AI NetCafé

Translate SQL between dialects

transpile_sql

Read-onlyIdempotent

Convert a SQL statement from one dialect to another — mysql, postgres, sqlite, tsql, oracle, snowflake, bigquery, redshift, spark, hive, presto, trino, duckdb, clickhouse, databricks, doris, starrocks and more. Deterministic parser (sqlglot), not an LLM: the same input always produces the same output, and syntax errors come back with the exact line and column. Use it when migrating queries between databases or debugging dialect-specific syntax.

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": {
      "sql": "<sql>",
      "write": "<write>"
    },
    "name": "transpile_sql"
  }
}

Input Schema

Name	Required	Description
`sql`	Yes	The SQL statement (or several, separated by semicolons).
`read`	No	Source dialect, e.g. "mysql". Omit to auto-detect from generic SQL.
`write`	Yes	Target dialect, e.g. "postgres", "bigquery", "doris".

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

AddedAug 13, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already declare readOnly/idempotent, but the description adds determinism ('same input always produces the same output') and exact error locations (line/column), going beyond structured metadata. This is valuable behavioral context.

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?

Three sentences, each valuable: purpose, determinism, and use case. Front-loaded and no redundancy.

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

Completeness5/5

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

Description covers purpose, usage, determinism, and error behavior. Output schema covers return shape. For a conversion tool with rich annotations, it's 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 covers all parameters at 100%, so baseline is 3. The description adds a useful list of supported dialects for read/write, aiding parameter selection, though it doesn't add much beyond that.

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?

Clearly states a specific verb ('Convert') and resource ('a SQL statement from one dialect to another') and lists many supported dialects. It distinguishes itself from sibling formatting tools by focusing on SQL dialect conversion.

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?

Explicitly states when to use ('when migrating queries between databases or debugging dialect-specific syntax'). It doesn't mention exclusions or alternatives, but the sibling tools are unrelated, so the context is clear.

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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