twilize

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 discloses that the tool analyzes data shape and returns prioritized suggestions, which is useful. However, it doesn't mention behavioral traits like whether it's read-only (likely, but not stated), performance considerations (e.g., large file handling), or error conditions (e.g., invalid CSV). It adds some context but misses key operational details.

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 and front-loaded: the first sentence states the purpose, followed by analysis details, parameter explanations, and return value. Every sentence adds value without redundancy. It's appropriately sized for a tool with 4 parameters and complex functionality.

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 complexity (data analysis and suggestion generation), no annotations, and an output schema (which covers return values), the description is mostly complete. It explains what the tool does, parameters, and returns. However, it could improve by mentioning prerequisites (e.g., CSV format requirements) or limitations (e.g., supported chart types), slightly reducing 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 provides clear semantics for all parameters: 'csv_path' (path to CSV), 'max_charts' (maximum suggestions, with default behavior), 'sample_rows' (rows to sample), and 'rules_yaml' (YAML overrides). This adds meaningful context beyond the bare schema, though it could elaborate on format specifics (e.g., YAML structure).

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: 'Suggest chart types and configurations for a CSV file.' It specifies the action (suggest), resource (chart types/configurations), and target (CSV file). It distinguishes from siblings like 'configure_chart' (which configures existing charts) or 'csv_to_dashboard' (which creates dashboards) by focusing on analysis and suggestion rather than creation or configuration.

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 when you have a CSV file and want chart suggestions, but it doesn't explicitly state when to use this tool versus alternatives. For example, it doesn't compare to 'recommend_template_for_csv' (which suggests templates) or 'inspect_csv' (which profiles data). The context is clear but lacks explicit guidance on alternatives or exclusions.

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