Framevine

Edit video

edit_video

Edit an existing completed video node with a text instruction. prompt describes the change and what should stay unchanged. modelKey must be a video model from list_models that supports video editing. Spends credits; use estimate_cost first if unsure. Returns immediately with a new nodeId and status, plus the parent node it edited so you can confirm it was the right one; poll get_generation_status until it is COMPLETED or FAILED. If the user's intent is ambiguous across branches, call get_thread and ask which node before spending credits.

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": {
      "threadId": "<threadId>",
      "nodeId": "<nodeId>",
      "prompt": "<prompt>",
      "modelKey": "<modelKey>"
    },
    "name": "edit_video"
  }
}

Input Schema

Name	Required	Description
`nodeId`	Yes	Completed video node to edit.
`prompt`	Yes	The change to make; also say what should stay unchanged.
`modelKey`	Yes	Video model key from list_models that supports video editing.
`threadId`	Yes	Thread containing the node.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 23, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Annotations only say it's a non-idempotent, non-destructive mutation; the description adds critical behavior the agent cannot infer: it spends credits, returns immediately with a new nodeId and status, echoes the parent node, and requires async polling. That is substantial value beyond structured fields.

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?

Dense but front-loaded: the core edit action comes first, followed by model/credit constraints, then return shape and polling, then the ambiguity fallback. Every clause adds operational value with no filler.

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?

No output schema exists, and the description fully covers the return shape (new nodeId, status, parent node) and the follow-up polling step, plus credit/precondition context. Nothing an agent needs to invoke and follow through is 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?

Schema coverage is 100%, so baseline is 3, but the description adds real meaning: prompt should state both the change and what stays unchanged, and modelKey must be a video model supporting editing. These constraints aren't captured in the schema descriptions, though threadId/nodeId semantics are left to 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 (edit) and a precise resource (an existing completed video node) with a text instruction, and clearly distinguishes itself from generate_video, extend_video, and edit_image by scope ('completed video node'). An agent can route to it without opening the 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?

Explicitly names prerequisites and alternatives: modelKey must come from list_models, run estimate_cost first if unsure, call get_thread and ask which node when intent is ambiguous, and poll get_generation_status until COMPLETED/FAILED. This is a full when-to-use/when-not playbook.

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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