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

Beyond the readOnly/idempotent/non-destructive annotations, it discloses the return shape (mention share, citation share, per-engine and buyer-prompt splits, rival counts, cited sites, fix list) and the two fixed matching rules (a repeated prompt is not a mention; a link counts only if it resolves to the brand domain). This is exactly the behavioral context annotations cannot carry.

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?

It is front-loaded with the call trigger, then what it returns, then the fixed rules and the exclusion, so no sentence is wasted. The density is high and it reads as one long paragraph rather than clearly segmented blocks, which slightly hurts scannability but not substance.

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 9-parameter, single-required-field tool with an output schema and full annotation coverage, the description supplies everything an agent needs: trigger conditions, input framing, matching rules, and scope limits. Return-value detail is redundant given the output schema but harmless.

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 the baseline is 3, but the description adds cross-parameter meaning by framing how inputs fit together ('pass the brand, its domain, rival names and every answer in answers, each with its prompt, engine and source links') and by tying the prompt-matching rule to why the prompt field matters. It stops short of documenting the aliases/competitor domain semantics, which remain schema-only.

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 opens with a precise trigger and a specific verb+resource: scoring whether pasted AI answers named or linked a brand, and computing the brand's share. It clearly separates this from the sibling tools (feedback replies/submission), which are unrelated to share scoring, so an agent can route to 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?

It gives explicit when-to-use conditions (user pastes answers from ChatGPT/Perplexity/Google AI Mode/Claude and asks about naming, share, rivals, or remediation) and an explicit exclusion ('scores pasted text only and never queries an AI engine'), which tells the agent this is not a fetch/discovery tool.

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