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by com.thefilmradar
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Upscale Clip

lab_upscale

Enlarge an EXISTING clip (Topaz, passed through fal): same shot, same length, same sound — more pixels. There is no prompt here; nothing is re-imagined, the frames are scaled. The source is the clip of a generated-video row. Both the length and the source height are measured server-side before anything is charged, and a source too tall for the larger factor is refused rather than run. ASYNCHRONOUS: a new video row comes back as pending; poll it with lab_job_status. Costs credits when called without get_cost.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
faktorNoHow many times larger, per side. Omit for the smaller one. Whichever you pick, the RESULT has to stay inside the published rate tiers — a source too tall for the factor you asked for is refused by name, with the ceiling in the message, instead of billing a run nobody can price. What the tier costs is in lab_list_models and in the preflight, not here.
get_costNo`true` returns what upscaling THIS clip costs (`cost_generations`, measured server-side from its length and the height of the RESULT, exactly what the run would charge) plus the published rate per output second per tier and your balance — WITHOUT scaling and without spending anything. For a clip that is still pending there is no total yet: then `cost_generations` is null and `preis_grund` says why. Which tiers are measured and which are catalog values is the same distinction lab_list_models makes.
video_idYesGenerated-video id (from lab_animate/lab_job_status/lab_review_queue) — positive integer as string. The row must already have its clip; a run still pending is refused, and nothing is charged for asking.
max_creditsNoThe most you agree to pay — pass the amount from get_cost that the person confirmed. If the measured price is higher when the run starts, it is refused before anything is charged.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / additionalProperties
      Added value: +false
  2. Changed2 schema fields changed
    • changedInput schema / properties / get_cost / description
      Previous value: -"`true` returns the published rate per output second per tier and your balance WITHOUT scaling and without spending anything — NOT a total. Two things decide the amount and both are measured server-side: the length of the source, and the height of the RESULT (source height times factor) which picks the tier. The answer also says which tiers are measured and which are catalog values — the same distinction lab_list_models makes. The real amount comes back with the actual call."New value: +"`true` returns what upscaling THIS clip costs (`cost_generations`, measured server-side from its length and the height of the RESULT, exactly what the run would charge) plus the published rate per output second per tier and your balance — WITHOUT scaling and without spending anything. For a clip that is still pending there is no total yet: then `cost_generations` is null and `preis_grund` says why. Which tiers are measured and which are catalog values is the same distinction lab_list_models makes."
    • addedInput schema / properties / max_credits
      Added value: +{
+  "description": "The most you agree to pay — pass the amount from get_cost that the person confirmed. If the measured price is higher when the run starts, it is refused before anything is charged.",
+  "maximum": 9007199254740991,
+  "minimum": 0,
+  "type": "integer"
+}
  3. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Goes well beyond the annotations (readOnlyHint=false, destructiveHint=false), disclosing the async pending-row lifecycle, server-side measurement of length and height before charging, refusal of too-tall sources, the credit cost model, the max_credits guard, and that get_cost is free and does not scale. A mutation-ish tool with real cost is fully explained.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core action and its limits, and every sentence carries information. It is dense rather than padded, though the Topaz/fal detail and the cost framing re-echo points already made in the schema, adding mild length.

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, so the description correctly carries the return behavior (a new pending video row), the polling path (lab_job_status), the cost/preflight flow, and the refusal conditions. Nothing an agent needs to invoke it safely and correctly is missing.

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% and the parameter descriptions are already exhaustive per-parameter, so the description's job is limited. It adds only light framing (cost lives in lab_list_models and the preflight, not here) and does not sharpen the faktor tiers or video_id preconditions beyond what the schema states. Baseline 3 is appropriate.

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 precise verb+resource — 'Enlarge an EXISTING clip (Topaz, passed through fal)' — and immediately scopes it as a pixel-scaling operation, not a generation: 'nothing is re-imagined, the frames are scaled.' This cleanly separates it from generation/restyle siblings like lab_generate and lab_transform.

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?

Gives clear context (source must be the clip of a generated-video row, pending runs are refused) and routes the agent to lab_job_status for polling and lab_list_models for tier pricing. It signals that get_cost should precede a paid call ('Costs credits when called without get_cost') but never states that explicitly as a when-to-use rule, and doesn't name a competing upscaling alternative.

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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