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mckinsey

vizro-mcp

Official
by mckinsey

validate_chart_code

Validate user-created chart code by checking configuration against data metadata and optionally open the chart in PyCafe for preview.

Instructions

Validate the chart code created by the user and optionally open the PyCafe link in a browser.

Returns:
    ValidationResults object with status and dashboard details

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chart_configYesA ChartPlan object with the chart configuration
data_infoYesMetadata for the dataset to be used in the chart
auto_openNoWhether to automatically open the PyCafe link in a browser

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
validYes
messageYes
python_codeYes
pycafe_urlYes
browser_openedYes
Behavior2/5

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

No annotations are provided, so the description must fully disclose behavior. It only states validation and optional browser opening, but omits details like whether code is executed, what constitutes invalid code, side effects, or authentication requirements. The return type is mentioned but not elaborated.

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 very concise with two sentences plus a return note. It front-loads the primary action. However, it could slightly expand on key behaviors without losing conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having an output schema (mentioned), the description lacks context about when validation is needed, error handling, or workflow integration with sibling tools. It omits important contextual details for an effective agent invocation.

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?

The input schema has 100% coverage with detailed descriptions for all parameters, including nested objects. The tool description adds no extra meaning beyond the schema, so it scores the baseline of 3. It does not clarify how parameters relate to validation logic.

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 validates chart code and optionally opens a PyCafe link in a browser. It uses a specific verb ('Validate') and resource ('chart code'), and the purpose is distinct from sibling tools like 'get_vizro_chart_or_dashboard_plan' and 'validate_dashboard_config'.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives, nor does it mention prerequisites, exclusions, or context. It fails to differentiate usage scenarios among sibling tools.

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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