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Retry Failed Render

retry_step
Destructive

Resume a FAILED video on the same job, keeping its script and every edit -- follow get_job_status's next_action. A failed script is written again for free (it then comes back for review). A render that failed before or during clip rendering or stitching carries on to the finished video and spends credits like the original render (estimate_cost gives the maximum). After a content-policy block, rewrite the flagged scenes with edit_scene and pass content_edited=true. Not for a job that is still running or looks stuck.

Input Schema

NameRequiredDescriptionDefault
job_idYesThe video's job_id (from create_video, create_from_template or list_videos).
content_editedNoOnly after a content-policy failure: true once the flagged scene(s) were rewritten with edit_scene (the retry is refused otherwise, since it would be blocked again).
idempotency_keyNoA unique string for this request (e.g. a UUID). Reuse it when retrying: a repeat within 24h returns the first result instead of starting (and charging for) a second render.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Adds substantial behavior beyond annotations: which failures are free to retry, which spend credits, how estimate_cost relates to the maximum charge, and the content-policy prerequisite. These are meaningful side-effect and cost disclosures for a destructive, credit-spending 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?

Front-loads the core action, then concisely covers failure-mode branches, cost implications, and exclusions. Every sentence adds operational value without repetition.

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?

Given a 3-parameter mutation tool with no output schema, the description is complete enough: it explains eligibility, cost behavior, content-policy handling, and the key exclusion case.

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 reinforces content_edited usage after content-policy failures, but it does not add much semantics beyond what the schema already documents for job_id, content_edited, and idempotency_key.

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 action (resume a FAILED video) and resource/job scope, and explicitly clarifies the preserved script/edits. It distinguishes the tool from related recovery or regeneration actions by focusing on same-job resume.

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 explicit eligibility (FAILED jobs), points the agent to get_job_status's next_action, covers the content-policy path with edit_scene and content_edited=true, and explicitly excludes running or stuck jobs.

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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