get_ppi_enrichment

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool performs a statistical test and returns a p-value, which implies a read-only, non-destructive operation. However, it lacks critical details: it doesn't specify computational requirements (e.g., timeouts for large protein sets), error conditions (e.g., invalid identifiers), or output format beyond 'p-value' (e.g., numeric range, significance thresholds). For a tool with no annotation coverage, this leaves significant behavioral 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 highly concise and front-loaded: the first sentence directly states the tool's purpose, and the second sentence adds crucial output information. There's no wasted language, repetition, or unnecessary elaboration. Both sentences earn their place by providing essential context that isn't redundant with the schema or annotations, making it efficient for an agent to parse.

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 the tool's complexity (statistical testing with 3 parameters) and lack of annotations or output schema, the description is minimally adequate. It covers the core purpose and output but omits important context: it doesn't explain what 'enrichment' means in this domain (e.g., biological context), doesn't detail error handling or performance limits, and doesn't guide interpretation of the p-value result. For a tool with no structured output schema, more elaboration on return values would be beneficial, but it meets a basic threshold.

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 the schema already fully documents all three parameters (identifiers, species, required_score) with clear descriptions. The description adds no parameter-specific information beyond what's in the schema—it doesn't explain how identifiers are processed (e.g., validation rules) or contextualize the statistical implications of parameters like 'required_score'. Given the high schema coverage, a baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

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's purpose: 'Test if your protein set has more interactions than expected by chance' specifies the action (test) and resource (protein set interactions). It distinguishes from siblings like 'get_interaction_partners' (which likely retrieves specific partners) by focusing on statistical enrichment testing rather than raw interaction data. However, it doesn't explicitly contrast with 'get_enrichment' (a sibling tool), leaving some ambiguity about differentiation.

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 provides no guidance on when to use this tool versus alternatives. It mentions the output ('Returns enrichment p-value') but doesn't specify scenarios where this statistical test is appropriate compared to siblings like 'get_enrichment' (which may handle different enrichment types) or 'get_network' (which might visualize interactions). There's no mention of prerequisites, exclusions, or typical use cases, leaving the agent to infer usage from context alone.

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