inspect_data_around

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

With no annotations provided, the description carries full burden and does well by explaining what the tool returns ('contextual view of data around the specified coordinates'), its inspection features (center point, radius view, data context), and practical applications. It doesn't mention performance implications, rate limits, or data size constraints, but provides substantial behavioral context 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 with clear sections (purpose, returns, features, examples, workflow integration) and uses emojis for visual organization. While somewhat lengthy, every section adds value. The front-loaded purpose statement is strong, though the later sections could be more condensed while maintaining clarity.

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 moderate complexity, 100% schema coverage, and presence of an output schema, the description provides excellent contextual completeness. It covers purpose, usage scenarios, behavioral characteristics, examples, and integration workflows. The output schema existence means the description doesn't need to detail return values, and it provides all necessary context for effective tool selection and use.

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 schema description coverage is 100%, providing complete parameter documentation. The description adds minimal value beyond the schema, mentioning 'configurable area around center cell' for radius and 'clear row/column reference system' for coordinates, but doesn't provide additional semantic context about parameter interactions or edge cases. This meets the baseline 3 for high schema coverage.

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 specific verbs ('inspect', 'examines') and resources ('data around a specific coordinate'), distinguishing it from siblings like get_cell_value (single cell) or get_row_data (entire row). It explicitly mentions 'contextual analysis' and 'understanding local data patterns', which differentiates it from broader analysis tools like profile_data or get_data_summary.

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 explicit usage guidance with 'Useful for data validation, error investigation, and understanding local data patterns' and 'AI Workflow Integration' section listing specific scenarios (error investigation, pattern recognition, etc.). It clearly indicates when to use this tool versus alternatives like get_cell_value (single point) or get_row_data (entire row) by emphasizing the 'surrounding' context.

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