boltpatternhq-mcp

get_sample_data

Export a curated 50-row JSON sample dataset for CAD integration, wheel fitment calculators, and B2B testing.

Instructions

Export the complete curated 50-row JSON sample dataset for CAD integration, wheel fitment calculators, and B2B testing.

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": "get_sample_data"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jul 29, 2026

TDQS

A4.2/5.0

Behavior4/5

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

The description discloses that the tool exports a complete, curated dataset of exactly 50 rows, which is a read‑only operation. Without annotations, this is clear, but it does not mention potential side effects or authentication requirements. However, given the trivial nature of the tool (no destructive actions), it is sufficiently transparent.

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 a single, well‑structured sentence that front‑loads the core action ('Export the complete curated 50‑row JSON sample dataset') and appends specific use cases. Every word contributes to clarity with no redundancy.

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 the tool's simplicity (no parameters, no output schema), the description provides sufficient context about the output and its purpose. It could optionally mention that the data is read‑only or provide a link to documentation, but the current text adequately supports selection and invocation.

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

Parameters4/5

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

There are no parameters; the schema is empty. The description adds meaning by specifying the dataset's size (50 rows), format (JSON), and intended use cases, which goes beyond the schema alone. According to the rule, zero parameters warrant a baseline of 4, and the description meets this.

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 exports a curated 50-row JSON sample dataset for specific integration and testing purposes. It distinguishes itself from sibling tools (search_bolt_pattern and get_make_models) which perform different tasks like searching or retrieving models.

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 usage for CAD integration, wheel fitment calculators, and B2B testing but provides no explicit guidance on when to use this tool versus the alternatives. With sibling tools listed, a note explaining that this exports sample data while siblings handle patterns or models would improve the score.

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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