories (including that function predictions carry confidence scores), which is useful behavioral context. It says nothing about error behavior for invalid IDs, database coverage, or whether the lookup is precise vs. fuzzy.

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 purpose sentence, then parameters, then returns — cleanly structured and easy to scan. The Returns block is somewhat redundant given an output schema exists, but it is brief and does not bloat the definition.

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 single-parameter read tool, the description is nearly complete: purpose, exact ID format with examples, and return content. An output schema already exists, so the return prose is a bonus rather than a necessity. The only real omission is guidance distinguishing it from lookalike sibling tools.

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 0%, so the description must compensate, and it does: it states the identifier format 'taxid.seqid' and provides three concrete, varied examples (human P53, human P2RX7, a bacterial gene). This meaningfully exceeds what the bare string schema conveys, though it omits edge cases like case sensitivity or whether the taxid must exist.

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 gives a specific verb and resource ('Look up detailed information for a protein in the eggNOG database') and enumerates the retrieved content (family membership, KEGG KO/GO predictions with confidence, OG list). This distinguishes it from generic lookup siblings, but it does not explicitly say how it differs from close siblings like get_protein_family or get_orthologs, which overlap in scope.

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?

There is no explicit when-to-use guidance, prerequisites, or named alternatives. With nine siblings including get_orthologs, get_protein_family, annotate_protein, and search_eggnog, the agent gets no routing help about which to pick for a given question. Usage is only weakly implied by the lookup phrasing.

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