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

Annotations already declare readOnly, idempotent, and non-destructive hints, so the baseline is lower. The description adds valuable behavioral context: it clarifies that methods are executed via start(), that each method carries a review_status (accountant-reviewed | source-cited), and that results may include either or both content types. This enriches understanding beyond the structured fields.

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 relatively long but well-structured: it opens with the core purpose, provides examples, then covers usage guidance and content_type semantics. Every sentence adds operational value (examples, when-to-use, method vs fact distinction). It is not overly terse nor redundant, though a slightly tighter phrasing could reduce length without loss.

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 4-parameter tool with an output schema, the description covers all essential aspects: what it searches, when to use it, how to use content_type, jurisdiction limiting, and the review_status attribute. It doesn't describe pagination or result formatting, but the output schema presumably covers that, so the description need not. It is complete relative to its complexity and sibling context.

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 100%, so baseline is 3. The description adds meaning beyond the schema: it explains the query parameter with concrete examples and advises that 'With content_type='method', a broad term lists the available methods.' It also clarifies the distinction between 'method' and 'fact' in the content_type parameter, which the schema only labels as 'What kind of Guide to find.' This additional context aids correct usage.

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 action (full-text search) and the resource (published tax and accounting skills). It provides concrete examples ('home office deduction', 'Schedule C') and explicitly differentiates from siblings by saying 'Use this when you don't know the exact skill slug' – distinguishing it from get_skill and list_skills, which require known slugs or full lists.

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?

It gives explicit when-to-use guidance: 'Use this when you don't know the exact skill slug.' It also explains the content_type parameter ('method' for executable procedures vs 'fact' for reference material) and how to leverage it for discovery. This is clear and actionable, covering both the core and edge use cases.

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