ontomics

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what the tool does (finds data-flow edges, shows functions passing/receiving types) but lacks details on output format, performance characteristics, limitations, or error conditions. The description doesn't contradict any annotations since none exist, but it's moderately informative rather than comprehensive.

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 perfectly concise and well-structured with three sentences: a purpose statement, a behavioral explanation, and usage examples. Every sentence adds value, and it's front-loaded with the core functionality. No wasted words or redundancy.

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 (type tracing in data-flow), no annotations, no output schema, and 100% schema coverage, the description is reasonably complete. It explains purpose, behavior, and usage context well, though it could benefit from mentioning output format or limitations. The lack of output schema means the description doesn't need to explain return values, but some additional behavioral context would help.

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 input schema has 100% description coverage, with the single parameter 'type_name' clearly documented. The description adds minimal value beyond the schema by mentioning examples ('Tensor', 'np.ndarray') that are already in the schema description. This meets the baseline of 3 when schema coverage is high.

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 with specific verbs ('Find all data-flow edges', 'Shows which functions pass and receive') and resources ('involving a specific type', 'this type through call edges'). It distinguishes from sibling tools like 'trace_concept' and 'type_flows' by focusing on type-based data-flow tracing rather than concept or general type analysis.

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 explicit usage guidelines with concrete examples: 'Use when asked 'where does Tensor flow', 'how is type X passed around', or 'which functions use type X''. This gives clear context for when to invoke this tool versus alternatives, though it doesn't explicitly name sibling tools as alternatives.

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