ai-content-drop

Run generation card

run_generation_card

DestructiveIdempotent

Render a generation card: its prompt and model become a video or image job whose result lands in the card. Requires a quote_id from estimate_credit_cost for the card's model and kind (quantity 1), taken with the card's duration and resolution when it has them: per-second models are priced from those, and the quote is verified against the card. A card added with from_card_id and no image_url starts from its source card's finished image; until that card is done the run is refused with nothing charged. Credits are charged only on success; a retry with the same quote_id returns the same job. SIGN-IN REQUIRED: connect this server with OAuth (the host prompts for it), or add an API key header "Authorization: Bearer acd_live_…" created at https://aicontentdrop.com/settings/integrations.

Input Schema

Name	Required	Description
`card_id`	Yes	A generation card with a model and a prompt.
`quote_id`	Yes	The quote_id from estimate_credit_cost for this card's model and kind, quoted with the card's duration and resolution when it has them. Required.
`revision`	No	The board revision this change is based on (from the last board you saw). Optional: when omitted the tool reads the current one first.
`canvas_id`	Yes	The board id, from list_canvases or create_canvas.

Output Schema

Name	Required	Description
`job`	Yes
`card`	Yes
`canvas`	Yes
`next_step`	Yes
`credits_quoted`	Yes	The ceiling fixed by the quote.

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedOct 8, 2026

removedOutput schema / properties / canvas / properties / updated_at

Removed value: -{
-  "type": [
-    "string",
-    "null"
-  ]
-}

changedOutput schema / properties / canvas / required

Previous value: -[
-  "id",
-  "name",
-  "revision",
-  "card_count",
-  "updated_at",
-  "link",
-  "cards",
-  "edges"
-]New value: +[
+  "id",
+  "name",
+  "revision",
+  "card_count",
+  "link",
+  "cards",
+  "edges"
+]

Changed2 schema fields changedSep 23, 2026

addedOutput schema / properties / canvas / properties / edges / items / description

Added value: +"A connection drawn from one card to the next: { id, source, target, label? }. data.role start_image means the source card's finished image is the target card's start frame."

addedOutput schema / properties / canvas / properties / edges / items / properties

Added value: +{
+  "id": {
+    "type": "string"
+  },
+  "label": {
+    "description": "Text drawn on the line, when it has one."
+  },
+  "source": {
+    "description": "The card the connection starts from.",
+    "type": "string"
+  },
+  "target": {
+    "description": "The card it leads to.",
+    "type": "string"
+  }
+}

AddedSep 11, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Beyond annotations, it discloses that credits are charged only on success, that the quote is verified against the card, that a from_card_id run is refused with nothing charged until the source image is done, and that retrying with the same quote_id returns the same job. These are exactly the expensive, mutation-adjacent behaviors an agent needs to understand before calling a destructive, open-world tool.

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?

The description front-loads the core action and then layers prerequisites, failure behavior, billing, idempotency, and sign-in details. It is dense but appropriate for a charge-incurring, open-world operation, with only minor repetition around quote verification.

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?

The description covers authentication, required quote provenance, source-card dependency, billing timing, retry idempotency, and failure behavior. With a rich input schema, an output schema, and annotations already declaring safety traits, an agent has everything needed to call this tool correctly.

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 card_id, quote_id, revision, and canvas_id are already documented in the input schema. The description reinforces the quote_id requirements and adds context about duration and resolution, but it largely repeats what the schema already provides and adds no meaning for revision.

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 description uses a specific verb and resource: it renders a generation card by turning its prompt and model into a video or image job whose result lands in the card. This distinguishes the tool from direct sibling generators such as generate_image and generate_video, which create output without operating on an existing card.

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?

The description gives clear prerequisites and conditions: the card must have a model and prompt, the caller needs a quote_id from estimate_credit_cost, and source-card runs require the source image to be finished. It does not explicitly contrast when to use this tool versus generate_image or generate_video, so it stops short of a full routing rule.

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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