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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly states the operation is read-only and consumes zero credits, explains that include_responses=true adds long raw AI responses, and discloses that a null audit means no completed audit exists. This is solid coverage of non-obvious behavior, though it omits authentication or error details.

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 front-loads the main purpose and then adds the most relevant operational notes about credits, parameter behavior, and null results. It is a single dense block rather than cleanly separated sentences, but every clause adds useful information and nothing is wasted.

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 simple read-only retrieval with two parameters and no output schema, the description conveys the returned content, the meaning of a null result, and the effect of the optional parameter. It does not describe the response format in detail, but the content list and null-handling note give an agent enough to understand what to expect.

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 only 50%, with include_responses lacking a schema description. The description compensates by explaining that setting include_responses=true adds raw AI responses and that they can be long. project_id is already documented in the schema as coming from list_projects, so the main semantic gap is addressed.

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 identifies the tool's purpose: retrieving the latest completed GEO Audit for a project, including how ChatGPT, Gemini, and Perplexity describe the brand. The parenthetical content list adds specificity. It does not explicitly distinguish itself from sibling audit tools like get_audit_overview or get_ai_visibility, so it stops short of a 5.

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?

The description provides useful usage context: the tool is read-only, costs zero credits, and the audit itself is launched from the app. It also explains the meaning of a null audit. However, it does not explicitly state when to prefer this tool over alternatives such as get_ai_visibility or get_audit_overview, so usage guidance is implied rather than explicit.

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