get_head

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

With no annotations, the description carries the full burden. It discloses a read-only behavior that returns first N rows as a formatted table, with no side effects. It does not cover edge cases like an empty df_name, but for a simple head operation this is adequate.

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?

Three concise sentences: action, usage context, and example. The description is front-loaded with the core purpose and every sentence adds 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?

The description covers purpose, usage, and output format ('formatted table') with an example, which is sufficient for a simple tool. However, it omits df_name semantics and selection behavior, a meaningful gap in the dataframe context.

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 only indirectly explains n via 'first N rows' and the example get_head(n=10). df_name is completely unaddressed, and with 0% schema description coverage, the description fails to compensate for the missing parameter meaning.

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 'Return the first N rows of the dataframe as a formatted table', specifying a verb, resource, and format. The phrase 'Quick first look after loading' differentiates it from other inspection tools like get_tail and get_info by emphasizing initial data verification.

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 explicitly tells when to use the tool: 'Quick first look after loading. Verify data loaded correctly and understand structure.' This provides clear context, though it does not explicitly name alternatives or exclusion criteria.

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