debug_table

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

No annotations are present, so the description carries the full burden. It describes output but does not disclose side effects, read-only behavior, or any constraints. For a diagnostic tool, it likely is read-only, but this is not explicitly stated.

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 concise and front-loaded with the purpose. It uses a brief structure with a summary line followed by args and returns. Every sentence contributes, though it could be slightly more organized.

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 has 3 optional parameters and an output schema (not shown), the description covers the basic behavior and return type. However, it lacks details about input-output relationships, error cases, or differentiation from sibling tools.

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 only repeats parameter names and basic types: headers (optional column headers), rows (data rows), data (alias for rows). This adds minimal value beyond the schema, lacking format constraints or usage details.

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 renders and diagnoses a table, showing exact character positions. It lists output components (plain table, annotated positions, drift comparison). However, it does not explicitly distinguish from sibling tools like analyze_table or make_table, so it is clear but lacks differentiation.

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?

No guidance is provided on when to use this tool versus alternatives. There is no mention of use cases, prerequisites, or situations where other tools would be more appropriate.

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