agent-toolbelt

CSV to JSON

csv_to_json

Convert CSV data to typed JSON with automatic delimiter detection, header parsing, and value type casting for processing spreadsheet exports.

Instructions

Convert CSV data to typed JSON. Auto-detects delimiters, uses the first row as headers, and casts values to proper types (numbers, booleans, nulls). Use when processing spreadsheet exports or any CSV-formatted data.

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": {
      "csv": "<csv>"
    },
    "name": "csv_to_json"
  }
}

Input Schema

Name	Required	Description	Default
`csv`	Yes	The CSV content to convert
`delimiter`	No	Column delimiter	auto
`hasHeader`	No	Whether the first row contains column names
`typeCast`	No	Auto-convert values to proper types
`limit`	No	Max rows to return

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Mar 30, 2026

TDQS

A4.4/5.0

Behavior4/5

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

No annotations provided, so description carries full burden. It effectively discloses transformation behaviors: auto-detects delimiters, uses first row as headers, and type-casting to numbers/booleans/nulls. Missing only error-handling behavior for malformed CSV.

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?

Two efficiently structured sentences with zero waste. First sentence front-loads core functionality with key features; second provides usage context. No redundant or filler text.

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 5 parameters with 100% schema coverage and no output schema, the description adequately covers the transformation logic. Minor gap: lacks description of output JSON structure (array of objects vs other formats), which would help given no output schema exists.

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?

With 100% schema coverage, baseline is 3. The description adds value by explaining how defaults behave (auto-detection for delimiters, first row handling for headers, casting for types), connecting features to specific parameters beyond raw schema definitions.

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 explicitly states 'Convert CSV data to typed JSON' with specific verb (convert) and resources (CSV to JSON). The mention of 'typed JSON' and CSV-specific processing distinguishes it from sibling conversion tools like convert_markdown.

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 second sentence explicitly states 'Use when processing spreadsheet exports or any CSV-formatted data,' providing clear contextual guidance. However, it lacks explicit exclusions or named alternatives for non-CSV data formats.

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