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ChartQuery MCP Server

ChartQuery MCP Server

by chartquery
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TypeScript
Hybrid

Label Preview

label
Read-only

Preview a thermal print layout by passing its URL-encoded JSON configuration in the query, and return a PNG image for ChatGPT display and validation.

Instructions

Label Preview. Preview a thermal label via GET. Pass the same label object as a URL-encoded JSON string in query. Inside that JSON, text items use content and size (1-10), not text or fontSize. Prefer format=png for ChatGPT. Credit cost: 3 credits per call.

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": {
      "query": "<query>"
    },
    "name": "label"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesFull label config as URL-encoded JSON. Must include width and elements. Text items use content and size (1-10), not text or fontSize.
shareNoIf true, persist the result and return share URLs instead of an image. Leave false to get the image. Default: false.
fieldsNoComma-separated list of fields to keep in the response, with dot notation for nested fields (e.g. name,location.city). Does not change the credit cost.
formatNoUse png so ChatGPT can display the image. svg is for download or infinite scale. Allowed values: svg, png. Default: "svg".

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=true, so safety is covered structurally. The description still adds genuinely useful behavior the annotations do not: a concrete credit cost ("3 credits per call") and the GET transport. It does not say whether previews are retained or rate-limited, so the addition is real but partial.

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 definition is short and puts the action and transport first, followed by the encoding pitfall and the credit cost. The only waste is the opening "Label Preview.", which merely echoes the title and adds no information.

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?

For a 4-parameter, read-only tool with full schema coverage, no output schema, and complete annotations, the description covers the important extras: credit cost, the URL-encoded nested JSON contract, the content/size naming trap, and the png display caveat. The remaining gap is routing among sibling preview/render/view tools, not the call mechanics.

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?

With schema description coverage at 100%, the schema already documents all four parameters. The description's JSON guidance ("pass the same label object as a URL-encoded JSON string", text items use content and size 1-10) largely restates the query parameter's own schema text, though "the same label object" hints the config is shared with sibling label tools.

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 states a specific verb and resource combination ("Preview a thermal label via GET"), so an agent knows it produces a preview image rather than a compiled or validated artifact. However, it never distinguishes itself from the six sibling label tools (label_render, label_view, label_compile, label_validate, label_parse, label_convert), leaving the agent to guess which one to call.

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?

There is no explicit when-to-use or when-not-to-use guidance, and none of the label siblings are named as alternatives. The only conditional advice is a parameter hint ("Prefer format=png for ChatGPT"), which is not a selection rule; the agent must infer that "preview" precedes compile/render.

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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