Scenario.com MCP Server

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 mentions that extra space is filled based on context, which implies generative AI behavior, but doesn't disclose critical traits like whether this is a read-only or destructive operation, authentication needs, rate limits, or output format. For a complex image transformation tool with 16 parameters, this is a significant gap in transparency.

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—two sentences that directly state the tool's purpose and a key behavioral aspect. Every word earns its place with no redundancy or fluff, making it easy to parse quickly. It's appropriately sized for a tool with a clear primary function.

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 (16 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the generative AI nature, potential side effects, error conditions, or what the output looks like. While the schema covers parameter details, the description fails to provide the broader context needed for safe and effective use of this image transformation tool.

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 88%, so the schema already documents most parameters well. The description adds minimal value beyond the schema—it implies reframing involves resizing and context-based filling, which aligns with parameters like targetWidth/targetHeight and prompt, but doesn't explain parameter interactions or provide additional semantic context. Baseline 3 is appropriate given high schema 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?

The description clearly states the tool's purpose: 'Reframe a given image to new sizes' specifies the action and resource. It distinguishes from siblings by focusing on image reframing rather than other image operations like captioning, inpainting, or background removal. However, it doesn't explicitly differentiate from similar tools like 'post-restyle-inferences' or 'post-generative-fill-inferences' that might involve image modification.

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 minimal usage guidance. 'Extra space is filled based on the context' hints at when to use it, but it doesn't specify when to choose this tool over alternatives like 'post-restyle-inferences' or 'post-generative-fill-inferences'. No explicit when-not-to-use scenarios or prerequisites are mentioned, leaving the agent with little contextual direction.

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