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Single tool 'generate_image' has moderate documentation but significant gaps. Description is present (245 chars, within baseline 34-392 range) but lacks critical information about prerequisites, error states, and when to use it. Input schema exists with type and basic description, but lacks constraints (e.g., prompt length limits, quality guidelines). Output schema is documented in the docstring but not formally structured in the tool registration. No parameter-level descriptions for validation rules or format expectations. Tool lacks error guidance for common failures (timeout, out-of-memory, invalid prompts). No security considerations documented despite the tool performing computationally expensive operations that could be abused. Overall definition is functional but below production grade.
Generate an image based on a text prompt using Stable Diffusion. Args: prompt: Text description of the image to generate Returns: Dictionary containing the image in a format compatible with MCP tools Usage: generate_image("A futuristic cityscape at sunset")
Output schema not formally defined in tool registration. Docstring describes return dict with fields (type, format, url, width, height, etc.), but no JSON Schema struct is visible in the @mcp.tool() decorator. LLMs cannot reliably plan downstream tool calls without formal output type definitions.
Input parameter 'prompt' lacks validation constraints and format guidance. Description says 'Text description' but does not specify: min/max length, allowed characters, guidance on what makes a good prompt, or what happens with extremely long or malformed input.
Error handling provides generic McpError responses (INVALID_PARAMS, INTERNAL_ERROR) but does not guide LLM on recovery. Examples: 'Request error' and 'Failed to generate image' lack actionable next steps. Per pattern:recovery-guide, errors should tell the agent what to try next (e.g., 'Prompt too long, try under 77 tokens' or 'Service timeout, retry in 5 seconds').
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 49 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 36 | - | v1 |
No documentation of prerequisites or resource requirements. Tool depends on IMAGE_GEN_URL environment variable and a running Flask service, but the tool description does not mention these dependencies. LLMs cannot diagnose failures if they don't know the tool requires a backend service to be running.
No documentation of permissions, resource limits, or rate-limiting guidance. Tool performs expensive GPU-based inference; agents could spam calls and exhaust resources. Per pattern:scope-declaration, tool should declare its resource cost and whether rate limits apply.
Docstring includes example usage in the description text. The example 'generate_image("A futuristic cityscape at sunset")' should be removed; use formal constraints instead.
No idempotency guarantee or confirmation step. Tool generates images with side effects (writes files, consumes GPU). Per pattern:idempotent-operation, agents retry on ambiguous failures, repeated calls with same prompt will generate multiple distinct images, risking resource exhaustion and unexpected behavior.