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Budka MCP

Budka MCP

by curlysasha
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

Generate Image

generate_image

Generates images from text prompts using Budka's CyberPhotoBooth API, supporting multiple models and aspect ratios with asynchronous job processing.

Instructions

Generates an image using the Budka (CyberPhotoBooth) API. Async: submits a job then polls for result. Statuses: queued → processing → completed/failed. If timed out or interrupted, use check_job with the job_id to retrieve the result without creating a new job. Requires BUDKA_API_KEY.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "prompt": "<prompt>"
    },
    "name": "generate_image"
  }
}

Input Schema

NameRequiredDescriptionDefault
modeNoGeneration mode. edit2_text (default) — Flux Klein model. nano-banana_text — Nano Banana model, use when specifically requested.edit2_text
ratioNoAspect ratio for the generated image. IMPORTANT: supported ratios differ by model. Use get_aspect_ratios(mode) or get_models to check. Flux Klein: 1:1,2:3,3:4,5:8,9:16,9:19,9:21,3:2,4:3,8:5,16:9,19:9,21:9. Nano Banana: 1:1,2:3,3:4,4:5,9:16,3:2,4:3,5:4,16:9,21:9.1:1
promptYesThe image generation prompt in English. Use create_prompt tool first to craft an optimal prompt.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries full burden. It discloses async behavior ('submits a job then polls'), status lifecycle, recovery instructions, and API key requirement. However, it does not mention rate limits, costs, or the exact return format, which are notable omissions for a mutation tool.

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

Conciseness5/5

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

The description is three sentences, front-loaded with the core purpose, followed by async flow and recovery. Every sentence adds essential behavioral or operational information with no fluff.

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?

While the description covers the async flow and error recovery, it lacks explanation of the return value (there is no output schema) and doesn't mention in the description itself that one should verify supported aspect ratios or craft prompts (though these are in schema). Given the tool's complexity and absence of output schema, this gap leaves the agent uncertain about what the 'result' contains.

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

Parameters3/5

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 itself adds no parameter-specific details, but the schema already provides rich descriptions for mode, ratio, and prompt, including supported ratios per model and a reference to create_prompt, thus fully compensating for the description's silence.

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 'Generates an image using the Budka (CyberPhotoBooth) API', specifying the action and resource. It distinguishes from siblings like check_job (for retrieving results) and get_models (for listing models), making the purpose unambiguous.

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

Usage Guidelines4/5

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

The description provides clear guidance on when to use this tool vs check_job in case of timeout/interruption ('If timed out or interrupted, use check_job... without creating a new job'). It doesn't explicitly mention using get_aspect_ratios or create_prompt, but those are suggested in the schema's parameter descriptions, offering context beyond the main description.

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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