inspect_physics

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 discloses that the tool analyzes simulation data and returns structured JSON, and details the types of extracted data (vortex detection, boundary conditions, etc.). However, it does not mention read/write nature, authorization needs, computational cost, or side effects, which are typical for such a tool.

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 moderately concise but uses bullet points and some redundant phrasing (e.g., 'structured JSON' and 'quantitative topology data' overlap). It front-loads the purpose but could be trimmed without losing clarity. It is adequate but not optimally concise.

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 complexity (5 parameters, output schema exists), the description is reasonably complete. It covers the two levels of extraction, return format, and the tool's role relative to a sibling. It lacks details on error handling or performance, but these are not critical for basic usage. The output schema likely covers return value structure, so the description does not need to explain it.

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 baseline is 3. The description adds context by explaining the high-level purpose of the extracted data levels, but it does not enhance understanding of individual parameters beyond what the schema already provides. The schema itself has adequate descriptions for each parameter.

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 it extracts structured physics data for AI storytelling, specifying L2 FieldTopology and L3 CaseContext. It distinctly identifies the tool's purpose and differentiates it from the sibling tool 'analyze_data' by emphasizing quantitative topology data over hardcoded heuristics.

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 usage for physics narrative building and mentions replacing 'analyze_data', but does not provide explicit guidance on when to use this tool versus alternatives, nor does it specify prerequisites or exclusion criteria. The guidance is implied but insufficiently explicit.

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