check_ai_brand_visibility

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 usefully discloses that the tool *simulates* searches across LLM providers and performs multi-step analysis, which signals non-determinism and likely expense. However, it says nothing about permissions/auth needs, runtime, rate limits, or whether results are persisted — meaningful gaps for what is clearly a heavy analytical operation.

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 purpose sentence is front-loaded and dense with real information. The trailing 'Args:' block is Python-docstring style and somewhat redundant in framing, but given the 0% schema coverage it earns its place by supplying examples and optionality markers.

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 not be described. For a complex multi-stage analysis tool the description covers what is computed and what inputs matter, but it omits operational context — how long a run takes, whether results are cached, and any auth or quota constraints — which an agent would want before invoking it.

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 0%, so the description must compensate, and it does: it documents all four parameters with inline examples ('Pytch', 'AI PR agent', 'fintech') and marks website and competitors as optional. What it does not explain is how the competitors list is used (presumably for comparative SOV) or what format a website URL should take, so it stops short of full 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?

Opens with a specific verb+resource ('Audit brand visibility in AI answers') and then enumerates the concrete sub-operations: simulating LLM searches, checking citation, computing AI Share of Voice, extracting media outlets, and matching journalists. This is clearly distinct from siblings like check_domain_deliverability and search_journalists, so an agent can route correctly 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?

Context is implied by the purpose statement — you use this when you want to know how a brand shows up in AI-generated answers — but there is no explicit when-to-use, when-not-to-use, or comparison to alternatives such as get_project_results. Prerequisites (e.g. whether a PR project must exist first) and rough cost/latency expectations are absent.

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