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vizro-mcp

Official
by mckinsey
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Hybrid

load_and_analyze_data

Load and analyze data files from absolute local paths or URLs. Supports CSV, JSON, HTML, Excel, ODS, and Parquet formats.

Instructions

Use to understand local or remote data files. Must be called with absolute paths or URLs.

Supported formats:
- CSV (.csv)
- JSON (.json)
- HTML (.html, .htm)
- Excel (.xls, .xlsx)
- OpenDocument Spreadsheet (.ods)
- Parquet (.parquet)

Returns:
    DataAnalysisResults object containing DataFrame information and metadata

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
path_or_urlYesAbsolute (important!) local file path or URL to a data file

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
validYes
df_infoYes
messageYes
df_metadataYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.0.0
    • addedInput schema / properties / path_or_url / description
      Added value: +"Absolute (important!) local file path or URL to a data file"
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "$defs": {
+    "DFInfo": {
+      "properties": {
+        "general_info": {
+          "title": "General Info",
+          "type": "string"
+        },
+        "sample": {
+          "additionalProperties": true,
+          "title": "Sample",
+          "type": "object"
+        }
+      },
+      "required": [
+        "general_info",
+        "sample"
+      ],
+      "title": "DFInfo",
+      "type": "object"
+    },
+    "DFMetaData": {
+      "properties": {
+        "column_names_types": {
+          "anyOf": [
+            {
+              "additionalProperties": {
+                "type": "string"
+              },
+              "type": "object"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "default": null,
+          "title": "Column Names Types"
+        },
+        "file_location_type": {
+          "enum": [
+            "local",
+            "remote"
+          ],
+          "title": "File Location Type",
+          "type": "string"
+        },
+        "file_name": {
+          "title": "File Name",
+          "type": "string"
+        },
+        "file_path_or_url": {
+          "title": "File Path Or Url",
+          "type": "string"
+        },
+        "read_function_string": {
+          "enum": [
+            "pd.read_csv",
+            "pd.read_json",
+            "pd.read_html",
+            "pd.read_parquet",
+            "pd.read_excel"
+          ],
+          "title": "Read Function String",
+          "type": "string"
+        }
+      },
+      "required": [
+        "file_name",
+        "file_path_or_url",
+        "file_location_type",
+        "read_function_string"
+      ],
+      "title": "DFMetaData",
+      "type": "object"
+    }
+  },
+  "properties": {
+    "df_info": {
+      "anyOf": [
+        {
+          "$ref": "#/$defs/DFInfo"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "df_metadata": {
+      "anyOf": [
+        {
+          "$ref": "#/$defs/DFMetaData"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "message": {
+      "title": "Message",
+      "type": "string"
+    },
+    "valid": {
+      "title": "Valid",
+      "type": "boolean"
+    }
+  },
+  "required": [
+    "valid",
+    "message",
+    "df_info",
+    "df_metadata"
+  ],
+  "title": "DataAnalysisResults",
+  "type": "object"
+}
  2. First observed

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description fully bears the burden of behavioral disclosure. It states the action (load and analyze), supported formats, and return type, but does not disclose whether the operation is read-only, potential file size/performance implications, or any side effects. This is adequate but could be more thorough.

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?

The description is extremely concise: three lines covering purpose, requirement, formats, and return type. It is front-loaded with the main purpose and includes no redundant information. Every sentence earns its place.

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 simplicity of the tool (one parameter, output schema exists), the description covers the essential aspects: purpose, required input format, supported file types, and return type. It may lack details about remote access authentication or error handling, but overall it is sufficiently complete for an agent to use the tool correctly.

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?

The input schema has 100% coverage for the single parameter (path_or_url) with a good description. The additional description adds critical usage constraints (absolute paths/URLs) and lists supported file formats, which significantly enhances the parameter semantics beyond what the schema provides.

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?

The description states the tool is used to 'understand local or remote data files' and specifies supported formats and return type. While the verb 'understand' is somewhat vague, the combination with the tool name and listed formats makes the purpose clear. It is distinguishable from sibling tools that handle schema, validation, and chart planning.

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 includes the critical usage constraint 'Must be called with absolute paths or URLs' but provides no explicit guidance on when to use this tool versus alternatives. The siblings are clearly different in function, so it's implicitly appropriate for data loading, but a brief comparison would improve clarity.

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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