make_table

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

Without annotations, the description carries the burden. It explains the return format (Markdown code block or raw pipe table) and notes that rows and data are mutually exclusive. However, it does not disclose error behavior (e.g., for invalid rows) or side effects. It is adequate but not exhaustive.

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 clear purpose statement followed by a bulleted Args list. It is slightly lengthy but front-loads the key action. Some redundancy (e.g., 'data' alias explanation) could be trimmed for conciseness.

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 7 parameters, no annotations, and an output schema, the description is thorough. It explains the output format, parameter interactions (e.g., mutual exclusivity), and formatting options. The agent can confidently invoke the tool correctly.

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 0%, so the description fully compensates. It details all 7 parameters, including fmt options (grid, box, pipe, safe), style variants, and the auto_format/safe_width flags. This provides significant added meaning beyond the raw schema.

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 'Render data as a table,' which identifies the tool's core function. However, it does not explicitly contrast with siblings like make_table_from_csv or make_table_from_json, which accept different input formats. This slightly reduces 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?

The description lacks explicit guidance on when to use this tool versus its siblings. It does not mention that this tool is for rendering raw arrays, while siblings handle CSV/JSON input. The agent must infer usage from parameter types.

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