mcp-csv-database

get_column_stats

Retrieve statistical summary for a specific table column, including count, nulls, unique values, and distribution data.

Instructions

Get statistical summary for a specific column.

Args: table_name: Name of the table column_name: Name of the column to analyze

Returns: Statistical summary including count, nulls, unique values, and distribution info

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": {
      "table_name": "<table_name>",
      "column_name": "<column_name>"
    },
    "name": "get_column_stats"
  }
}

Input Schema

Name	Required	Description	Default
`table_name`	Yes
`column_name`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.6Jul 11, 2026

TDQS

B3/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It does disclose that the tool returns a statistical summary including count, nulls, unique values, and distribution info. However, it does not mention any safety or side effects (e.g., whether it is read-only, requires permissions, or is destructive), leaving some behavioral aspects unclear.

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 concise and structured as a docstring with clear sections for Args and Returns. It is appropriately sized and front-loaded with the main purpose. However, it could be slightly more concise by removing redundant information (e.g., the Returns section already mirrors the main purpose).

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that an output schema exists, the description does not need to fully detail return values, but it does provide useful information about what is included. However, the description lacks usage guidelines and parameter details, which are important for a tool with moderate complexity. It is adequate but has clear gaps.

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?

The input schema has 0% description coverage, meaning no parameter descriptions are provided in the schema. The description merely restates the parameter names ('Name of the table', 'Name of the column to analyze') without adding any additional meaning, format constraints, or examples. With two parameters and no schema descriptions, the description does not compensate adequately.

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 clearly states the tool's purpose: 'Get statistical summary for a specific column.' It uses a specific verb and resource, and the tool is distinct from siblings like 'get_data_summary' (broader) and 'analyze_missing_data' (focused on missing data). However, it does not explicitly differentiate itself from these siblings, so it is not a perfect 5.

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?

No guidance is provided on when to use this tool versus alternatives. Siblings like 'get_data_summary' and 'analyze_missing_data' might be more appropriate in certain contexts, but the description does not mention these or provide any usage conditions.

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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