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

No annotations are provided, so the description carries the full burden. It clearly discloses that it fetches SKILL.md from a URL, computes a canonical SHA-256 hash, runs an 8-point audit, and starts scoring at 100. It does not mention caveats like non-public URL failures or side effects of the fetch, but the core behavior is transparent.

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 compact, purpose-first, and free of filler. Each sentence adds value: the audit scope, scoring method, threshold, and URL format are all covered without redundancy.

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?

With one simple parameter and an output schema present, the description covers the tool's purpose, process, and input format adequately. It could add explicit guidance on when to prefer this over sibling tools, but that gap is already reflected in the usage dimension.

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?

Schema coverage is 0%, so the description must compensate for the single parameter, and it does: it specifies that github_url must be a GitHub repo or a direct HTTPS link to SKILL.md. This provides the format and constraint that the schema lacks.

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 ('Audit'), a clear resource ('AI agent skill (SKILL.md)'), and the security-risk focus. It also adds an 8-point audit and passing score, which differentiates it from sibling verification tools like mt_skill_verify and mt_skill_issue_vc.

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?

Usage is implied: call this to assess a skill URL for security risks. However, the description never explicitly names alternatives, when not to use it, or how it compares to related auditing/verification tools, leaving some selection burden on the agent.

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