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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds genuinely non-derivable context: every row is assertion_class 'inferred' and is a hypothesis, not a measured result. It does not mention pagination or result volume, but the epistemic caveat is the higher-value 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?

Three sentences, fully front-loaded: what the tool returns, the critical inferred/hypothesis caveat, then the presentation rule plus the alternative. No sentence is filler; the caveat is placed before the alternative deliberately.

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?

No output schema, so the description carries return-value context — it discloses that rows are inferred assertions. It pairs well with the read-only annotations and the required node_id. Pagination semantics are left to the schema's defaults/ranges, a minor gap for a 4-parameter list tool.

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 50%: node_id and predicate are documented inline ('A node id from search_nodes, e.g. CHEBI:16919', 'e.g. transports'), while limit/offset rely on defaults. The description's category list (transporters, enzymes, pathways, expression) loosely hints at predicate values, but it does not add format or syntax beyond the schema, 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?

States a specific verb+resource — what public reference databases (Reactome, UniProt, Rhea, ChEBI) record about a node — and enumerates the content types (transporters, enzymes, pathways, expression). It explicitly distinguishes itself from the sibling get_findings, so an agent can route without opening either schema.

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 explicit when-not guidance ('never as effects' / 'present these as possible mechanisms') and names the alternative tool by name: 'For effects, use get_findings.' This is exactly the when-to-use/when-not/alternative triad.

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