suggest_monitoring_config

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 discloses the return artifact ('ready-to-use configuration JSON and API instructions to apply it'), which implies a non-mutating generation step, but it never states that it writes nothing, whether an API key is needed to apply the config, or any rate/cost limits. Adequate but leaves real behavioral gaps.

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?

Purpose and output contents are front-loaded in the first sentence, and the when-to-use condition follows immediately. The final sentence ('AI agents can use this to auto-fill...') largely restates the usage clause and is the one element that does not fully earn its place.

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?

There is no output schema, but the description compensates by naming what comes back (audience descriptions, location, queries, ready-to-use JSON plus apply instructions). For a four-parameter generation tool with no side effects this is close to sufficient; only error/edge behavior when the business description is thin is unaddressed.

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%, so all four parameters are already documented, including the note that target_queries tunes audience suggestions. The description restates that tuning idea ('audience suggestions will be tuned to match how these queries are typically asked') without adding format, syntax, or constraint details beyond the schema. 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 ('Generate') and resource ('AI monitoring configuration for a business') and enumerates the artifacts produced: target audience descriptions, monitoring location, query suggestions. None of the siblings (analyze_geo_score, get_geo_fixes, get_site_report) overlap, so the agent can distinguish it 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 trigger condition: 'Use this when the user describes their business and wants to set up monitoring for the right audience and location,' plus a secondary agent-facing use case in the final sentence. Clear context, but no when-not-to-use guidance or named alternatives (e.g., when to just run the monitoring directly instead of generating a config).

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