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data-profiler-mcp

data-profiler-mcp

by haiiibin
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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suggest_dtypes

Recommends better column dtypes to reduce memory usage and fix incorrect types, showing per-column and total savings.

Instructions

Recommend more memory-efficient or more-correct column dtypes.

For each column, proposes a better dtype when one exists: text that is fully numeric to a numeric type, low-cardinality text to category, and oversized integer/float columns downcast to smaller types. Reports per-column and total estimated memory savings.

Use this to help a user shrink a DataFrame's memory footprint or fix columns that were loaded with the wrong type.

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": {
      "path": "<path>"
    },
    "name": "suggest_dtypes"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYes
max_rowsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior5/5

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

Details specific behaviors: proposing numeric conversions, category for low-cardinality, downcasting, and reporting savings. Covers key expectations.

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?

Three succinct sentences with no redundancy. Front-loaded with purpose, then behavior, then usage.

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, and behavior well for a simple tool. Minor gap: no parameter explanations, but overall adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema coverage, description should parameter details. It does not explain 'path' or 'max_rows', leaving their roles ambiguous.

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 recommends memory-efficient or correct column dtypes, distinguishing it from sibling tools that handle stats, quality, or profiling.

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?

Provides explicit use cases: shrink memory footprint or fix wrong types. No exclusions or alternatives mentioned, but context 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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