system_audit

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 names what will be audited but does not state whether the audit is read-only, whether it has side effects, how long it might take, or what form the results take. 'Audit' weakly implies inspection, but that is not made explicit.

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 a single, front-loaded sentence that avoids filler and immediately communicates the tool's scope. Every component listed 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 tool, the description gives a reasonable overview of scope. However, with no output schema and no annotation support, it does not describe the expected result format, return payload, or behavioral characteristics, leaving an agent uncertain about what the audit will produce or whether it is safe to invoke.

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 tool has zero parameters and the schema already fully documents that (100% coverage). With no parameters to explain, the description need not add parameter-level detail, so the baseline of 4 applies.

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 resource as the 'entire development platform' and lists concrete audit areas: providers health, Studio connection, DataModel state, and knowledge graph statistics. It is specific enough to distinguish from narrower analysis tools, though it does not explicitly name or contrast any sibling tools.

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 this is the tool for broad, system-wide diagnostics by saying 'Deep audit of the entire development platform.' However, it provides no explicit guidance on when to choose this over sibling tools like project_health, completeness_audit, or capability_audit, and no exclusions are given.

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