Galatic.ai

Publish episode

publish_episode

Destructive

Mark an episode as published (status "published", publishedAt set) and return it. This posts nothing and makes nothing public; it LOCKS the episode: scene re-makes and music changes are refused afterwards, and there is no unpublish. The route does not check that the episode has rendered. Use only when the user is sure no more re-makes are wanted; use episode_social_copy for captions. 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": {
      "seriesId": "<seriesId>",
      "episodeId": "<episodeId>",
      "context": "<context>",
      "llm_model": "<llm_model>"
    },
    "name": "publish_episode"
  }
}

Input Schema

Name	Required	Description
`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.
`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.

First observedOct 24, 2026

TDQS

A4.5/5.0

Behavior3/5

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

The description adds genuinely important context beyond the annotations: it explains what is actually destroyed (re-makes and music changes are refused afterwards), that the action is irreversible ("there is no unpublish"), and that the route does not verify rendering. However, most of this reinforces rather than extends the destructiveHint=true / idempotentHint=false annotations, and it omits failure modes and the shape of the returned episode. Solid but not exhaustive behavioral disclosure.

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?

Front-loads the core action and state change, then immediately follows with the non-obvious behavioral constraint and the sibling routing. Every sentence carries distinct information — no filler, no repetition of the title or schema.

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?

For an irreversible mutation with no output schema, the description covers the state transition, irreversibility, what becomes blocked, the missing render validation, usage preconditions, a sibling alternative, and the ambiguous key parameter. An agent has everything needed to decide and invoke correctly.

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 a middling 60%, and the description compensates for the most ambiguous parameter by defining seriesId as the flowId of the Flow Studio flow holding the series, with a pointer to list_flows. episodeId is left to the schema, but its name is self-explanatory, and the analytics-only parameters (context, llm_model, conversation_id) are already documented in the schema.

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?

States a specific verb and resource ("Mark an episode as published") plus the concrete state change it produces (status "published", publishedAt set). It also names the sibling it is not (episode_social_copy) and clarifies that "publish" here does not mean making anything public, removing the main ambiguity an agent would face.

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?

Gives an explicit precondition ("Use only when the user is sure no more re-makes are wanted") and an explicit alternative with its condition ("use episode_social_copy for captions"). Nothing about when to select this tool is left to inference.

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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