Generate images from text prompts using Flux models. Returns base64-encoded PNG.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | Image model (e.g. 'flux-schnell') | |
| prompt | Yes | Description of the image to generate | |
| num_steps | No | Number of inference steps. More steps = higher quality. Default 4 |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds useful context beyond the readOnlyHint annotation by specifying the return format (base64-encoded PNG). However, it does not disclose potential rate limits, costs, or model-specific behaviors, which the annotation does not cover. The annotation already ensures no mutation, so the additional output format info is valuable but not comprehensive.
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 two sentences, front-loaded with the action, and contains no filler. Every word contributes: the purpose, the model family, and the output format. It is concise and well-structured.
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 simplicity (3 well-described parameters, no output schema), the description is adequately complete. It states the return format, which is essential for the agent to handle the response. It does not cover every edge case, but the schema and annotation fill the essential gaps.
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 100%, so the baseline is 3. The description mentions 'Flux models' and 'text prompts' which map to the model and prompt parameters, but it adds no extra meaning beyond the schema's parameter descriptions. The schema already explains num_steps, so the description does not need to compensate.
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 uses a specific verb ('Generate') and resource ('images from text prompts') and clearly states the output format ('base64-encoded PNG'). It is distinct from sibling tools like ai_chat or ai_embeddings by focusing on image generation.
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 clearly implies the appropriate use case: generating images from text prompts. While it doesn't explicitly mention alternatives or exclusions, the context is unambiguous given the tool's name and purpose. The sibling context further differentiates it from chat, embedding, and transcription tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct capability: chat, embeddings, image generation, speech synthesis, transcription, currency exchange, translation, and weather. There is no overlap or ambiguity between them.
The five AI tools follow a consistent 'ai_' prefix pattern (ai_chat, ai_embeddings, etc.), but the three utility tools (currency, translate, weather) break this convention, creating a minor inconsistency.
With 8 tools, the server is well-scoped for a multi-purpose AI and utility toolkit. The count is neither too sparse nor overly heavy, and each tool has a clear role.
The toolset covers a broad range of AI modalities (text, embedding, image, audio) plus common utilities (currency, translation, weather). Minor gaps exist, such as video generation or web search, but these are not essential for the apparent scope.