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datawrapper-mcp

by palewire
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Python
Hybrid

Update Chart

update_chart
Idempotent

Modify an existing Datawrapper chart's data, styling, or configuration fields like title, colors, and axes. Chart type cannot be changed; create a new chart for that.

Instructions

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

Update an existing Datawrapper chart's data or configuration using Pydantic models.

⚠️ IMPORTANT LIMITATION: You CANNOT change the chart type with this tool. Chart types are immutable once created. To change from one chart type to another (e.g., column → stacked_bar, or line → area), you must create a new chart instead.

WHAT YOU CAN UPDATE: • Chart data (add/modify/replace data points) • Title, intro, byline, source information • Colors, styling, axes configuration • Tooltips, annotations, labels • Any other configuration options for the existing chart type

WHAT YOU CANNOT UPDATE: ✗ Chart type (bar, line, column, etc.) - this is permanent

The chart_config must use high-level Pydantic fields only (title, intro, byline, source_name, source_url, etc.). Do NOT use low-level serialized structures like 'metadata', 'visualize', or other internal API fields.

STYLING UPDATES: Use get_chart_schema to see available fields, then apply styling changes:

  • Colors: {"color_category": {"sales": "#ff0000"}}

  • Line properties: {"lines": [{"column": "sales", "width": "style2"}]}

  • Axis settings: {"custom_range_y": [0, 200], "y_grid_format": "0,0"}

  • Tooltips: {"tooltip_number_format": "0.0"}

See https://datawrapper.readthedocs.io/en/latest/ for detailed examples. The provided config will be validated through Pydantic and merged with the existing chart configuration.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNoNew chart data (optional). Same formats as create_chart.
chart_idYesID of the chart to update
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.
chart_configNoUpdated chart configuration using high-level Pydantic fields (optional)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior5/5

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

Adds substantial context beyond the annotations: chart type is immutable, config is validated through Pydantic and merged with the existing configuration, and only high-level fields (not 'metadata'/'visualize') are accepted. The merge and immutability behaviors are exactly the traits an agent needs and annotations don't convey.

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?

Content is dense and mostly earns its place, with limitations front-loaded. The banner header and the readthedocs link are marginal filler, but the bullets are functional rather than redundant.

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?

Covers purpose, limitations, parameter semantics, validation/merge behavior, and a documentation pointer. With annotations covering the safety profile and no output schema required, nothing essential for correct invocation is missing.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning: chart_config must use high-level Pydantic fields and must not use low-level serialized structures, plus concrete styling examples that illustrate the expected shape.

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?

Specific verb+resource ('Update an existing Datawrapper chart's data or configuration') with explicit scope: what can be updated (data, title, colors, axes) and what cannot (chart type). An agent can immediately distinguish this from create_chart and delete_chart.

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 a clear routing rule for the main edge case: to change chart type you must use create_chart instead. It also points to get_chart_schema before styling updates. It doesn't give a general when-to-use-vs-sibling statement, but the exclusion path is explicit.

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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