audit_star_schema

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

Even without annotations, the description explicitly states READ-ONLY, lists the classification logic and schema smells checked, and describes the return value (scored report 0-100 with per-issue recommendations). This is highly transparent about behavior and output.

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 two dense sentences, front-loaded with READ-ONLY star-schema audit, and enumerates specific behaviors and output. Every sentence provides meaningful detail with no fluff.

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?

Despite lacking an output schema, the description fully explains what the tool does, what it detects, and what it returns (scored report with recommendations). This is sufficient for an agent to select and invoke the tool correctly.

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 only parameter, sessionId, is not explained in the description and schema coverage is 0%. Since it is a standard session context parameter common to many tools, the lack of explanation is a minor gap, but the description does not compensate for the schema omission.

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 identifies the tool as a READ-ONLY star-schema audit, specifies what it does (classify tables into fact/dimension/date/bridge/disconnected) and what it flags (schema smells like snowflaking, M:M, etc.). It is easily distinguished from sibling audit tools like audit_theme_compliance or audit_naming.

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 implies it should be used for star-schema model auditing, with clear context of its scope, but it does not explicitly mention alternatives or when not to use it. There are no exclusions or comparisons to other audit tools.

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