pivot_table

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool creates a pivot table and returns it as a string, but lacks details on permissions needed, file format requirements, error handling, or performance characteristics. The description is minimal beyond basic functionality.

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 appropriately sized. It begins with a clear purpose statement, followed by a parameter list with brief explanations, and ends with return information. Every sentence serves a clear purpose with zero wasted content.

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 6 parameters with no schema descriptions and no output schema, the description provides parameter semantics but lacks behavioral context. For a data transformation tool with multiple siblings, it should ideally explain more about when to use it, what the output format entails, or any limitations. It's minimally adequate but has clear gaps.

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 description provides clear semantic explanations for all 6 parameters beyond their titles in the schema (which has 0% description coverage). Each parameter's role in pivot table creation is explained, such as 'index: Column to use as the pivot table index' and 'aggfunc: Aggregation function ('mean', 'sum', 'count', etc.)'. This adds significant value over the bare 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 the tool creates a pivot table from Excel data, specifying the verb 'create' and resource 'pivot table from Excel data'. It distinguishes from siblings like 'analyze_excel' or 'data_summary' by focusing specifically on pivot table creation, though it doesn't explicitly contrast with them.

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?

No guidance is provided on when to use this tool versus alternatives like 'analyze_excel' or 'data_summary'. The description only states what the tool does without indicating appropriate contexts, prerequisites, or exclusions.

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