validate_table_text

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 explicitly lists the checks performed: auto-detecting format, border/data line alternation, marker position consistency, column count stability, and correct border type. It also states the return type: a validation report with structural issues. This gives the agent a clear understanding of behavior.

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 efficiently structured: a one-line purpose, a bullet list of checks, and clear Args/Returns sections. Every sentence adds value, and the most critical information (purpose) appears first. No redundant phrases or filler.

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 context (1 required parameter, output schema exists), the description covers the essential aspects: the single parameter with format constraints, the checks performed, and the return type. The output schema handles return structure details, so the description does not need to list fields. A complete and sufficient description for the agent.

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 one parameter 'table_text' with only a title and no description (0% coverage). The description adds meaning by stating 'Raw table text (box, grid, or pipe format)', clarifying the acceptable formats and that it is raw text. This is valuable beyond the schema, though a note about expected input length or encoding would improve it further.

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 validates structural integrity of tables in various formats (box/grid/pipe). It specifies the resource (table text), the action (validate), and the scope of validation (structural checks). This distinguishes it from sibling tools like analyze_table, which may perform deeper analysis, and make_table which creates tables.

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 does not provide guidance on when to use this tool versus alternatives like analyze_table or debug_table. There is no mention of prerequisites, constraints, or scenarios where validation is appropriate. The agent must infer usage from context, which could lead to incorrect tool selection.

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