get_functional_enrichment

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

No annotations are provided, so the description carries full burden. It mentions the data source (STRING) and output types, but does not disclose behavioral traits like read-only nature, rate limits, or data freshness. A 3 is appropriate as it provides basic context but lacks depth.

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?

Two short sentences with no fluff. Front-loaded with the action and resource, then specifies return types. Every sentence earns its place.

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 two parameters, high schema coverage, and no output schema, the description adequately explains what the tool does and returns. It could mention significance or sorting of results, but overall is complete enough for effective use.

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% with descriptions for both parameters. The description adds no further per-parameter details, so it meets the baseline for high coverage. No extra value beyond schema.

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 the tool performs functional enrichment analysis on a list of proteins using STRING, returning GO terms, pathways, and domains. This distinguishes it from sibling tools like 'get_kegg_pathway' which fetch data for single entities.

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?

The description implies usage context: for a list of proteins, using STRING. It doesn't explicitly exclude other tools, but the purpose is clear enough to infer when to use it over single-entity query tools among siblings.

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