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Bannerbear MCP by usefulapi

Create collection

bannerbear_create_collection
Destructive

Generate a collection of images from a template set (additive; CONSUMES API quota). Async: returns a pending collection whose uid you poll with bannerbear_get_collection (image_urls map populates on completion). API: POST /v2/collections.

Input Schema

NameRequiredDescriptionDefault
metadataNoArbitrary metadata string echoed back on the object.
webhook_urlNoURL Bannerbear will POST the finished collection to when done.
template_setYesTemplate set uid to render.
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.

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?

The description discloses traits not in the annotations: that the operation is additive, that it CONSUMES API quota, that it is asynchronous, and that it returns a `pending` collection whose uid must be polled while image_urls populates. That is rich behavioral context beyond the single destructiveHint annotation.

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 core action and cost warning, then the async behavior, then the API endpoint. No filler; every clause carries actionable information.

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?

Despite no output schema, the description explains the return shape (pending collection with uid, image_urls map on completion) and the required follow-up call. For an async create tool, the agent has everything needed to invoke and then retrieve the result.

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 coverage is 100%, so the schema already documents all four parameters (template_set, modifications, metadata, webhook_url). The description only implies template_set usage and adds no syntax or format detail beyond the schema, so the 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?

Specific verb ('Generate') plus resource ('a collection of images from a template set') and scope, immediately distinguishing it from bannerbear_create_image / create_video and other create siblings. An agent can tell what resource is produced 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 Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly describes the async workflow and routes the agent to bannerbear_get_collection for polling, which is clear operational context. It does not compare against the other create_* siblings or state when a collection is preferable to a single image, so it stops short of full when-to-use guidance.

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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