SQL Explanation for AI Agents

Overview

SQL Explainer sends the `query` you POST, up to 50,000 characters, to a Gemini language model with instructions to explain it in plain English and return structured JSON. The model runs at temperature 0, so the same query normally produces the same explanation. Output is always in English, whatever language the query's comments or identifiers use.

The `detail` parameter sets the length. `brief`, the default, asks for one or two sentences. `standard` asks for two to four. `detailed` asks for a step-by-step breakdown and allows the longest output. An unrecognised value falls back to `brief`.

`explanation` is the prose description. `operation` is the primary statement type, such as `SELECT`, `INSERT`, `UPDATE`, `DELETE` or `CREATE`. `tables` lists the table names the model identifies in the query. `complexity` is one of `simple`, `moderate` or `complex`.

The model also judges whether the input is SQL at all. If it is not, the call returns a 400 with an example of a valid query instead of an explanation. Input that fails the text safety check is rejected with a 400 before the model is called. The query is never executed or connected to a database, so the explanation describes intent from the text alone: it cannot know your schema, indexes or row counts, and `complexity` reflects the query's structure, not its run time. On the Free plan `detail` is ignored, so every explanation is brief, and `complexity` is removed.

Live Test SQL Explanation for AI Agents Source →

The tool

Once your client is connected to the VerveContext server, this appears in its tool list as SQLExplanationforAIAgents. It is read-only and open-world — it fetches and never mutates anything on your side — so most clients call it without asking you to confirm.

{
  "name": "SQLExplanationforAIAgents",
  "arguments": {
    "query": "SELECT * FROM users WHERE age > 18"
  }
}

You do not name the tool yourself; the model picks it. Asking about SELECT * FROM users WHERE age > 18 in the terms this source covers is enough for it to reach for SQLExplanationforAIAgents on its own — naming it explicitly also works, and is the way to force the call.

Connecting

One server URL covers every source in the catalog, including this one. Authorization is OAuth: the client opens a browser once, and there is no key to paste into a config file.

{
  "mcpServers": {
    "vervecontext": {
      "url": "https://api.vervecontext.com/v1/mcp"
    }
  }
}

https://api.vervecontext.com/v1/mcp

Per-client setup — Claude, Cursor, VS Code, ChatGPT — is on the MCP setup page.

Arguments

These are the properties on the tool's inputSchema, so a well-behaved client validates them before the call is made. Premium arguments are accepted on every plan but only take effect on plans that include them.

What the model gets back

The result carries a structuredContent object matching the tool's declared outputSchema, so a client reads fields without parsing prose. status is "ok" and error is null on success; a null field means the value was not available for that input, not that the call failed.

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{
  "status": "ok",
  "error": null,
  "data": {
    "explanation": "This SQL query retrieves the names of users and the number of orders they have placed. It filters users who were created after January 1, 2024. The query then groups the results by user ID and only includes users who have placed more than 5 orders, finally ordering the results by the order count in descending order.",
    "operation": "SELECT",
    "tables": [
      "users",
      "orders"
    ],
    "complexity": "moderate"
  }
}

Response fields

Paths are relative to data. Premium fields are absent rather than zeroed on plans that do not include them, so check for presence instead of comparing to 0.

Why ground on it

A model can produce something that looks like this answer from its training data, and be confidently out of date or simply wrong. This source returns the current value in a shape you can check, which is the difference between an answer you can cite and one you have to hedge.

Point an evaluation at explanation: it is the field most worth pinning a claim to, and it is either present and current or absent — never plausibly invented.

Failure modes

Errors come back as tool errors carrying a sentence the model can act on, not a bare status code. Error handling covers the full list.

A call costs 10 credits each time the tool actually runs; a model that reasons about the tool without calling it costs nothing.

Use cases

Learning SQL

Understand complex queries while learning database concepts

Code Reviews

Quickly grasp what SQL queries do during reviews

Documentation

Generate explanations for query documentation

Debugging

Understand legacy or unfamiliar queries

Other ways to use SQL Explanation for AI Agents

Set up SQL Explanation for AI Agents on VerveContext, or reach the same source a different way. Your VerveContext account and credits work on all of them — one key, one balance.

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