SQL Parser AST Engine MCP. Analyze Query Structure Before Running Code

Q: Does parsesql extract only table names or also column details?

The parsesql tool extracts both. It provides lists of all referenced tables and, critically, the fully qualified names for every single column used in the query.

SQL Parser AST Engine provides deep, programmatic analysis of any SQL query. Instead of just running code, this MCP breaks down the syntax into an Abstract Syntax Tree, revealing every table, column, and join condition. It's essential for security teams checking for unauthorized data access or DevOps engineers validating complex database migrations across 15+ dialects like PostgreSQL, BigQuery, and Snowflake.

Claude

ChatGPT

Cursor

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Windsurf

VS Code

JetBrains

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Detecting unauthorized table access

The MCP analyzes the query to see exactly which tables are referenced, allowing you to enforce data governance rules before execution.

Extracting schema details

It lists every column and table name used in a query, providing fully qualified names necessary for auditing or documentation generation.

Validating SQL syntax fidelity

You can parse an existing query into an AST and then reconstruct valid SQL from that tree structure to ensure full compatibility across different database dialects.

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AI Agent

What AI agents can do with SQL Parser AST Engine: 1 Tool

The listed tool allows you to take any SQL query text and convert it into a detailed, machine-readable Abstract Syntax Tree for deep analysis.

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Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

Start using SQL Parser AST Engine MCP

Parse Sql

Sends an SQL query and receives its Abstract Syntax Tree (AST) structure, along with lists of all tables, columns, and clauses used...

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SQL Parser AST Engine MCP is compatible with Claude

Claude AI

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

Start a conversation

Open a new chat. The SQL Parser AST Engine integration is available immediately — no restart needed.

Antigravity

Configure Agent Environment

Open your Antigravity agent's workspace configuration or mcp-servers.json file.

Bind the Endpoint

Add the Vinkius endpoint URL to your agent's MCP connections list:

"mcp_servers": {
  "sql-parser-ast-engine": {
    "serverUrl": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
  }
}

Provide your secure token in place of [YOUR_TOKEN_HERE] to ensure your agent requests are authenticated.

Execute

Start your Antigravity session. The agent will autonomously discover and utilize the SQL Parser AST Engine tools with full Vinkius guardrails applied.

SQL Parser AST Engine MCP is compatible with VS Code

VS Code Copilot

⚡

One-Click Install (Recommended)

In your Vinkius Dashboard, simply click the Add to VS Code button for this server. We'll automatically configure your local workspace.

Or configure manually

Open MCP Settings

Open VS Code, press Ctrl/Cmd + Shift + P, and search for GitHub Copilot: MCP Servers.

Add Server Config

Add the Vinkius endpoint configuration to your mcp-servers.json file:

"sql-parser-ast-engine": {
  "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}

Ensure you replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.

LangChain

Install Dependencies

Install the LangChain MCP adapters for your environment:

pip install langchain-mcp-adapters

Connect the Server

Use the SSEClient in LangChain to connect to the Vinkius managed endpoint:

from langchain_mcp_adapters.client import SSEClient

# Connect to Vinkius
client = SSEClient(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
tools = client.get_tools()

CrewAI

Define the Tool

Load the Vinkius MCP tools into your CrewAI agents:

from crewai import Agent
from mcp_crewai import MCPTool

# Connect securely to Vinkius
vinkius_tools = MCPTool(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

# Assign to Agent
researcher = Agent(
    role='Data Researcher',
    tools=vinkius_tools.get_all()
)

Execute Task

Run your CrewAI process. The agent will autonomously route tasks to the Vinkius managed server.

Choose How to Get Started

Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.

Build Your Own

Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

Import from OpenAPI, Swagger, or YAML specs
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Publish to catalog or keep private

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Start with SQL Parser AST Engine, then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.

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The Pain of Manual Query Audits

Right now, when a query looks suspicious or needs auditing, you're stuck manually reading long blocks of code. You copy the SQL into a text editor, hunting for keywords like 'DROP', 'UNION', or specific table names. If the malicious intent is hidden in a subquery nested three layers deep, or disguised by comments and complex JOINs, your manual review fails instantly.

With this MCP, you feed that suspicious query directly to the parser. It immediately generates an Abstract Syntax Tree—a clean, structured object that exposes every single element. You get an objective list of all tables and columns used, regardless of how complicated or obfuscated the original text was.

How the SQL Parser AST Engine Gives You Structural Proof

The manual effort disappears because you no longer need to hunt for keywords. Instead, your agent uses the `parse_sql` tool to generate a verifiable blueprint. It gives you an explicit list of all referenced data assets, allowing immediate comparison against your allowed schema lists.

It’s a fundamental shift from guessing what the code might do to knowing exactly how it's built. You move from reactive security patches to proactive structural enforcement.

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sql-parsing

ast

query-analysis

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database-security

code-inspection

What SQL Parser AST Engine MCP does for your AI

When you deal with SQL queries from user input, the biggest risk isn't just bad syntax; it's what might be hidden inside. This MCP doesn't execute your code. Instead, it takes a raw query string and converts it into an Abstract Syntax Tree (AST). Think of the AST as a detailed blueprint of the query, showing exactly how every piece connects—every table reference, every column name, every join type, and even nested subqueries.

This structured view is critical for governance. Your agent can now inspect the query's intent without ever risking execution. It lets you programmatically check if a query accesses tables outside of an allowed list or if it contains malicious patterns like injection attempts. Because this MCP supports over 15 major SQL dialects, from MySQL to Snowflake, you get one reliable source for analyzing database language across your entire stack.

You'll find this engine cataloged and managed easily on Vinkius, giving your AI client a single connection point for all your parsing needs.

Built · Hosted · Managed by Vinkius SQL Parser AST Engine - Analyze Query Structure

Server ID 019e38f1-f2c9-707d-9e5d-e2e6c57a179b

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Compliance Grade A+

Score 100/100

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Benefits of connecting SQL Parser AST Engine MCP

Security checks are airtight. By using the parse_sql tool, you don't trust the query text; you inspect its underlying structure for unauthorized operations or injection patterns.

Data governance compliance becomes automated. You can use this MCP to extract and list every table and column referenced in any given SQL query, essential for auditing data access rights.

It handles database diversity. Since it supports 15+ dialects—including Snowflake, MariaDB, and SQLite—you get one tool that works reliably across your entire mixed-environment stack.

Debugging complex logic is easier. You can parse a working query into an AST, then rebuild SQL from the tree to spot subtle syntax differences between dialect versions.

The analysis happens safely. The MCP reads the structure; it never executes the code. This means you get deep insight without any risk of running malicious or faulty queries.

SQL Parser AST Engine MCP use cases

01 01

Auditing a third-party API query

A marketing team submits an ad copy that includes an SQL snippet for data extraction. You feed the snippet to your agent, which uses parse_sql to verify that the query only touches the 'leads' and 'campaigns' tables, flagging any attempt to access internal HR records.

02 02

Validating a cross-platform migration

Your DevOps pipeline needs to migrate a schema from PostgreSQL to BigQuery. Before deployment, you run the parse_sql tool on critical DDL statements across both dialects to ensure all necessary clauses and types are correctly represented in the AST.

03 03

Building a query validation layer

You're building an internal data portal where users submit custom queries. Your agent intercepts every request, using parse_sql to check for disallowed functions or table joins before sending it to the database.

04 04

Analyzing user-submitted search logic

A client gives you a complex SQL query meant for their analytics dashboard. You use the MCP's parsing capabilities to extract all referenced columns, giving your data governance team an immediate list of assets needing review.

SQL Parser AST Engine MCP tradeoffs

What to watch out for, and the recommended way to handle each one.

Treating queries as pure text

Avoid

Relying on regex or simple string matching to check for keywords like 'DROP TABLE' or 'SELECT *'. These methods are easily fooled by subqueries, comments, or obfuscated syntax.

Instead

Use the parse_sql tool. It builds a formal Abstract Syntax Tree that understands the query's structure, making it impossible to hide unauthorized operations within comments or complex clauses.

Running queries in a sandbox

Avoid

Executing the query against a test environment just to see if it throws an error. This is slow, expensive, and still requires running potentially harmful code.

Instead

Use parse_sql first. Analyze the AST locally to identify schema violations or security risks without ever touching a live database connection.

Assuming dialect consistency

Avoid

Writing validation logic based solely on PostgreSQL syntax, only to have it fail when deployed against a Snowflake data warehouse.

Instead

This MCP supports 15+ dialects. Use parse_sql with the specific target dialect specified to validate the query structure correctly for your intended database environment.

Frequently asked questions about SQL Parser AST Engine MCP

Can the SQL Parser AST Engine handle dialects I haven't used before? +

Yes, this MCP supports 15+ major dialects, including MySQL, PostgreSQL, BigQuery, and Snowflake. You specify the dialect when calling parse_sql to ensure accurate parsing for your environment.

Does using the SQL Parser AST Engine execute the query against my database? +

No. This MCP analyzes the syntax structure (the Abstract Syntax Tree) and never executes the code. It's purely a reading and validation tool, making it safe for use with untrusted input.

What is an Abstract Syntax Tree in relation to SQL? +

The AST is a structured representation of the query's logic. Instead of seeing text like 'SELECT * FROM t1 JOIN t2', you receive data showing: 'Operation: SELECT; Target Columns: (*); Source 1: (t1); Join Type: INNER; Source 2: (t2)'. This structure is what makes it useful.

Is this better than just using a standard database client for validation? +

Yes. A database client only validates against its own engine rules. The MCP provides an external, programmatic audit layer that can check the query's structure against your custom governance policies (e.g., 'This user cannot access tables X, Y, and Z').

Does parse_sql extract only table names or also column details? +

The parse_sql tool extracts both. It provides lists of all referenced tables and, critically, the fully qualified names for every single column used in the query.

Give Claude and any AI agent real-world access

What AI agents can do with SQL Parser AST Engine: 1 Tool

Parse Sql

Sends an SQL query and receives its Abstract Syntax Tree (AST) structure, along with lists of all tables, columns, and clauses used...

Security and governance baked right in.

Claude AI

Open Claude Settings

Add Custom Connector

Start a conversation

Claude Code

Open your terminal

Add the MCP Server

Start coding

Cursor

One-Click Install (Recommended)

Open Cursor Settings

Add New Server

Use in Composer

Antigravity

Configure Agent Environment

Bind the Endpoint

Execute

VS Code Copilot

One-Click Install (Recommended)

Open MCP Settings

Add Server Config

Windsurf

One-Click Install (Recommended)

Open Windsurf Settings

Add Server Endpoint

LangChain

Install Dependencies

Connect the Server

CrewAI

Define the Tool

Execute Task

Choose How to Get Started

Build Your Own

Make Your AI Do More

The Pain of Manual Query Audits

How the SQL Parser AST Engine Gives You Structural Proof

sql-parsing

ast

query-analysis

security-firewall

database-security

code-inspection

What SQL Parser AST Engine MCP does for your AI

How to set up SQL Parser AST Engine MCP

Who uses SQL Parser AST Engine MCP

Benefits of connecting SQL Parser AST Engine MCP

SQL Parser AST Engine MCP use cases

Auditing a third-party API query

Validating a cross-platform migration

Building a query validation layer

Analyzing user-submitted search logic

SQL Parser AST Engine MCP tradeoffs

Treating queries as pure text

Running queries in a sandbox

Assuming dialect consistency

When to use SQL Parser AST Engine MCP

Frequently asked questions about SQL Parser AST Engine MCP