# SQL Parser AST Engine MCP for AI Agents AI Agent Connect

> SQL Parser AST Engine turns raw SQL queries into structured data objects. It identifies every table, column, and join in queries across 15+ dialects. Use it to build security firewalls or automate data governance audits without relying on fuzzy text matching.

## Overview
- **Category:** developer-tools
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_IaMB6VbtA33IOGoYDwunOjWljHqHTke8wZBDmmAY/ai-agent-connect
- **Tags:** sql-parsing, ast, query-analysis, security-firewall, database-security, code-inspection

## Description

When you feed a raw SQL query to an AI client, it treats the text like a string of words. That's a problem when you need to know exactly which tables are being touched or if a hidden subquery contains a malicious command. This Connector solves that by breaking the SQL down into a complete Abstract Syntax Tree. Instead of guessing, your agent gets a structured map of every join, where clause, and function call. You can use this to build a security layer that blocks unauthorized database access or to automatically generate documentation for your data warehouse. It handles everything from MySQL and PostgreSQL to BigQuery and Snowflake. If you're looking for a reliable way to manage database interactions without the risk of fuzzy text matching, this is the standard you need. You can find this and thousands of other tools in the Vinkius catalog to keep your stack connected and your data secure.

## Tools

### parse_sql
Converts a SQL query into a structured AST to extract tables, columns, and clauses. This lets your agent understand the query structure without actually running it on a database.

## Prompt Examples

**Prompt:** 
```
A user just sent this SQL query: SELECT * FROM users WHERE id = 10; Check if it accesses anything other than the 'orders' table.
```

**Response:** 
```
Tables extracted: `orders`, `products`, `users`.

⚠️ **Warning: 'users' table access detected.**

The query attempts to access the `users` table, which is not on your allowed list of `orders` and `products`.
```

**Prompt:** 
```
I have this BigQuery query. List all the tables and columns it mentions so I can update our data dictionary.
```

**Response:** 
```
I've analyzed the BigQuery query. Here are the extracted components:

* **Tables:** `sales_data`, `regional_metrics`
* **Columns:**
  * `sales_data.transaction_id`
  * `sales_data.amount`
  * `regional_metrics.region_code`
  * `regional_metrics.total_sales`

**Total:** 8 columns across 2 tables. ✅
```

**Prompt:** 
```
Check this PostgreSQL migration query for any syntax errors before I run it on production.
```

**Response:** 
```
I've parsed the PostgreSQL migration query.

* **Status:** Valid ✅
* **Syntax Errors:** None detected
* **Actions Identified:** `CREATE TABLE`, `INSERT INTO`

The query is safe to proceed with in your production environment.
```

## Capabilities

### Detect SQL injection
Decompose queries into a tree to check for unauthorized operations and malicious commands.

### Extract tables and columns
Identify every table and column referenced in a query for data governance audits.

### Support 15+ dialects
Parse queries for MySQL, PostgreSQL, BigQuery, Snowflake, MariaDB, and more.

### Rebuild valid SQL
Generate valid SQL strings from a structured AST with full fidelity.

### Audit query performance
Analyze the query structure to identify heavy joins and inefficient subqueries.

## Use Cases

### Blocking Malicious Queries
A user submits a query through a web form. Your agent uses parse_sql to see if it touches the 'users' table and blocks it if it does.

### Generating Data Dictionaries
You need to document a complex Snowflake setup. Your agent parses hundreds of queries to list every unique column and table used.

### Migration Validation
A developer wants to run a PostgreSQL migration. The agent parses the query to check for syntax errors and forbidden keywords like DROP.

### Query Performance Auditing
A DevOps engineer wants to find slow queries. The agent analyzes the AST to identify heavy joins or inefficient subqueries.

## Benefits

- Stop SQL injection attacks by using parse_sql to inspect queries for unauthorized operations before they hit your database.
- Automate data governance by extracting every table and column from queries to maintain an accurate data map.
- Support multi-cloud environments because the Connector handles 15+ dialects including BigQuery, Snowflake, and MariaDB.
- Verify migration scripts for syntax errors using parse_sql to ensure they are valid before deployment.
- Reduce manual audit time by converting complex joins and subqueries into inspectable objects for your agent.

## How It Works

The bottom line is that you turn unpredictable SQL text into predictable, programmable data.

1. Pass a raw SQL string from any dialect to the Connector.
2. The engine parses the query into a structured Abstract Syntax Tree.
3. You get back a JSON object containing every component of the query.

## Frequently Asked Questions

**Can the SQL Parser AST Engine handle my specific database dialect?**
Yes, it supports over 15 dialects including MySQL, PostgreSQL, BigQuery, and Snowflake. You can parse queries from almost any major cloud or on-premise database.

**How does this Connector help with SQL injection?**
It breaks the SQL query into a structured tree so your agent can see exactly what the query is trying to do. You can then set rules to block any query that touches forbidden tables or uses dangerous commands.

**Does this tool actually run the queries on my database?**
No, it never executes the SQL. It only parses the text into a structured object, which makes it safe for analyzing untrusted user input without risking your data.

**Can I use this to rebuild SQL queries?**
Yes, the Connector is bidirectional. It can take a structured AST and turn it back into a valid SQL string with full fidelity.

**Is this useful for data governance?**
It's perfect for that. You can use it to automatically extract every table and column referenced in your logs or queries to build a complete map of your data usage.

**Does it support complex queries with many joins?**
Yes, it handles complex queries, nested subqueries, and multiple joins by breaking them down into a complete Abstract Syntax Tree.

**Can this detect SQL injection attempts?**
Yes. Parse the query into AST and inspect the node types. If user input resulted in unexpected DROP, DELETE, UNION, or subquery nodes, the injection is exposed structurally — no regex guessing.

**Which SQL dialects are supported?**
15+: MySQL, PostgreSQL, MariaDB, SQLite, BigQuery, Snowflake, Hive, TransactSQL, FLINKSQL, FlinkSQL, PostgresQL, and more.

**Can I rebuild SQL from the AST?**
Yes. Parse SQL→AST, modify the AST programmatically (add WHERE clauses, change table names), and rebuild valid SQL. Full round-trip support.