Galatic.ai

Generate episode

generate_episode

Estimate, generate or resume an episode render, or check its progress. Estimate first. generate and resume spend Galatic.AI credits and start a background render; poll with action "status" (not get_job_status), which returns status, contentUrl and, on failure, canResume. seriesId is the flowId of the Flow Studio flow that holds the series (from list_flows).

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": {
      "action": "<action>",
      "seriesId": "<seriesId>",
      "episodeId": "<episodeId>",
      "context": "<context>",
      "llm_model": "<llm_model>"
    },
    "name": "generate_episode"
  }
}

Input Schema

Name	Required	Description
`action`	Yes
`context`	Yes	Explain 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."
`seriesId`	Yes
`episodeId`	Yes
`llm_model`	Yes	The 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.
`aspectRatio`	No
`videoProvider`	No
`conversation_id`	No	Pass 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.

Changed1 schema field changedOct 25, 2026

changedInput schema / properties / action / enum

Previous value: -[
-  "estimate",
-  "generate",
-  "resume"
-]New value: +[
+  "estimate",
+  "generate",
+  "resume",
+  "status"
+]

First observedOct 24, 2026

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already declare this is a non-readonly, non-idempotent, non-destructive, closed-world operation. The description adds material context annotations cannot express: generate and resume consume Galatic.AI credits, they start a background render, and status returns status, contentUrl and canResume on failure. It stops short of saying what estimate or generate themselves return, which is the main remaining gap.

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?

Four dense sentences, zero filler, with the recommended call order front-loaded and the sibling exclusion parenthetically attached where it is needed. Every clause carries operational information.

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

Completeness4/5

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

With no output schema, the description carries the return-value burden and covers the status response signature, including the failure case (canResume). For a 5-required-parameter tool that spends credits it is nearly complete, but it doesn't say what estimate returns (cost figure?) or whether aspectRatio/videoProvider matter for correctness.

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?

Schema coverage is only 38%, so the description must compensate, and it does for the two highest-stakes parameters: seriesId is defined as the flowId from list_flows, and the action enum values are given meaning plus ordering. aspectRatio and videoProvider are left entirely to the schema, keeping it from a 5.

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 opening sentence names a specific verb set (estimate, generate, resume, check progress) against a specific resource (an episode render), covering all four enum actions. It also distinguishes itself from get_job_status, so an agent can separate this from its closest sibling without opening either schema.

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?

Explicit sequencing is given: 'Estimate first,' then generate/resume, then poll with action 'status' (not get_job_status). The seriesId prerequisite is also routed — take the flowId from list_flows — so the agent knows how to obtain required input before calling.

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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