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Baronco

SQL-Transpiler MCP Tool

by Baronco

Transpiler

Convert SQL queries between different database dialects to ensure cross-system compatibility. Check supported dialects first, then provide source SQL, source dialect, and target dialect for translation.

Instructions

Transpile SQL queries from one dialect to another using sqlglot. First, run the Dialects tool to check if both the source and target dialects are supported.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sql_queryYesThe SQL query to transpile.
from_dialectYesThe dialect of the input SQL query.
to_dialectYesThe target dialect for the output SQL query.
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It mentions the underlying library (sqlglot) and a prerequisite check, which adds useful context. However, it lacks details on error handling, performance, or output format (e.g., whether it returns a string or structured data), leaving behavioral gaps for a tool with no annotations.

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 two sentences with zero waste: the first states the core purpose, and the second provides essential usage guidance. It is front-loaded with the main action and efficiently includes only necessary information.

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?

Given no annotations and no output schema, the description is moderately complete. It covers purpose and usage prerequisites but lacks details on output (e.g., what the transpiled query looks like) and error cases (e.g., unsupported dialects). For a tool with 3 parameters and no structured output info, more behavioral context would be beneficial.

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 the schema fully documents all three parameters. The description does not add any parameter-specific details beyond what the schema provides (e.g., no examples of dialect values or query formatting). Baseline 3 is appropriate as the schema handles the heavy lifting.

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 specific action ('transpile SQL queries from one dialect to another') and the resource ('SQL queries'), using the verb 'transpile' which is precise. It distinguishes from the sibling 'Dialects' tool by mentioning it as a prerequisite check rather than a direct alternative, establishing clear functional separation.

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?

The description explicitly provides usage guidance: 'First, run the Dialects tool to check if both the source and target dialects are supported.' This gives a clear prerequisite step and references the sibling tool, indicating when to use it (before this tool) and why (to verify dialect support).

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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