analyze_excel

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool performs statistical analysis and returns JSON, but lacks critical details like what statistical methods are used, whether it modifies the Excel file, error handling for invalid data, or performance considerations. This is inadequate for a tool with 3 parameters and no annotation coverage.

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 'Args' and 'Returns' sections, making it easy to parse. It's concise with no redundant information, though the lack of usage guidelines slightly reduces efficiency. Every sentence adds value, earning a high score for structure.

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 (3 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the statistical analysis output in detail (e.g., what metrics are included), error conditions, or how it interacts with sibling tools. For a data analysis tool with multiple parameters, this leaves significant gaps for an AI agent.

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 adds some semantic context beyond the input schema, which has 0% description coverage. It explains that 'columns' is a comma-separated list and defaults to analyzing all numeric columns if None, and that 'sheet_name' is for Excel files. However, it doesn't clarify the format for 'file_path' (e.g., local vs. remote) or provide examples, leaving gaps in parameter understanding.

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 with a specific verb ('perform statistical analysis') and resource ('on Excel data'), making it immediately understandable. However, it doesn't differentiate this tool from potential sibling tools like 'data_summary' or 'read_excel' that might also analyze Excel data, preventing a perfect score.

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 provides no guidance on when to use this tool versus alternatives like 'data_summary', 'filter_excel', or 'pivot_table' from the sibling list. It mentions parameters but doesn't explain the tool's specific use cases, prerequisites, or exclusions, leaving the agent with minimal contextual direction.

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