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mckinsey

vizro-mcp

Official
by mckinsey

get_sample_data_info

Retrieve metadata and structure of sample datasets (iris, tips, stocks, gapminder) to support chart exploration without custom data.

Instructions

If user provides no data, use this tool to get sample data information.

Use the following data for the below purposes:
    - iris: mostly numerical with one categorical column, good for scatter, histogram, boxplot, etc.
    - tips: contains mix of numerical and categorical columns, good for bar, pie, etc.
    - stocks: stock prices, good for line, scatter, generally things that change over time
    - gapminder: demographic data, good for line, scatter, generally things with maps or many categories

Returns:
    Data info object containing information about the dataset.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
data_nameYesName of the dataset to get sample data for

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_nameYes
file_path_or_urlYes
file_location_typeYes
read_function_stringYes
column_names_typesNo
Behavior3/5

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

No annotations present, so description bears full burden. It discloses return type ('Data info object') and implies read-only behavior, but lacks details on permissions, rate limits, or side effects. Adequate for a simple retrieval tool.

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?

Well-structured with a bullet list for dataset descriptions. Condition ('If user provides no data') is front-loaded. Slightly verbose but every sentence adds value.

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 an output schema (not shown), description correctly focuses on input semantics and use case. It adequately covers the tool's role among siblings, which handle user-provided data or code validation.

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% (single parameter with enum). Description adds significant meaning beyond schema by explaining each dataset's suitability for different visualizations (e.g., 'iris: mostly numerical... good for scatter, histogram').

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's purpose: to get sample data information when no data is provided. It lists four specific datasets (iris, tips, stocks, gapminder) and their characteristics, distinguishing it from sibling tools that handle user-provided data or schemas.

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?

Explicitly states when to use ('If user provides no data'). Provides guidance on dataset selection based on chart types (scatter, bar, line, etc.). Does not explicitly exclude alternatives but usage condition is clear.

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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