predicate_chain

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

With no annotations, the description carries the full burden. It reveals that the output includes the age of each predicate and that it shows how AI/ML devices chain to older predicates. While it lacks detail on edge cases (e.g., missing predicates, depth limits), it provides meaningful behavioral insight beyond the schema.

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 two sentences, no filler, and front-loads the primary action and resource. Every word adds value, making it efficient for an agent to parse quickly.

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 specialized function and good schema coverage, the description covers the core purpose and distinctive features (predicate age, AI/ML focus). It lacks mention of output format or error behavior, but these are not critical for basic selection; it is sufficiently complete for the complexity level.

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 coverage is 100% so the schema already documents both parameters (k_number and depth) with descriptions. The tool description does not add new semantic meaning to the parameters beyond what the schema provides, earning the baseline score of 3.

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 uses the specific verb 'Trace' and identifies the resource as 'predicate ancestry of a 510(k) device'. It adds unique value by mentioning the age of predicates and the focus on AI/ML devices, clearly distinguishing it from sibling tools like device_risk_lookup or evidence_search.

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 the tool is for analyzing predicate chains and specifically highlights AI/ML devices, providing clear context. However, it does not explicitly state when not to use it or name alternative tools, missing a chance to guide the agent away from inappropriate uses.

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