validate_capture

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

With no annotations, the description carries the burden of behavioral disclosure. It clearly enumerates what the validation checks, notes that issue categories can overlap, and states that example_limit only limits retained examples while counts always include all matches. It does not explicitly state whether the operation is read-only, but 'validate' strongly implies a non-mutating analysis.

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 well-structured with a purpose statement, a checklist of validation categories, Args, and Returns. Each sentence adds information, and the most important purpose is front-loaded. No redundant or filler content is present.

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?

The tool has an output schema, so return-value details do not need to be repeated. The description covers the operation, checked issues, both parameters, and the nature of results. It could be slightly more explicit about error behavior or prerequisites beyond 'currently loaded capture,' but it is otherwise complete for a validation tool.

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 fully explains both parameters: capture_id is 'ID of a currently loaded capture' and example_limit is 'Maximum frame numbers retained for each issue, from 0 through 100,' adding the important behavior that counts include all matches. This goes well beyond the bare schema types.

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 opens with a specific verb and resource: 'Validate capture readability and identify common packet-quality issues.' It goes on to list concrete checks (malformed packets, truncation, missing fields, decode failures, unsupported protocols, timestamps, chronological order), which clearly separates it from sibling summary/statistics tools.

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: call this when you need to validate a capture's readability and packet quality. However, it never explicitly states when to choose this tool over siblings like get_summary or get_capture_statistics, nor does it provide exclusions or alternative routing.

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