Skip to main content
Glama

generate_image

Turn text prompts into images using a model catalog. Accepts optional aspect ratio, quality tier, and advanced style parameters. One integration works with all models.

Instructions

Generate an image from a text prompt using the MediaEngine model catalog. Use this for any image creation request: illustrations, photos, AI art, logos, icons, graphics, or visuals. Accepts a raw prompt and optional aspect ratio, quality tier, model id, and advanced style parameters. Quality tiers: basic/standard (Nano Banana — fast, inexpensive default), premium (GPT-Image — strongest prompt adherence and clean text rendering). One integration, every model: new models are added to the catalog server-side, so the same wiring keeps working. Style guidance: put the desired look in the prompt itself (e.g. "black and white archival photograph", "flat vector illustration") and bake negatives into the prompt ("no text, no words, no logos"). Use the intent-specific tools (generate_article_hero_image, etc.) when you know the content type.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sizeNoLegacy size hint; prefer aspect_ratio.
modelNoCatalog model id (see https://mcpmediaengine.com/models). Overrides quality tier. Default: nano-banana.
promptYesText description of the image to generate. Include the desired style in the prompt (e.g. "photorealistic", "watercolor", "pencil sketch") and constraints like "no text, no words, no logos".
qualityNoQuality tier: basic/standard (Nano Banana — fast default), premium (GPT-Image — text rendering, strict adherence) (default: standard).
face_swapNoUse face swap with reference images (default image model).
hires_fixNoHigh-resolution fix with super_resolution (default image model).
schedulerNoAdvanced: sampler/scheduler algorithm (default image model).
controlnetNoAdvanced: structural conditioning mode (default image model).
film_grainNoAdd film grain effect (default image model).
aspect_ratioNoOutput aspect ratio (default: 1:1).
face_correctNoApply face correction (default image model).
style_presetNoOptional style preset (default image model).
color_gradingNoOptional color grading preset (default image model).
inpaint_facesNoInpaint faces for better quality (default image model, requires super_resolution).
mask_image_urlNoURL of mask image for inpainting (default image model).
controlnet_txt2imgNoUse structural conditioning for txt2img (default image model).
controlnet_conditioning_scaleNoConditioning scale 0.0-1.0 (default image model).
Behavior4/5

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

No annotations provided, but the description explains model catalog updates, quality tiers, and style guidance. It omits cost/rate limits but adequately discloses behavior for a generation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is thorough and well-structured, front-loading purpose, then parameters, then usage guidance. Slightly long but no redundancy; every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, so description should explain return format (e.g., image URL). It covers usage well but misses output details and error handling. Adequate for 17-param tool with good schema coverage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds meaning beyond schema: explains quality tiers, style prompt tips, and clarifies that 'size' is legacy. With 100% schema coverage, it adds useful context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'generate' and resource 'image from a text prompt' using the MediaEngine model catalog. It also distinguishes from sibling tools by mentioning intent-specific tools like generate_article_hero_image.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Use this for any image creation request' and advises to use intent-specific tools when the content type is known, providing clear when-to-use and when-not-to-use guidance.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Tr1ckyMag1ca1/mcp-media-engine'

If you have feedback or need assistance with the MCP directory API, please join our Discord server