recommend_template_for_csv

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

No annotations are provided, so the description carries full burden. It mentions that the tool 'recommends templates' and returns 'ranked template recommendations', which gives basic behavioral insight. However, it lacks details on what 'templates' entail (e.g., visualization templates, dashboard layouts), how recommendations are generated, performance characteristics, or any limitations. For a tool with no annotations, this leaves significant gaps in understanding its 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 appropriately sized and front-loaded: the first sentence states the purpose clearly, followed by structured sections for Args and Returns. There's no wasted text, and each section serves a purpose. However, the formatting with separate sections could be slightly more integrated, but it remains efficient and easy to parse.

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 3 parameters with 0% schema coverage and no annotations, but with an output schema present, the description is moderately complete. It covers the basic purpose, parameters, and return value, but lacks depth in behavioral context and parameter details. The output schema likely documents the return structure, so the description doesn't need to explain return values further. However, for a tool with no annotations and poor schema coverage, more elaboration on usage and parameters would improve completeness.

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 lists parameters with brief explanations: 'csv_path: Path to the CSV file', 'chart_types: Comma-separated chart types (optional)', and 'sample_rows: Rows to sample for inference'. This adds meaning beyond the schema's titles, but it's minimal—lacking details on format constraints, valid chart types, or sampling implications. With 3 parameters and no schema descriptions, this partial compensation is insufficient for full clarity.

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's purpose: 'Recommend templates for a CSV file' with the specific verb 'recommend' and resource 'templates for a CSV file'. It distinguishes from siblings by specifying 'no active workbook needed', which differentiates it from workbook-related tools like 'create_workbook' or 'open_workbook'. However, it doesn't explicitly differentiate from the sibling tool 'recommend_template' (without '_for_csv'), which might handle different input types.

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 context with 'no active workbook needed', suggesting this tool is for standalone CSV analysis rather than workbook operations. However, it doesn't provide explicit guidance on when to use this versus alternatives like 'suggest_charts_for_csv' or 'recommend_template', nor does it mention prerequisites or exclusions. The guidance is present but limited to a single contextual note.

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