data-analysis-agent

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describe_dataset

Get summary statistics for dataset columns, including counts, nulls, mean, standard deviation, quantiles, and frequent values to quickly assess data quality and distributions.

Instructions

Get comprehensive summary statistics for dataset columns. Returns count, nulls, mean, std, min, max, 25%/50%/75% quantiles, plus exact positive, negative, and zero value counts for numeric columns. Also provides distinct count and top frequent values for categorical columns.

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": {
      "session_id": "<session_id>"
    },
    "name": "describe_dataset"
  }
}

Input Schema

Name	Required	Description	Default
`sample`	No
`columns`	No
`session_id`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 4, 2026

TDQS

A3.6/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does well by specifying the exact return contract: numeric summary statistics, categorical distinct counts, and top frequent values. It does not explicitly state side effects or read-only behavior, but the 'Get' framing and tool name make the read-only nature reasonably clear.

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 concise and well-structured, front-loading the core purpose and then listing the specific statistics returned. Every sentence adds concrete value with no filler.

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?

The output behavior is thoroughly described and an output schema exists, but the definition leaves key gaps: optional parameter semantics are absent and there is no guidance for choosing this tool over several closely related siblings. It is adequate for a default call with only session_id but not complete for more targeted use.

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

Parameters1/5

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

Schema description coverage is 0%, and the description adds no meaning to any of the three parameters: session_id, sample, and columns are never mentioned. The agent must infer the role of sample and columns from their names and defaults alone, which is insufficient for reliable non-default invocation.

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 identifies the action and resource: 'Get comprehensive summary statistics for dataset columns.' It enumerates exact outputs such as nulls, mean, std, quantiles, positive/negative/zero counts, distinct count, and top frequent values, making it easy to distinguish from sibling tools like compute_statistic or frequency_analysis.

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 implies it should be used when a broad statistical overview is needed, but it does not explicitly state when to prefer it over similar tools like compute_statistic, frequency_analysis, or get_dataset_info. No alternatives, exclusions, or conditional usage guidance are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.