vegalite-viewer

by stephaneberle9

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upload_data

Instructions

A tool to upload and register a JSON dataset by name for use in subsequent visualizations. When to use this tool:

When the user asks to visualize data, call this tool first to register the dataset, then call visualize_data. How to use this tool:
Provide a short, descriptive name and the dataset as a list of JSON objects (records).
Each object should share a consistent set of keys (fields).

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": {
      "name": "<name>",
      "data": "<data>"
    },
    "name": "upload_data"
  }
}

Input Schema

Name	Required	Description	Default
`data`	Yes
`name`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 2, 2026

TDQS

A4.4/5.0

Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses that the tool registers data for later use, implying persistence, but does not mention whether re-uploading with the same name overwrites, errors, or other side effects. Basic behavioral transparency exists, but lacks depth for a mutation tool.

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 well-structured with clear sections for when and how to use. It is concise, with every sentence providing necessary guidance. No filler or redundant repetition of the schema.

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, the description covers the essential context: sequencing relative to visualize_data, data format guidelines, and the purpose. An output schema exists, so return values need not be described. Missing edge cases like name collisions or overwrite behavior, but for the core use case it is sufficiently complete.

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?

Schema coverage is 0%, so the description compensates by explaining that 'name' should be a short, descriptive label and 'data' should be a list of JSON objects with consistent keys. This adds meaningful semantics beyond the bare schema types, though it stops short of giving full examples or constraints.

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's function: 'upload and register a JSON dataset by name for use in subsequent visualizations.' It uses specific verbs ('upload', 'register') and identifies the resource (JSON dataset) and its purpose, effectively distinguishing it from the sibling tool visualize_data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit usage instructions are given: 'When the user asks to visualize data, call this tool first to register the dataset, then call visualize_data.' It also provides how-to guidance on providing a name and data format, leaving no ambiguity about when and how to use the tool.

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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