Scenario.com MCP Server

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions editing an image but does not clarify critical behaviors: whether this is a generative AI operation, if it modifies the original asset or creates a new one, potential rate limits, authentication needs, or output format. The description is too minimal to inform safe and effective use, especially for a complex tool with 17 parameters.

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 extremely concise—a single sentence with no wasted words. It is front-loaded and to the point, though this brevity contributes to its inadequacy in other dimensions. Every sentence (here, just one) earns its place by stating the core action, but it lacks necessary detail.

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?

Given the tool's complexity (17 parameters, no annotations, no output schema), the description is incomplete. It fails to explain the tool's purpose in context, behavioral traits, or usage scenarios. While the schema covers parameters, the description does not add value to help an agent understand when or how to use this tool effectively, making it insufficient for such a multifaceted operation.

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 high at 82%, so the schema already documents most parameters well. The description adds no parameter-specific information beyond the schema—it does not explain key parameters like 'prompt', 'image', or model-specific options. However, with high schema coverage, the baseline score is 3, as the description does not compensate but also does not detract from the existing documentation.

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 'Edit an image with a prompt' states the basic action but is vague and tautological—it essentially restates the tool name 'post-prompt-editing-inferences' without specifying what kind of editing occurs (e.g., generative AI-based transformation, inpainting, style transfer) or distinguishing it from sibling tools like 'post-img2img-inferences' or 'post-inpaint-inferences'. It lacks specificity about the resource being edited (an image asset) and the mechanism (prompt-driven AI editing).

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 no guidance on when to use this tool versus alternatives. With many sibling tools for image manipulation (e.g., 'post-img2img-inferences', 'post-inpaint-inferences', 'post-generative-fill-inferences'), there is no indication of this tool's specific role, prerequisites, or exclusions. It fails to help an agent choose appropriately among similar tools.

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