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netcafe-live-data

by com.ainetcafe

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sqlYesThe SQL statement (or several, separated by semicolons).
readNoSource dialect, e.g. "mysql". Omit to auto-detect from generic SQL.
writeYesTarget dialect, e.g. "postgres", "bigquery", "doris".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.5/5.0
Behavior5/5

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

The description adds rich behavioral context beyond the annotations: it reveals the tool is deterministic (sqlglot parser, same input -> same output) and provides exact line/column on syntax errors. This gives the agent useful expectations about reliability and error handling, complementing the readOnly/idempotent hints.

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 and front-loaded with the primary action. Every phrase earns its place: supported dialects, determinism, error reporting, and usage context. No redundant restating of the title.

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?

Given the complexity of dialect conversion, the description covers purpose, when to use, behavior, and error characteristics. An output schema exists, so return format is handled upstream. The description is fully sufficient for an agent to select and 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?

The input schema already provides 100% coverage: each parameter (sql, read, write) includes a description with examples and default behavior. The tool description itself does not add parameter-specific detail beyond what the schema states, so baseline 3 is appropriate.

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 starts with a specific verb+resource: "Convert a SQL statement from one dialect to another" and gives an extensive list of supported dialects. This clearly distinguishes it from unrelated sibling tools (check_vulns, get_weather, etc.) which cover other domains.

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 description explicitly states when to use the tool: "migrating queries between databases or debugging dialect-specific syntax." It does not explicitly state when not to use it or name alternatives, but the sibling tools are so unrelated that no confusion arises.

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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TDQS

A3.6/5.0
Disambiguation4/5

Most tools are clearly separated by domain, but the cluster of domain-related tools (domain_check, ssl_check, china_reachability) could be confused by an agent looking for a generic 'check this domain' operation. The descriptions help clarify each one's specific focus, so overall ambiguity is low.

Naming Consistency2/5

Naming conventions are mixed: some tools use a verb-first pattern (check_vulns, get_weather, transpile_sql), while others use a noun-first pattern (domain_check, stock_quote, ip_lookup). Verbs are also inconsistent (check, get, lookup, transpile), and some names are pure noun phrases (exchange_rate, package_info). This lack of a uniform pattern makes the set feel disjointed.

Tool Count4/5

At 11 tools, the count is within a reasonable range and each tool has a distinct purpose. The broad scope makes the set feel somewhat unfocused, but there is no redundancy or excessive bloat.

Completeness3/5

The server intends to provide live data, but coverage is shallow within each category. For example, weather only gives current conditions and a short forecast, package tools only have info and vulnerabilities, and there is no historical data for stocks. The overall domain is vague, so significant gaps exist for a general-purpose live-data server.

Resources