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

With no annotations, the description carries the full transparency burden. It discloses the statistical methodology (observed vs expected edges) and provides output field interpretations (e.g., 'p_value: smaller = more enriched'). However, it does not mention potential side effects (though likely none), prerequisites like species handling, or behavior under invalid input, leaving gaps.

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 description is well-structured with bullets and an output field list, but it includes extraneous elements like 'Report the p-value as a human-readable value' and 'When calling related tools...' which are not core tool-defining content. It could be tightened without losing key information.

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?

An output schema exists, so listing return fields is redundant but adds interpretive value. The description does not address when to provide required_score or how species defaults behave, and it lacks guidance on input prerequisites. It is adequate for basic selection but not fully complete for invocation without schema inspection.

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 low (33%), so the description must compensate. It provides an example identifier format ('SMO%0dTP53') but does not clarify the semantics of the optional species or required_score parameters beyond what the schema already says. The phrase 'use the same input parameters' adds little specific value.

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 starts with a specific verb ('tests if your network is enriched') and clearly identifies the resource (protein-protein interactions) and the comparison (background proteome-wide distribution). This distinguishes it from sibling tools like string_enrichment, which likely focus on functional term enrichment.

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 when one wants to assess whether a protein set is more functionally connected than random, but it does not explicitly state when to choose this tool over alternatives such as string_enrichment or string_network_clustering. The only related-tool note ('When calling related tools use the same input parameters') is about parameter reuse, not tool selection.

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