Find Advisor Recommendations by Text Search

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered. The description adds one genuinely useful behavioral fact – that list results omit playbooks – but is otherwise thin, with no note on result limits, pagination, or matching behavior beyond 'exact substring'.

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?

Two short sentences with zero waste; the core action is front-loaded and the sibling pointer follows. Nothing could be trimmed without losing routing 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?

An output schema exists, so return values need no explanation, and annotations cover the safety profile. For a one-parameter read tool the description is nearly sufficient, lacking only detail on result size/ordering limits.

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% for the single parameter, so the schema already explains the input. The description's phrase 'exact text substring' marginally reinforces the matching mode but adds no syntax, case-sensitivity, or format detail beyond the schema.

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 gives a specific verb+resource ('Find Advisor Recommendations') plus the matching semantics ('exact text substring'), which an agent can act on directly. It does not, however, differentiate this search tool from other advisor retrieval siblings such as get_active_rules or get_rule_from_node_id.

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 proactively routes the agent: lists omit playbooks, and get_rule_details is named for remediation. That is a clear use-this-for-X, use-that-for-Y split, though it does not state when to prefer this over the other advisor list/search tools.

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