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by io.github.9thShini
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CSV to JSON converter

csv_to_json

Parse CSV/TSV text (auto-detecting tab, semicolon or comma delimiter) with a header row into an array of JSON objects. Runs on smart-tools.xyz.

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

Input Schema

NameRequiredDescriptionDefault
textYesCSV/TSV text to convert
localeNoLanguage for the source_url link (default en)
has_headerNoTreat the first row as column names (default true); if false, columns are named col1, col2, …

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the burden of behavioral disclosure. It mentions auto-detecting delimiters and output as JSON objects, but omits behaviors like handling of missing headers, the locale/source_url mechanism, or error handling. Adds some context but is not comprehensive.

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?

Two sentences, with the first being highly informative. The second sentence 'Runs on smart-tools.xyz' is not essential for tool invocation and slightly dilutes focus, but the description is otherwise clean and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple, and the description covers main behavior and output. However, gaps remain: the locale/source_url link is unexplained, the description says 'with a header row' but the schema supports has_header=false, and no error or edge cases are mentioned. Given no annotations, it is not fully complete.

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?

Schema description coverage is 100%, so baseline is 3. The description adds extra context about delimiter auto-detection relevant to the 'text' parameter, but does not clarify 'source_url' in the locale parameter. Overall, it adds marginal value beyond the schema.

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 parses CSV/TSV text into an array of JSON objects, with specific details like auto-detecting delimiter and header row. This distinguishes it from sibling tools such as csv_to_sql and table_to_csv.

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 converting tabular text to JSON but does not explicitly state when to prefer this tool over alternatives like csv_to_sql or table_to_csv. No exclusions or alternative guidance is provided.

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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