get_posterior_plots

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

With no annotations at all, the description carries the full behavioral burden. It discloses the output format (base64-encoded PNG/SVG), where images go (evidence envelope and MCP resources at a specific URI pattern), and the prerequisite that the model must be 'fitted and approved.' This is substantial coverage of side effects and constraints, though it omits error cases or permission requirements.

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 is a single, dense sentence that front-loads the main action and then packs in output format, caching, and supported types. No filler words. It earns its place, though it could be split into two sentences for readability without losing concision.

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?

Despite zero schema coverage and no annotations, the description conveys the tool's purpose, output format, caching side effect, and valid plot types. It falls short on explaining the format and model_id parameters in detail, and it does not clarify what 'approved' means or how failures are reported. For a tool with only three simple parameters, this is adequate but not complete.

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. It explicitly lists the allowed plot_types, which maps directly to the plot_types parameter. However, it does not explain model_id (though its role is obvious from context) or format (though the output description implies it). The description partially covers parameters but leaves some to inference.

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 specific verb and resource: 'Generate posterior visualization plots from a fitted and approved MMM.' It enumerates the exact plot types supported, which sharply distinguishes it from sibling tools like get_response_curves or get_channel_contributions. The purpose is unambiguous and not a tautology.

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 gives no explicit guidance on when to use this tool versus alternatives. It implies a precondition (fitted and approved model) but does not mention when not to use it, nor does it point to any sibling for other plot types. An agent must infer usage from the tool name and sibling list.

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