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

Going beyond the readOnlyHint annotation, the description discloses that this read can spend AI budget, which is a non-obvious side effect. It also reveals the fallback mechanism and the inclusion of a 'source' field to indicate the origin, providing clear behavioral expectations.

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 somewhat verbose but each sentence serves a purpose: defining the output, explaining the AI/static behavior, and clarifying why it is a separate tool. It is structured logically and not overly redundant.

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?

Given the simplicity of a single-parameter read, the description covers essential context: output format, fallback behavior, and project context requirement. It lacks explicit error scenarios or prerequisites beyond the finding ID, but these are not critical for basic usage.

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 schema already provides a complete description for the single parameter findingId, including its origin from get_security_findings. The tool description adds no further semantic detail about the parameter, so it stays at the baseline for high schema coverage.

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 states the tool's purpose: retrieving remediation advice for a single finding in GitHub-flavored markdown. It also distinguishes this tool from other reads by highlighting its unique AI budget consumption and static fallback behavior, making its role unambiguous.

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 explains when AI-generated advice is used versus the static fallback, and notes that project context is required. It implies usage for a specific finding (via findingId) but does not explicitly name alternative tools like list_security_guidance, though the reference to 'other reads' hints at the distinction.

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