disease_surveillance

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 this is a lookup operation for public domain data, implying it's likely read-only and non-destructive, but doesn't explicitly confirm this or address other behavioral aspects like rate limits, authentication needs, error handling, or data freshness. For a tool with zero annotation coverage, this is a significant gap.

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 appropriately concise with two sentences: one stating the purpose and scope, and another specifying the data source. It's front-loaded with the core functionality. There's no wasted text, though it could be slightly more structured (e.g., bullet points for key features).

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 moderate complexity (3 parameters, no output schema, no annotations), the description is partially complete. It covers the purpose and data source but lacks details on usage guidelines, behavioral traits, and output format. Without annotations or an output schema, the agent has incomplete context for effective tool invocation.

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?

The description adds minimal parameter semantics beyond the input schema. It mentions 'by condition and geography,' which aligns with the 'condition' and 'state' parameters in the schema, but doesn't provide additional context like examples of valid conditions or geographic scopes. With 100% schema description coverage, the baseline is 3, and the description doesn't significantly enhance parameter understanding.

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: 'Look up disease surveillance data including case counts and trends by condition and geography.' This specifies the verb ('look up'), resource ('disease surveillance data'), and scope ('case counts and trends by condition and geography'). However, it doesn't explicitly differentiate from sibling tools like 'code_lookup' or 'drug_lookup', which are also lookup tools but for different data types.

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 data source ('CDC National Notifiable Diseases Surveillance System') but doesn't specify use cases, prerequisites, or exclusions. Given the sibling tools include various lookup and analysis tools, this lack of differentiation leaves the agent without clear usage context.

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