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

Annotations already declare readOnlyHint/idempotentHint/non-destructive, so the safety profile is covered. The description adds genuine context beyond that: the output is step-by-step remediation guidance with a priority level for triage, and codeSnippet enables context-aware fixes. No auth or rate-limit details, but none are critical for a read-only prompt fetch.

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?

Front-loads what is retrieved, then usage and workflow in clearly delimited sections. Slightly verbose with bolded emphasis, but every sentence carries actionable routing or behavioral information.

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?

Output schema exists so return values need no explanation, annotations cover safety, and usage is fully specified. Only marginal gaps (e.g., what kind of rule slugs are valid) remain, which the sibling get_rule/search_rules context covers.

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 description coverage is 100% and the description does not restate slug or codeSnippet semantics beyond what the schema documents. The mention of 'priority level' concerns the response, not an input, so the baseline 3 is appropriate.

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?

States a specific verb and resource ('Retrieves the fix/implementation prompt for a specific rule'), and the workflow sentence distinguishes it from get_rule (context) and explain_rule (understanding). An agent can tell what it returns without opening the schema.

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?

Explicit triggering conditions ('Use PROACTIVELY after identifying issues in frontend code') plus a named workflow: call after review_code or check_rule. Alternatives are named with their distinct roles, leaving little to inference.

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