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

No annotations are provided, so the description carries full burden. It discloses critical behavioral traits: graph_status is an internal label, not clinical validation; coverage is partial and absence is not evidence; pathway_tags are raw metadata; dose/pathway details are suppressed for certain rows; and legacy dose_mg usage is clarified. This is exemplary transparency.

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 dense yet every sentence earns its place: purpose first, then provenance caveats, coverage limitations, pathway_tags caveat, and dose field usage. No fluff, front-loaded, and verbose only where necessary.

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?

With no output schema and no annotations, the description compensates by explaining key output field semantics and limitations. It could be more explicit about the overall return structure (e.g., a list of intervention objects), but it covers the most important caveats thoroughly.

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%, with both biomarker and limit documented in the schema. The description adds context about output fields (dose_value, dose_unit, dose_mg) but does not elaborate on parameter input semantics beyond what the schema already provides. Baseline 3 is appropriate.

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 opens with 'Find mixed interventions in Aviado's current graph that affect a biomarker,' a specific verb+resource statement that clearly distinguishes it from sibling supplement-focused tools. It goes further by enumerating the exact intervention_type values, reinforcing the scope.

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 context is clear: use this when you need a biomarker's interventions across multiple types (e.g., supplement, drug, compound). However, it does not explicitly state when not to use it or mention sibling alternatives like get_supplements_for_biomarker. The 'mixed' wording implies broader coverage, but alternatives are not named.

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