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palewire

datawrapper-mcp

by palewire

get_chart_schema

Read-onlyIdempotent

Retrieve the JSON schema for a chart type to explore all styling and configuration options, including properties, types, defaults, and enum values.

Instructions

⚠️ DATAWRAPPER MCP TOOL ⚠️ This is part of the Datawrapper MCP server integration.


Get the Pydantic JSON schema for a specific chart type. This is your primary tool for discovering styling and configuration options.

The schema shows:

  • All available properties and their types

  • Enum values (e.g., line widths, interpolation methods)

  • Default values

  • Detailed descriptions for each property

WORKFLOW: Use this tool first to explore options, then refer to https://datawrapper.readthedocs.io/en/latest/ for detailed examples and patterns showing how to use these properties in practice.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chart_typeYesChart type to get schema for

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already indicate readOnly=true and idempotent=true, so the description does not need to restate safety. It adds behavioral context by explaining what the schema reveals (properties, types, enums, defaults, descriptions) and how it fits into the workflow. No contradictions.

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 is somewhat verbose with formatting and emoji, but every sentence serves a purpose (clarify purpose, list schema contents, suggest workflow). It could be slightly more concise, but the structure is clear and front-loaded.

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?

Given the tool's simplicity (one param, read-only, output schema exists), the description is complete. It explains what the tool does, what the response contains, and how to use it in a workflow. No gaps remain.

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

Parameters5/5

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

There is only one parameter (chart_type) with 100% schema coverage. The description adds value by explaining that the output contains properties, types, enums, defaults, and descriptions, which enriches the agent's understanding beyond the schema alone.

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 clearly states it gets the Pydantic JSON schema for a specific chart type. It distinguishes itself from sibling tools by being the primary tool for discovering styling and configuration options. The verb 'Get' and resource 'chart schema' are specific and unambiguous.

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 provides a workflow: 'Use this tool first to explore options'. It implies when to use (before other operations) and directs to documentation for detailed examples. However, it does not explicitly state when not to use it or list alternatives, so it gets a 4.

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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