data-analysis-agent

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preview_dataset

Inspect a dataset by viewing sample or top rows, capped at 50, to verify its structure and contents before performing analysis.

Instructions

Preview sample or top rows from dataset (capped at max 50 rows).

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": "preview_dataset"
  }
}

Input Schema

Name	Required	Description	Default
`n`	No
`sample`	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

A3.9/5.0

Behavior4/5

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

With no annotations, the description carries the burden, and it does disclose meaningful constraints: the `capped at max 50 rows` limit and the sample-vs-top variant. It does not explicitly confirm the operation is non-destructive, but `preview` and the absence of mutation language make that a minor gap.

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?

One sentence with no filler; the behavioral cap appears immediately and the sentence is fully informative. It earns its place without redundant phrasing.

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?

For a simple preview tool with an output schema and only three parameters, the description covers the core behavior. It remains slightly thin on parameter-level detail and explicit read-only guarantees, but these are not critical for this tool.

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 compensation matters. `sample or top rows` maps clearly to the `sample` boolean, and the 50-row cap contextualizes `n`; however, `session_id` is left unexplained and the exact cap/`n` interaction is not spelled out.

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 names a clear verb (`Preview`), a resource (`dataset`), and the exact scope (`sample or top rows`, capped at 50). This makes it easy to distinguish from siblings such as `load_dataset` or `describe_dataset`, which serve different purposes.

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 quick row-level inspection but does not explicitly state when to choose `preview_dataset` over `load_dataset` or `describe_dataset`, nor any exclusions. The use case is inferable from `preview`, but the guidance is not made explicit.

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