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GoSQLX

by io.github.ajitpratap0
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validate_sql

Read-onlyIdempotent

Use this when the user wants to know whether a SQL statement is syntactically valid, optionally for a specific dialect. Returns {valid: bool, error?: string, dialect?: string}.

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>"
    },
    "name": "validate_sql"
  }
}

Input Schema

NameRequiredDescriptionDefault
sqlYesThe SQL string to validate
dialectNoSQL dialect: generic, postgresql, mysql, mariadb, sqlserver, oracle, sqlite, snowflake, bigquery, redshift, clickhouse

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description goes further by disclosing the return shape {valid, error?, dialect?}, which is genuinely useful behavioral context given there is no output schema.

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?

Two tight sentences: the usage condition comes first and the return contract second, with no filler or repetition. Every clause earns its place.

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?

For a two-parameter read-only tool with no output schema, the description supplies the missing piece: the response structure and its optional fields. Nothing essential for correct invocation is absent, though a note on how invalid SQL is reported (vs. an error thrown) would close the last gap.

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% and the enum for dialect is fully enumerated in the schema, so the parameters are already documented. The description only echoes that the dialect is optional ('optionally for a specific dialect'), adding no syntax or format detail beyond the schema. Baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('validate' a 'SQL statement') plus the scoping detail 'syntactically valid' and optional dialect targeting. It clearly differs in intent from siblings like format_sql or parse_sql, though it never names an alternative to sharpen the boundary.

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?

Gives an explicit trigger: 'Use this when the user wants to know whether a SQL statement is syntactically valid.' This is a clear context for invocation. It stops short of exclusions or naming the neighboring tools (lint_sql, parse_sql) that an agent might otherwise confuse it with.

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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