get_data_quality_report

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

With no annotations, the description carries full transparency burden. It discloses the report scope and contents, but does not explicitly state read-only behavior, performance implications, or how data is aggregated. Adequate but minimal for a report tool.

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 short lines with no fluff. Leads with the purpose, lists report categories, and mentions return value. Every word earns its place.

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?

For a zero-parameter report tool with an output schema, the description covers the main purpose, scope, and return value. Could add a note about data freshness or when to run, but not critical given the tool's simplicity.

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?

No parameters exist, so baseline is 4 per rubric. Description adds no parameter details, but none are needed since schema is empty.

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?

Description clearly states the tool generates a data quality report across all imported student data, with specific metric categories (missing fields, invalid URLs, format issues, completeness). This distinguishes it from sibling analytics tools like get_profile_statistics or validate_excel.

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?

Implies usage after data import ('across all imported student data'), but provides no explicit guidance on when to prefer this over alternatives like validate_excel or get_import_status. No exclusions or alternative tool references.

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