analyze_structured_data

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

No annotations are provided, so the description carries the full burden. It mentions the tool analyzes for PII and returns findings, but lacks critical behavioral details: it doesn't specify what 'analyze' entails (e.g., detection only, no modification), required permissions, rate limits, error handling, or performance characteristics. The description is minimal and misses key operational context.

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 sized and front-loaded: the first sentence states the purpose, followed by a structured list of args and returns. Every sentence adds value, with no redundancy. It could be slightly more concise by integrating the args/returns into prose, but it's efficient overall.

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 4 parameters with 0% schema coverage and no annotations, the description is moderately complete: it covers the purpose and parameters at a high level. However, with an output schema present, it needn't explain return values in detail, but it still lacks behavioral context (e.g., safety, limits) and usage guidelines, making it adequate but with clear gaps.

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 must compensate. It lists all four parameters with brief explanations (e.g., 'JSON string representing structured data'), adding basic meaning beyond the schema's titles. However, it doesn't elaborate on formats (e.g., JSON structure), entity type options, or threshold implications, leaving gaps in 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: 'Analyze structured data (JSON/dict) for PII.' It specifies the verb ('analyze'), resource ('structured data'), and target ('PII'). However, it doesn't explicitly differentiate from siblings like 'analyze_text' or 'batch_analyze' beyond the 'structured data' qualifier, which is implied but not contrasted.

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 doesn't mention siblings like 'analyze_text' (for text vs. structured data) or 'batch_analyze' (for batch processing), nor does it specify prerequisites, exclusions, or optimal use cases. Usage is implied by the tool name but not explicitly stated.

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