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vizro-mcp

vizro-mcp

Official
by mckinsey
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

validate_chart_code

Validates user-generated chart code to meet required structure and optionally opens a PyCafe preview in a browser for immediate verification.

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

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_config": "<chart_config>",
      "data_info": "<data_info>"
    },
    "name": "validate_chart_code"
  }
}

Input Schema

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

Output Schema

NameRequiredDescriptionDefault
validYes
messageYes
pycafe_urlYes
python_codeYes
browser_openedYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changedv1.0.0
    • addedInput schema / properties / auto_open / description
      Added value: +"Whether to automatically open the PyCafe link in a browser"
    • addedInput schema / properties / chart_config / description
      Added value: +"A ChartPlan object with the chart configuration"
    • addedInput schema / properties / data_info / description
      Added value: +"Metadata for the dataset to be used in the chart"
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "browser_opened": {
+      "title": "Browser Opened",
+      "type": "boolean"
+    },
+    "message": {
+      "title": "Message",
+      "type": "string"
+    },
+    "pycafe_url": {
+      "anyOf": [
+        {
+          "type": "string"
+        },
+        {
+          "type": "null"
+        }
+      ],
+      "title": "Pycafe Url"
+    },
+    "python_code": {
+      "title": "Python Code",
+      "type": "string"
+    },
+    "valid": {
+      "title": "Valid",
+      "type": "boolean"
+    }
+  },
+  "required": [
+    "valid",
+    "message",
+    "python_code",
+    "pycafe_url",
+    "browser_opened"
+  ],
+  "title": "ValidateResults",
+  "type": "object"
+}
  2. First observed

TDQS

B3.1/5.0
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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