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datawrapper-mcp

datawrapper-mcp

by palewire
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Get Chart

get_chart
Read-onlyIdempotent

Retrieve a Datawrapper chart's configuration, metadata, type, and URLs to inspect styling, adapt to a new dataset, or clone chart styling.

Instructions

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

Get information about an existing Datawrapper chart, including its complete configuration, metadata, and URLs.

The returned configuration can be used to:

  • Understand how a chart is styled and configured

  • Adapt the configuration to a new dataset

  • Clone a chart's styling to create similar visualizations

Returns:

  • chart_id: The chart's unique identifier

  • title: Chart title

  • type: Simplified chart type name (bar, line, stacked_bar, etc.) - same format as used in list_chart_types and create_chart

  • config: Complete Pydantic model configuration including all styling, colors, axes, tooltips, annotations, and other properties

  • public_url: Public URL if published

  • edit_url: Editor URL

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_id": "<chart_id>"
    },
    "name": "get_chart"
  }
}

Input Schema

NameRequiredDescriptionDefault
chart_idYesID of the chart to retrieve
access_tokenNoOptional Datawrapper API token. When provided, uses the caller's account. When omitted, falls back to the server's DATAWRAPPER_ACCESS_TOKEN env var.

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint, so the safety profile is fully covered by structured data. The description adds little beyond that: it does not mention rate limits, permission/account requirements beyond what the schema says, or failure modes. It essentially restates that this is a non-mutating fetch.

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

Conciseness2/5

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

The first two lines ('⚠️ DATAWRAPPER MCP TOOL ⚠️ / This is part of the Datawrapper MCP server integration. ---') are pure boilerplate that earn no place, and the entire 'Returns:' block duplicates the output schema. The genuinely useful purpose statement is buried below the noise rather than front-loaded.

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?

An output schema exists, so the description needn't restate return fields — yet that is most of what it does. For a simple read-only get with rich annotations and full schema coverage, the minimum is met, but the description misses the one thing that would add value: disambiguating from siblings like get_chart_schema and update_chart.

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% and both parameters (chart_id, access_token) are already documented in the schema, including the env-var fallback behavior. Baseline 3 applies since the description adds no syntax, format, or constraint detail beyond the structured fields.

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 and resource ('Get information about an existing Datawrapper chart') and enumerates what comes back (configuration, metadata, URLs). It is clear on its own, but never names or contrasts with siblings like get_chart_schema, so an agent can't tell from the text alone when this is the right fetch tool.

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

Usage Guidelines3/5

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

The 'returned configuration can be used to...' list implies downstream use cases (styling, adapting to a new dataset, cloning), which is helpful implied context. However there is no 'use this when X, use get_chart_schema when Y' guidance and no exclusions, so selection vs. alternatives is left 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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