ScorpTech x402 API

Csv To Sql

csv_to_sql

Read-onlyIdempotent

Turn CSV/TSV into SQL: CREATE TABLE with inferred column types (integer/bigint, numeric, boolean, date, timestamp, varchar/text, NOT NULL where never empty, optional primary key) and batched INSERT statements with correct quoting and escaping for PostgreSQL, MySQL, SQLite or SQL Server. Up to 20,000 rows. POST JSON body {csv, table, dialect}. Price: $0.003 USDC per successful call (x402, Solana). Same tool over HTTPS: POST https://scorptech.it.com/api/x402/v1/csv-to-sql

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_sql"
  }
}

Input Schema

Name	Required	Description	Default
`csv`	Yes
`table`	No		data
`header`	No
`dialect`	No		postgres
`batchSize`	No	Rows per INSERT statement
`delimiter`	No
`primaryKey`	No	Column to declare as PRIMARY KEY
`createTable`	No

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 23, 2026

TDQS

A3.9/5.0

Behavior4/5

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

Beyond annotations, the description discloses inferred type mapping, NOT NULL and primary-key behavior, quoting/escaping, a 20,000-row limit, payment via x402/Solana, and an HTTPS endpoint. It does not contradict the read-only/non-destructive annotations, and it adds meaningful operational context.

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 purpose is front-loaded and the dense sentence packs useful constraints, dialect options, and access details. The POST body example is compact, though the description is slightly run-on.

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?

With no output schema and eight parameters, the description does well by explaining the generated SQL shape, type inference, dialects, limits, and payment model. It leaves some parameter-level details to the schema, but provides enough context to invoke the tool correctly.

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 only 25%, so the description must add meaning. It covers csv, table, dialect, primary key, and batching, but omits explicit semantics for header, delimiter, createTable, and batchSize beyond a brief mention of batched INSERTs.

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 states a specific verb and resource: turning CSV/TSV into SQL with CREATE TABLE generation and INSERT statements. It also names the exact generated artifacts and supported dialects, making it clearly distinct from siblings like csv_profile or sql_format.

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 purpose implies when the tool is useful, and the row limit and dialect list provide context, but there is no explicit guidance on when to choose this over alternatives such as csv_profile or sql_format. No exclusions or sibling routing are stated.

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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