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

Beyond the readOnly/idempotent/non-destructive annotations, the description adds important behavioral context: aggregate-only data, no PII, single-issue decisions only, and that each evidence flag attaches to exactly one decided issue. It also discloses the limitation that this is not predictive.

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 but organized, with the core purpose front-loaded and parameter details at the end. The long enumeration of evidence types is relevant and earns its place, though it could be tightened without losing meaning.

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 simple two-parameter tool with no output schema, the description is complete: it explains the data source, filtering, output semantics (granted vs. not, raw fractions), and scope limitations. An agent has enough context to invoke it correctly and interpret results appropriately.

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 0%, so the description carries the full burden for parameters. It provides concrete examples for condition ('ptsd', 'sleep apnea', 'back', 'knee', 'tinnitus') and fully enumerates the allowed benefit values with the default ('service_connection'). This goes well beyond the bare string 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 that the tool returns measured evidence-vs-outcome data for a VA condition, filtered from the BVA archive, with grant rates by evidence type. It is specific about the resource and purpose, but it does not explicitly contrast itself with sibling tools like va_condition_intelligence or va_denial_stats.

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 gives explicit intended use: to assess which missing evidence historically mattered most and by how much, and it warns that the data is association, not prediction. However, it does not state when not to use this tool or point to alternative tools for related questions.

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