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vuluu2k

WebCake Storefront MCP

by vuluu2k

create_collection

Create a new data collection (custom table) with system columns and optional custom fields. Define column types, defaults, and references to structure your storefront data.

Instructions

Create a new collection (custom data TABLE). It starts with the system columns (id/inserted_at/updated_at/creator_id); pass columns to add custom fields. NOTE: to WRITE records into it, use an HTTP function (webcake-data: db.model(table).create({...})) — the dashboard has no direct record-insert API. See get_http_function for the SDK guide.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesDisplay name.
columnsNoCustom columns to add, e.g. [{name:'email',type:'text'},{name:'amount',type:'integer'}].
table_nameNoTable name (snake_case, unique). Defaults to name.
Behavior4/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It reveals that collections start with system columns and highlights the platform limitation about no direct record insertion. However, it does not mention permission requirements, potential errors, or reversibility, so it stops short of full transparency.

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?

The description is three sentences with no wasted words. The core action is front-loaded, followed by a useful note and a cross-reference. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the essential creation workflow and importantly warns about the record-write limitation, which is critical for the overall data management context. It does not describe the return value or error scenarios, but given the schema's completeness, this is sufficient.

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?

The input schema has 100% coverage with descriptions for all three parameters (name, columns, table_name). The description's mention of 'pass columns to add custom fields' largely repeats what the schema already says, adding no new parameter-level details or constraints.

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 opens with 'Create a new collection (custom data TABLE)', using a specific verb and resource. It distinguishes from sibling tools by clarifying that a collection is a custom data table, and the parenthetical about system columns helps define its behavior.

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?

The description explicitly states when to use this tool (to create a collection) and when not to: 'to WRITE records into it, use an HTTP function... the dashboard has no direct record-insert API'. It even points to get_http_function as the alternative, giving clear usage 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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