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mcp-canva

mcp-canva

by bryankwandou
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

canva_brand_template_fields

Retrieve a Canva brand template's autofillable text, image, and chart fields by template ID. Use it to identify fillable slots before automating design updates.

Instructions

Show the autofillable fields (text/image/chart) of a brand template.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "brand_template_id": "<brand_template_id>"
    },
    "name": "canva_brand_template_fields"
  }
}

Input Schema

NameRequiredDescriptionDefault
brand_template_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. 'Show' implies a non-mutating read, but nothing is said about permissions, whether the brand template must exist, or how many fields are returned.

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?

A single front-loaded sentence with no filler; the purpose is stated before any modifier.

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

Completeness3/5

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

For a simple one-parameter read tool this covers the core purpose, but with no output schema the agent gets no hint about the returned field structure (names, types, identifiers) it would need to feed canva_autofill.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and there is one required parameter (brand_template_id) that the description never mentions. 'of a brand template' only loosely implies the ID selects the template; no format or validation detail is added.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Show') and resource ('autofillable fields of a brand template'), and even enumerates the field kinds (text/image/chart). It does not, however, distinguish itself from siblings like canva_autofill or canva_list_brand_templates, so an agent can't tell from the description alone when to prefer this over them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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

No when-to-use, prerequisite, or alternative guidance is given. The natural workflow (call this to discover fields, then pass them to canva_autofill) is left entirely to inference.

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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