What happens if my schema fails validation?

The MCP returns a specific verdict like QUERY_BLIND or SCALE_UNPLANNED. This tells your agent exactly which architectural principle was violated so it can fix the design.

Can I use this with Cursor or VS Code?

Yes. You can connect this MCP to any compatible client including Claude, Cursor, Windsurf, and VS Code through the Vinkius platform.

Does this MCP actually create the database?

No. This MCP is a validation and reasoning capability. It helps your agent design a correct schema that you can then use to generate SQL.

How does it handle large tables?

It checks for scalability by verifying if you have planned for partitioning and used appropriate datatypes like BIGINT for high-volume primary keys.

Why does it reject 'we will add indexes later'?

The MCP treats missing indexes as a production risk. It requires explicit index definitions to prevent full table scans at scale.

Does this Prover generate SQL queries automatically?

No. The AI agent generates the code. This capability validates the relational architectural design, forcing index plans, relationship rules, and scaling bounds before writing SQL.

Can I use this for non-relational schemas (like MongoDB)?

Yes. The core validation concepts (access pattern indexing, scaling capacity, and references) apply equally to document schemas and key-value distributions.

How does the capability enforce schema consistency?

The engine uses structured decision pivots. If the proposed layout misses index patterns or foreign keys, the validation engine rejects the execution step.

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Connect Task Planner to ChatGPT, Claude and Gemini

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Build database schemas that actually survive production traffic.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 1 capability

We ship only the actions that matter.

Capability set01 / 01

01

1 capability in this set.

Part of 1 available through Database Architect Prover.

01
Validate database schema
This capability acts as a rigorous DBA validator that checks schema designs against normalization, indexing, relationship, and scalability standards. It forces your agent to provide specific justifications for denormalization, define indexes based on actual access patterns, and plan for future data volume.

Real work

What teams actually run

New Feature Schema Design

Validate a new set of tables for a feature to ensure they won't cause performance issues as the user base grows.

Database Migration Auditing

Check proposed ALTER TABLE statements to ensure they won't lock large tables for extended periods.

Refactoring Legacy Schemas

Use the capability to identify normalization violations and missing relationships in existing designs.

Scaling High-Volume Systems

Verify that tables expected to hit millions of rows have proper partitioning and BIGINT primary keys.

Automate a task

Put Database Architect Prover to work on one thing.

Step-by-step automations your AI runs through this Connector: the workflow, the prompts to start with, and the exact capabilities behind each move.

One connector, every AI

Database Architect Prover works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

Building your own app? The connector is yours to use.

You don't need a client to put Database Architect Prover to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

879ms average. Fast in production.

Database Architect Prover is checked daily against the live service.

Daily averagePeak 1188ms

Oct 3Today

Fastest day: 700ms
Slowest day: 1188ms
14-day trend: Slowing+37%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 1 capability arrives ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Database Architect Prover, so you can see the experience inside your AI.

It does not authenticate your account with Database Architect Prover. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Database Architect Prover Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_LMNoXzHPAB9tqjZz8CXZmAXbvKHwFDqJGaGigTlv/mcp

Claude Desktop

Follow the steps below to connect in seconds.

1In Claude Desktop, open Settings → Connectors.
2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3Click Add and start a new chat — Database Architect Prover capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "task-planner-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_LMNoXzHPAB9tqjZz8CXZmAXbvKHwFDqJGaGigTlv/mcp"
    }
  }
}

Claude
ChatGPT
Cursor
VS Code
Windsurf
Claude Code
JetBrains
Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up Database Architect Prover in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Database Architect Prover owners hand off.

This MCP is built for engineers who need to move from AI-generated drafts to production-ready database architectures.

01
Backend Engineers
They use it to validate the structural integrity of schemas before writing migration scripts.
02
Database Administrators
They use it to audit AI-generated designs for missing indexes or improper datatypes.
03
Data Architects
They use it to ensure high-volume tables are planned for partitioning and proper scaling.

FAQ

Questions Database Architect Prover owners ask.

01
What happens if my schema fails validation?
The MCP returns a specific verdict like QUERY_BLIND or SCALE_UNPLANNED. This tells your agent exactly which architectural principle was violated so it can fix the design.
02
Can I use this with Cursor or VS Code?
Yes. You can connect this MCP to any compatible client including Claude, Cursor, Windsurf, and VS Code through the Vinkius platform.
03
Does this MCP actually create the database?
No. This MCP is a validation and reasoning capability. It helps your agent design a correct schema that you can then use to generate SQL.
04
How does it handle large tables?
It checks for scalability by verifying if you have planned for partitioning and used appropriate datatypes like BIGINT for high-volume primary keys.
05
Why does it reject 'we will add indexes later'?
The MCP treats missing indexes as a production risk. It requires explicit index definitions to prevent full table scans at scale.
06
Does this Prover generate SQL queries automatically?
No. The AI agent generates the code. This capability validates the relational architectural design, forcing index plans, relationship rules, and scaling bounds before writing SQL.
07
Can I use this for non-relational schemas (like MongoDB)?
Yes. The core validation concepts (access pattern indexing, scaling capacity, and references) apply equally to document schemas and key-value distributions.
08
How does the capability enforce schema consistency?
The engine uses structured decision pivots. If the proposed layout misses index patterns or foreign keys, the validation engine rejects the execution step.

Explore

More in Productivity

Connect Database Architect Prover to Claude, Cursor, ChatGPT & more

Your AI does the work. We keep it safe and sound.

Each Connector runs inside its own sealed environment.

One switch stops every agent. Instantly.

Spending limits that pause instead of surprise.

We ship only the actions that matter.

01

Validate database schema

What teams actually run

New Feature Schema Design

Database Migration Auditing

Refactoring Legacy Schemas

Scaling High-Volume Systems

Building your own app? The connector is yours to use.

Database Architect Prover Connector

Claude Desktop

Backend Engineers

Database Administrators

Data Architects

What happens if my schema fails validation?

Can I use this with Cursor or VS Code?

Does this MCP actually create the database?

How does it handle large tables?

Why does it reject 'we will add indexes later'?

Does this Prover generate SQL queries automatically?

Can I use this for non-relational schemas (like MongoDB)?

How does the capability enforce schema consistency?

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