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MCP-for-Stata

MCP-for-Stata

by 54cwh
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_data_info

Retrieve descriptive statistics, variable details, and optional preview rows from a data file to understand its structure when exploring an unfamiliar dataset.

Instructions

Get descriptive statistics and a data preview for a supported data file (.dta, .csv, .tsv, .psv, .xlsx, .xls, .sav, .zsav). Returns overview, variable details, and optional head rows filtered by requested variables. Use when you need to understand a dataset or have no prior knowledge of the data.

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": {
      "data_path": "<data_path>"
    },
    "name": "get_data_info"
  }
}

Input Schema

NameRequiredDescriptionDefault
headNo
encodingNoutf-8
data_pathYes
vars_listNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.25.0

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description must convey behavioral transparency. It implies a non-destructive read operation via 'Get' and 'preview', but does not explicitly state that no file modifications occur or address error/permission behavior. It adds some context with supported formats and output shape, but safety traits are left to inference.

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?

Two sentences that are front-loaded and efficient. The first sentence states the core function and return outputs; the second gives the usage trigger. No filler or redundant content.

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?

For a data-info tool with an output schema, the description covers the essential purpose, supported formats, return summary, and usage context. It misses the encoding parameter and does not specify whether the tool is read-only, but overall it is sufficiently complete for an agent to invoke it correctly in most scenarios.

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?

Schema description coverage is 0%, so the description must compensate. It explains 'optional head rows' (head) and 'filtered by requested variables' (vars_list), and data_path is implied by 'data file'. However, the encoding parameter is not mentioned, leaving 1 of 4 parameters undocumented. The description partially mitigates the schema gap but not completely.

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 function: 'Get descriptive statistics and a data preview for a supported data file' with explicit file format list. It specifies the return content (overview, variable details, optional head rows) and distinguishes itself from unrelated siblings like read_log and stata_do.

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?

The description gives a clear use case: 'Use when you need to understand a dataset or have no prior knowledge of the data.' It does not explicitly mention when not to use or alternatives, but the context implies it's the primary tool for data exploration.

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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