datawrapper-mcp

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose (create, delete, export, get, get schema, list types, publish, update). No two tools overlap in functionality; descriptions are explicit about their unique roles.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., create_chart, get_chart, update_chart, export_chart_png). No mixing of conventions or ambiguous naming.
Tool Count5/5
With 8 tools, the server is well-scoped for the Datawrapper chart lifecycle: CRUD, schema exploration, publishing, and export. Neither too few nor excessively many.
Completeness4/5
Covers most core operations: create, read, update, delete, publish, export, schema discovery, and type listing. The only notable gap is a missing 'list charts' tool, but the surface is otherwise thorough.
Average 4.4/5 across 8 of 8 tools scored. Lowest: 3.7/5.
See the Tool Scores section below for per-tool breakdowns.
- 1 of 2 community issues answered or closed in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on March 27, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate destructive and idempotent behavior. The description adds 'permanently' but does not disclose additional traits like irreversibility beyond that. With annotations covering safety, the description adds limited value.
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 brief (two sentences plus a header) and front-loaded. The warning about the MCP server integration is not strictly necessary but does not add significant bloat. Could be slightly more concise.
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?
Given the existence of output schema, full parameter coverage, and annotations covering safety, the description provides enough context. It lacks details on return values or side effects, but the output schema likely covers this.
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 coverage is 100% and schema descriptions are clear for both parameters. The description adds no further meaning to the parameters, so baseline score of 3 is appropriate.
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 the action ('delete') and the resource ('chart'), with 'permanently' reinforcing the effect. This distinguishes it from sibling tools like create_chart, update_chart, and get_chart.
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 description implies use for deletion but provides no explicit guidance on when to use or avoid, nor does it mention alternatives. Context is clear but lacks exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, etc. The description adds value by detailing the return structure and explaining how the configuration can be reused, enhancing understanding beyond the annotations.
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 well-structured with a warning header, clear bullet points for return values, and concise sentences. It could be slightly more compact but remains effective without redundancy.
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 presence of an output schema (context says true), the description fully explains what the tool returns and its potential uses. No missing behavioral or contextual details for a read-only retrieval tool.
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 coverage is 100% and already describes chart_id and access_token. The description does not add new parameter details or usage nuance beyond the schema, meeting the baseline for high schema coverage.
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 explicitly states 'Get information about an existing Datawrapper chart' and enumerates returned fields (chart_id, title, type, config, URLs), clearly distinguishing it from sibling tools like create_chart, delete_chart, or update_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?
The description provides context on when the returned configuration can be used (adapt styling, clone chart), but does not explicitly state when not to use this tool or name alternatives. It implies retrieval use rather than creation/modification.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, which are consistent. Description adds that it displays inline and supports high-res zoom, plus the explicit usage constraint. 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?
Description is reasonably concise but includes a warning header that might be considered clutter. Front-loaded with key constraints and purpose. Could be slightly tighter.
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?
No output schema, but description covers what the tool does (export PNG, inline display) and key parameter notes. It addresses prerequisite and usage constraints, making it fairly complete for a parameter-rich tool.
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 has 100% description coverage for all 9 parameters, so description adds little new. It only highlights zoom for high-res and plain for no header/footer, but that's not essential beyond 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?
Clearly states it exports a chart as PNG and displays inline. Distinguishes from siblings like create_chart and get_chart by specifying the export action and dependency on prior creation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says to use only on user request for chart image or PNG export, and warns against automatic export. Also notes prerequisite that chart must exist via create_chart.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate modification (readOnlyHint=false) and idempotency (idempotentHint=true). The description adds context about the effect: making the chart publicly accessible and rendering a preview in chat, which is beyond what annotations provide. 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 short and front-loaded with the purpose. The initial '⚠️ DATAWRAPPER MCP TOOL ⚠️' line is slightly redundant but not harmful. Overall concise and to the point.
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 simple publish action with 2 parameters and no output schema, the description covers the main effect and usage guidelines. It lacks details on edge cases (e.g., already published) but is generally complete for typical use.
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?
Input schema coverage is 100%, and schema descriptions are adequate for both parameters. The description does not add extra parameter details beyond the schema, so baseline 3 applies.
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 'Publish a Datawrapper chart to make it publicly accessible and render a preview in chat,' using a specific verb and resource. It distinguishes itself from sibling tools like create_chart, delete_chart, etc., by being the only tool that publishes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly provides usage guidelines: 'Only use this tool when the user explicitly requests to publish the chart. Do not automatically publish charts after creation unless specifically asked.' This clearly tells when and when not to use the tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, non-destructive. Description adds that it returns a list with descriptions. Does not contradict annotations. Could mention any sorting or filtering, but not necessary.
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?
Very concise: two sentences plus a returns line. No fluff. Important usage guidance is front-loaded.
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?
Covers purpose, usage flow, and return type. Since there is no output schema, the description sufficiently explains the output. Could mention example chart types, but complete enough for a simple discovery tool.
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?
No parameters; schema coverage is 100% (empty schema). Baseline of 4 applies. Description does not need to add parameter info.
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?
Explicitly states it lists all available Datawrapper chart types with descriptions. Clearly identifies the resource (chart types) and action (list). Differentiates from sibling tools like create_chart or 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 Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Directly states when to use: to discover chart types before creation. Names the next tool to use (get_chart_schema) with explicit syntax. Provides a usage flow.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses important behavioral traits: it is idempotent (idempotentHint=true) and non-destructive (destructiveHint=false). It also explains the limitation on changing chart types, the use of Pydantic models, and merging behavior. No contradiction with annotations.
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 well-structured with sections for what you can/cannot update and styling updates. It is longer but every sentence adds value. It could be slightly more concise, but it's not excessively verbose.
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?
Given no output schema, return values are not described, but the tool's behavior (validation, merging) is explained. Context is provided about the API and where to find details. For an update tool with sibling tools for schema and creation, this is sufficient.
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 baseline is 3. The description adds significant value by explaining each parameter's purpose, providing examples for chart_config (e.g., colors, tooltips), and warning against low-level fields. It clearly distinguishes optional vs required parameters.
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 the tool updates an existing Datawrapper chart's data or configuration. It distinguishes itself from siblings like create_chart and delete_chart by explicitly listing what can be updated and what cannot (chart type).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool (updating existing chart) and when not to (cannot change chart type, must create a new chart instead). It also references alternatives like creating a new chart.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations show readOnlyHint=false, destructiveHint=false, idempotentHint=false, openWorldHint=true. The description confirms this is a creation tool (not read-only, not destructive), and it details the behavior: creates a chart, uses provided data and config, handles tokens. It adds context beyond annotations (e.g., how to handle large datasets, preference for inline data). 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 long but well-structured with sections (warning, intro, best practices, examples, workflow, common patterns). It is front-loaded with the crucial warning and purpose. While every section adds value, some parts (like the DO NOT list and the note about automatic scaling) could be slightly condensed without losing effectiveness. However, it remains clear and organized.
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 complexity (chart creation with many options), the description is comprehensive. It covers: purpose, prohibited alternatives, data format preferences, parameter usage (data, chart_type, chart_config, access_token), best practices, a step-by-step workflow, and detailed examples. The sibling tools are mentioned in the workflow. No output schema exists, but the description adequately hints at the action (chart creation) – the agent can infer that a chart object is returned. Overall, the description fully compensates for the lack of an output schema.
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?
Input schema has 100% coverage with detailed descriptions. The description adds significant additional value: preferred data formats with examples, alt format for large datasets, instructs to use list_chart_types for chart_type, and explains chart_config as a Pydantic model dict with examples. This goes well beyond the schema's descriptions.
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 'Create a Datawrapper chart' and emphasizes this is the complete Datawrapper integration. It distinguishes from sibling tools like list_chart_types, get_chart_schema by providing a workflow reference and explicitly warning against using other methods (e.g., 'DO NOT install the datawrapper Python package'). The verb-resource combo is 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 Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use guidance ('Use this MCP tool for ALL Datawrapper chart creation') and what not to do (DO NOT list). It includes best practices (start simple, let Datawrapper handle scaling), a styling workflow (list_chart_types → get_chart_schema → build config), and references to external documentation. This gives the agent clear decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![datawrapper-mcp MCP server](https://glama.ai/mcp/servers/palewire/datawrapper-mcp/badges/card.svg)](https://glama.ai/mcp/servers/palewire/datawrapper-mcp)

Score Badge

Copy to your README.md:

[![datawrapper-mcp MCP server](https://glama.ai/mcp/servers/palewire/datawrapper-mcp/badges/score.svg)](https://glama.ai/mcp/servers/palewire/datawrapper-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/palewire/datawrapper-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server