Skip to main content
Glama
Sign In
datawrapper-mcp

datawrapper-mcp

by palewire
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Get Chart Schema

get_chart_schema
Read-onlyIdempotent

Get the Pydantic JSON schema for a specific Datawrapper chart type to explore styling and configuration options before building charts.

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.

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": {
      "chart_type": "<chart_type>"
    },
    "name": "get_chart_schema"
  }
}

Input Schema

NameRequiredDescriptionDefault
chart_typeYesChart type to get schema for

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

Annotations cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description adds real value by enumerating what the schema exposes (types, enum values, defaults, descriptions) and clarifying the discovery workflow. Minor gap: no mention of output format/caching, but this is largely covered by the output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The workflow guidance is front-loaded well, but the tool description is padded with an unrelated banner ('⚠️ DATAWRAPPER MCP TOOL ⚠️ ... server integration') and a horizontal rule that add no selection value. The substantive content is efficient once past the boilerplate.

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?

For a single-parameter, read-only introspection tool with an output schema present, the description covers purpose, output contents, and next steps. Complete enough to call correctly; the only omission is pointing to the sibling list_chart_types for discovery of valid chart_type values.

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 the single parameter is already documented. The description adds no syntax or format detail beyond 'chart type', which means it does not meaningfully extend the schema.

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 ('Get the Pydantic JSON schema for a specific chart type') and explicitly positions itself as the primary discovery tool for styling/config options. This distinguishes it from list_chart_types and create_chart without ambiguity.

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?

Provides an explicit workflow ('Use this tool first to explore options') and points to external docs for follow-up. It does not explicitly state when NOT to use it vs alternatives like list_chart_types, so it stops short of 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools