get_data_summary

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 describes what the tool returns (comprehensive overview with structural information) and mentions optional preview functionality, but doesn't disclose performance characteristics, error conditions, or resource implications beyond memory usage breakdown.

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 (Returns, Summary Components, Examples, AI Workflow Integration) and uses bullet points effectively. While comprehensive, some sections could be more concise, and the emoji icons in the Summary Components add visual clutter without semantic value.

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 and the presence of an output schema, the description provides good context about what the tool does and when to use it. The examples and workflow integration sections add practical guidance. However, for a tool with no annotations, more behavioral details about performance or limitations 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?

The input schema has 100% description coverage, so the schema already documents both parameters thoroughly. The description adds minimal value beyond the schema through the examples showing parameter usage, but doesn't provide additional semantic context about parameter interactions or effects.

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 as providing a 'comprehensive data overview and structural summary' with specific components like dimensions, data types, memory usage, and preview. It distinguishes from siblings by focusing on high-level structural analysis rather than specific operations like filtering or transformation, though it doesn't explicitly name alternatives.

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 clear context for when to use the tool ('Essential first step in data exploration and analysis planning workflows') and includes an 'AI Workflow Integration' section with specific use cases. However, it doesn't explicitly state when NOT to use it or name alternative tools for similar purposes.

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