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

With no annotations provided, the description carries the full burden and excels. It discloses metadata stripping, transparency handling, never upscaling, pricing behavior (free if not smaller), relevant response headers (X-Pictomancer-*), C2PA not carried over, and delivery method semantics (presigned URL credentials never stored).

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 long but well-structured with headers for responses and explicit field lists. It front-loads the core purpose and behavior, then adds delivery specifics. The response examples are verbose and partly redundant with the schema, but they aid comprehension. Overall, it earns its length.

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 tool with 7 parameters, multiple delivery modes, and no output schema, the description covers all major aspects: input formats, output formats, quality control, pricing, metadata handling, security considerations, and delivery options. The response examples give concrete return types. It is complete for its complexity.

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 coverage is 86%, so baseline is 3. The description adds meaningful context beyond the schema: the relationship between q and quality_target (mutually exclusive, surcharge), default format, strip default rationale (C2PA invalidation), and delivery security details. This adds value but doesn't fully document every parameter nuance, so a 4 is appropriate.

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+resource: 'Optimize an AI-generated image for the web', then clarifies it as the post-generation step and lists target formats and behaviors. It clearly differentiates from siblings like compress_image and convert_image by tying it to AI-generated sources and format-specific optimizations, though it doesn't explicitly name alternatives.

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 states when to use: 'The step after image generation' and mentions specific generators (gpt-image, DALL-E, etc.). It also notes 'Same price as convert' and describes the quality_target surcharge. However, it doesn't explicitly say when NOT to use this vs compress_image or convert_image, so the guidance is mostly implicit.

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