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

Annotations already declare readOnlyHint, openWorldHint=false and destructiveHint=false, so the safety profile is covered. The description adds genuinely non-obvious behavioral context: results come from a real algorithm engine rather than LLM generation, and it names the kind of output returned. It does not discuss input length limits or handling of ambiguous findings.

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 short, front-loaded lines: bracketed scope, usage condition, then return value. No filler sentences, though the bracketed header is slightly redundant with the annotation title and the parenthetical trust claim could be tightened.

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 two-parameter read-only tool with no output schema, the description covers purpose, trigger conditions and the shape of the return value. What remains thin is how a 'finding' should be phrased and what happens with sparse or ambiguous input, but nothing critical is missing for correct invocation.

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%, so both parameters are already documented, and the description's prose ('from clinical findings to basic mechanisms, or from mechanism to clinical application') mirrors the direction parameter's two values without adding format or default guidance. Baseline 3 is appropriate when the schema does the heavy lifting.

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 states a specific verb-and-resource pair: converting between clinical findings and basic mechanisms ('临床↔基础转化配对', 'Bridge'), and names the output (translational hypotheses and experimental protocols). It is clear what the tool does, though it does not explicitly contrast itself with any sibling such as act_gene_pathway or act_decision_followup.

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 concrete when-to-use conditions in both directions: when the user wants to find a basic mechanism behind a clinical finding, or a clinical application for a mechanism. There are no explicit exclusions or named alternatives, so it stops 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.