table_convert

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

With no annotations, the description carries the full burden and does so well. It discloses strong behavioral guarantees: 'Rows are never silently dropped, and ragged rows are padded rather than truncated.' It also details the Markdown alignment behavior with East-Asian wide characters counted as two cells. These go far beyond the bare conversion verb and give an agent a reliable mental model of the tool's 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 front-loaded with a one-sentence summary, immediately followed by usage context and behavioral guarantees, then a structured Args list. Every sentence earns its place: the wide-character note, row-preservation guarantee, and alias examples are all operationally relevant. No filler or repetition.

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 5 parameters, no annotations, and no schema descriptions, the description covers all essential information: full parameter semantics, behavioral edge cases, and explicit usage triggers. The output schema exists, so return values do not need to be explained. An agent has enough to select and call this tool correctly without additional investigation.

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. The Args section fully compensates by explaining each parameter's meaning, acceptable values, aliases (md, htm), example formats ("left,right,center"), and defaults (indent 2, aligns empty). This adds substantial value beyond the schema titles and enables correct invocation.

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+resource+scope: 'Convert tabular data between formats.' It immediately clarifies the exact operation and gives concrete format-pair examples (CSV to Markdown, Markdown to JSON, HTML table to CSV), which distinguishes it from sibling tools like table_align, table_transform, and table_inspect. The behavior is stated as 'change shape,' making the tool's role unambiguous.

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 explicitly says 'Use this whenever a table needs to change shape,' providing a clear triggering condition. However, it does not mention when not to use it or name alternative sibling tools for alignment or inspection, so the guidance lacks exclusions and alternative routing. This is clear context but not full when/when-not coverage.

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