quality_report

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

With no annotations, the description carries the transparency burden. It clearly details the output contents and implies a read-only operation, though it does not explicitly state that it modifies nothing. No contradictions exist.

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 three sentences: one lists contents, one gives a usage directive, one reinforces the value. Each sentence earns its place, though the first sentence is slightly dense with metrics.

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?

An output schema exists, so return types are likely defined. The description covers the scope of the report and provides essential usage context. For a single-parameter tool, this is sufficiently complete, though it could mention whether the tool accepts only the current dataframe.

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 does not mention the single 'df_name' parameter, and schema coverage is 0%. The schema only provides the title 'Df Name' and a default empty string, which is minimal. The description fails to explain what to pass or how the parameter relates to the current dataframe.

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 states a specific verb and resource ('Comprehensive data quality report') and enumerates the exact metrics (dtype, missing values, uniques, duplicates). This clearly distinguishes it from sibling tools like get_info, get_column_profile, and get_statistics.

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 marks it as the 'ESSENTIAL FIRST STEP after loading data' and instructs to run it before any cleaning or transformation, giving strong temporal guidance. It does not name alternative tools or state when not to use it, so it lacks explicit exclusions.

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