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Generate video

generate_video

Generate a video from a prompt, optionally from a start image. model and prompt are both required by the server (400 without either). With imageUrl the video is image-to-video, starting from that frame. Spends Galatic.AI credits. Returns a job id; poll get_job_status with kind "video".

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>",
      "context": "<context>",
      "llm_model": "<llm_model>"
    },
    "name": "generate_video"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelNoA video model id from list_models. Required by the server (400 without it).
styleNoOptional style preset applied to the prompt.
promptYesWhat the video shows. Required by the server.
contextYesExplain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
imageUrlNoStart frame for image-to-video: an upload_media publicUrl or an image from your library.
settingsNoModel-specific options, passed through to the model.
llm_modelYesThe exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
resolutionNoOutput resolution such as "720p" or "1080p". Not every model accepts every resolution.
aspectRatioNoAspect ratio such as "16:9" or "9:16". Not every model accepts every ratio.
conversation_idNoPass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed7 schema fields changed
    • addedInput schema / properties / aspectRatio / description
      Added value: +"Aspect ratio such as \"16:9\" or \"9:16\". Not every model accepts every ratio."
    • addedInput schema / properties / imageUrl / description
      Added value: +"Start frame for image-to-video: an upload_media publicUrl or an image from your library."
    • addedInput schema / properties / model / description
      Added value: +"A video model id from list_models. Required by the server (400 without it)."
    • addedInput schema / properties / prompt / description
      Added value: +"What the video shows. Required by the server."
    • addedInput schema / properties / resolution / description
      Added value: +"Output resolution such as \"720p\" or \"1080p\". Not every model accepts every resolution."
    • addedInput schema / properties / settings / description
      Added value: +"Model-specific options, passed through to the model."
    • addedInput schema / properties / style / description
      Added value: +"Optional style preset applied to the prompt."
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

The description is highly transparent regarding operational behavior beyond the schema. It mentions the service cost ('Spends Galatic.AI credits') and explicitly details the output behavior (returns a job id, requires polling). This is critical context for a long-running operation.

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 extremely efficient. It front-loads the primary function and efficiently groups all operational constraints (requirements, cost, asynchronous output) into a dense, highly actionable paragraph without unnecessary padding.

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

Completeness5/5

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

Considering the complexity of a video generation tool, this definition covers all necessary bases: inputs required by the backend, different modes of operation (text/image), and the entire lifecycle of the call (job ID returned, polling required). No critical pieces of execution context are missing.

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?

Although the schema coverage is 100%, the description adds semantic value by explaining the function of specific parameters, such as clarifying that 'With imageUrl the video is image-to-video, starting from that frame.' This helps the agent understand the intent behind the parameters.

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 primary function: generating video. It differentiates between standard text-to-video (using a prompt) and image-to-video (using the imageUrl parameter), which is vital given the large list of sibling generation tools.

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?

It provides clear guidance on prerequisites by stating that 'model and prompt are both required by the server (400 without either).' It guides the agent to use 'get_job_status' for asynchronous results, which is key to correct usage flow.

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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