Flux Schnell Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'image_generation' has a clear, distinct purpose that cannot be confused with any other tool in this server.
Naming Consistency5/5The single tool name 'image_generation' follows a clear noun_verb pattern. With only one tool, there is no inconsistency to evaluate, and the naming convention is straightforward and appropriate for its function.
Tool Count2/5A single tool for an image generation server feels thin and limited in scope. While it covers the core functionality, typical image generation servers might include additional tools for variations, editing, or different models. This minimal set may restrict agent capabilities.
Completeness3/5The server provides basic image generation, but lacks tools for related operations like image editing, style transfer, or batch processing. The surface is functional but incomplete for a comprehensive image generation domain, potentially causing agent workarounds.
Average 2.6/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior2/5
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 states the basic action (generation) and return format (base64), but lacks critical behavioral information such as rate limits, processing time, quality expectations, model details, or error conditions. For a generative AI tool with zero annotation coverage, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief but has structural issues. It front-loads the core purpose, but the parameter documentation is inconsistent with the actual schema. The bilingual nature (English description with Chinese parameter documentation) creates confusion. While concise, the structural problems reduce its effectiveness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For an image generation tool with 4 parameters, 0% schema description coverage, no annotations, and no output schema, the description is incomplete. It doesn't explain the generation process, quality factors, limitations, or what the base64 output represents. The parameter mismatch between description and schema creates additional confusion.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does 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 documents 'prompt' and 'image_size' parameters, but the input schema actually has 'prompt', 'image_width', 'image_height', and 'seed' - with 'image_size' not matching the schema's separate width/height parameters. This creates confusion and doesn't adequately cover the 4 parameters, especially missing 'seed' entirely.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with 'Generate an image from a prompt' - a specific verb ('Generate') and resource ('image'). It distinguishes itself by focusing on image generation from text prompts. However, without sibling tools, it doesn't need to differentiate from alternatives, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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. It doesn't mention any prerequisites, limitations, or typical use cases. The only contextual information is the parameter documentation, which doesn't constitute usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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