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Bannerbear MCP by usefulapi

Create video

bannerbear_create_video
Destructive

Generate a video from a video template (additive; CONSUMES API quota). Async: returns a pending video whose uid you poll with bannerbear_get_video (video_url populates on completion). API: POST /v2/videos.

Input Schema

NameRequiredDescriptionDefault
metadataNoArbitrary metadata string echoed back on the object.
webhook_urlNoURL Bannerbear will POST the finished video to when done.
modificationsNoLayer overrides applied to the template. Array of objects, one per layer, each keyed by `name` (the layer name) plus the props to set, e.g. { name: "title", text: "Hello", color: "#FF0000" } or { name: "photo", image_url: "https://..." }. Only include layers you want to change.
video_templateYesVideo template uid to render.
input_media_urlNoURL of the source media (e.g. an audio/video file) to build from.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Goes well beyond the single destructiveHint annotation: discloses that the call is additive, that it consumes API quota, that the response is a `pending` video with a uid, that video_url only populates on completion, and that polling is done via a named sibling. These are the behavioral facts an agent actually needs to avoid misusing the result.

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?

Three dense sentences, front-loaded with the action and cost signal, then the async contract, then the endpoint. No filler and nothing redundant.

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?

With no output schema, the description compensates by explaining the return shape (pending video + uid) and how completion is detected. Combined with full schema coverage, an agent has everything needed to call and follow up 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 every parameter is already documented in the schema. The description implies video_template via "from a video template" but adds no syntax or constraint detail for modifications, webhook_url, metadata, or input_media_url, so the schema carries the load.

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 ("Generate a video from a video template") and scopes it apart from bannerbear_create_image/create_screenshot by naming the template type. The endpoint (POST /v2/videos) reinforces the resource unambiguously.

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 operational context — async creation and the need to poll with bannerbear_get_video — which tells the agent how this tool fits into a workflow. It does not explicitly contrast when to pick this over bannerbear_create_image, but the "video template" qualifier makes the boundary inferable.

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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