Climate MCP Server

preview_dataset

Examine the initial rows of any dataset table to understand data shape before analysis. Supports up to 25 rows.

Instructions

    First N rows of any dataset table (max 25).
    Pass 'filename' or 'dataset_uri'.
    Use to inspect raw data shape before analysis.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "preview_dataset"
  }
}

Input Schema

Name	Required	Description	Default
`n`	No
`filename`	No
`dataset_uri`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 30, 2026

TDQS

A4/5.0

Behavior3/5

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

With no annotations provided, the description carries full burden. It states the maximum rows (25) and parameter options but does not explicitly state that the tool is read-only or mention any authentication requirements. It implies non-destructive behavior through 'inspect,' which is adequate but not fully explicit.

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 extremely concise, consisting of two short sentences that convey all essential information. It is front-loaded with the core functionality and uses no unnecessary words.

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?

Given that the tool has an output schema (not shown), the description does not need to explain return values. It effectively covers the purpose, parameters, and usage context. It could mention that the output is a table or data frame, but the output schema likely provides that.

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?

The description adds meaning beyond the schema by explaining that 'filename' or 'dataset_uri' can be used and that 'n' is for the number of rows (max 25). However, it does not specify that the two parameters are mutually exclusive or provide format examples. Given 0% schema description coverage, the description compensates partially.

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 that the tool returns the first N rows of any dataset table with a max of 25. It specifies the verb 'preview' and the resource 'dataset table'. It also distinguishes itself from sibling tools by focusing on raw data inspection, which is unique among the listed siblings.

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 explicitly says to use it 'to inspect raw data shape before analysis,' providing clear context. However, it does not mention alternative tools or when not to use it, but given the unique purpose among siblings, the lack of exclusions is acceptable.

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