AI Agent for GibsonAI MCP
Integrate GibsonAI’s Model Context Protocol (MCP) Server with your favorite development tools and IDEs to streamline database schema design, project management, SQL querying, and deployment workflows using natural language. Automate tasks, manage projects, and query databases directly from editors like Cursor, Windsurf, Claude Desktop, and VS Code with seamless authentication and robust tool support.
Unified Project Management & Schema Design
Effortlessly create, view, and manage GibsonAI projects and database schemas directly from your existing development environment. Visualize project structures, generate schema diagrams, and apply schema changes with automated migrations—all with natural language commands.
- Project Creation & Organization.
- Quickly generate new GibsonAI projects and maintain organized databases for all your applications.
- Visual Schema Diagrams.
- Automatically visualize database schemas, relationships, and structures for better understanding and collaboration.
- Automated Schema Migrations.
- Apply schema changes and trigger automatic migrations to keep your database up-to-date with zero manual effort.
- Natural Language Modeling.
- Submit schema modeling requests using plain English, reducing friction and accelerating development cycles.
Seamless Integration with Popular IDEs
GibsonAI MCP Server connects with tools like Cursor, Windsurf, Claude Desktop, and VS Code, allowing you to manage and deploy databases directly from your preferred editors. Enjoy fast setup, easy authentication, and a unified workflow across your stack.
- Multi-IDE Support.
- Integrates natively with Cursor, Windsurf, Claude Desktop, VS Code, and more for a seamless developer experience.
- Simple Authentication.
- Secure your workflow with CLI-based authentication and effortless connection to GibsonAI MCP Server.
- Unified Configuration.
- Configure once and use everywhere—manage MCP server connections across all supported editors with a single setup.
Automated Queries & Deployment
Run SQL queries, deploy projects to production, and fetch live database credentials all from within your development environment. GibsonAI MCP automates database operations and gives you full control over your data lifecycle with intuitive tools.
- Instant SQL Queries.
- Run complex SQL queries on your GibsonAI-hosted databases with secure, authenticated access.
- One-Click Deployment.
- Deploy your project schema to production or development with a single command from any supported IDE.
- Live Schema Sync.
- Fetch and compare working vs. live schemas to ensure your deployments are always up-to-date.
MCP INTEGRATION
Available GibsonAI MCP Integration Tools
The following tools are available as part of the GibsonAI MCP integration:
- get_projects
Retrieves all GibsonAI projects associated with the authenticated user, helping locate projects by name or UUID.
- create_project
Creates a new GibsonAI project and assists in updating the project reference file with the new UUID.
- get_project_details
Returns metadata and configuration information for a specified project by its UUID.
- get_project_hosted_database_details
Provides credentials, connection strings, and other details necessary to access the hosted GibsonAI database.
- update_project
Updates the name of a project using its UUID for easy project identification and management.
- submit_data_modeling_request
Handles natural language data modeling requests to create or modify database schemas using GibsonAI's internal modeler.
- deploy_project
Deploys the current schema to all supported databases by triggering automatic schema migrations.
- get_project_schema
Retrieves the current working schema, including any unpublished or un-deployed changes.
- get_deployed_schema
Fetches the schema currently deployed to the primary hosted database for comparison or verification.
- query_database
Executes SQL queries against a database using the associated API key, supporting various SQL dialects.
Supercharge Your Database Projects with GibsonAI
Experience seamless project setup, schema design, and instant deployments directly from your favorite IDE. Discover how GibsonAI can accelerate your workflow—book a demo or try it free today!
What is GibsonAI
GibsonAI is a technology company specializing in AI-powered cloud databases. Their platform allows users to design, deploy, manage, and scale serverless SQL databases instantly with the help of artificial intelligence. GibsonAI aims to simplify database management for developers and enterprises by automating complex tasks such as schema optimization, scaling, and deployment. With a focus on performance and reliability, GibsonAI helps organizations reduce operational overhead, accelerate development cycles, and focus on building features rather than managing infrastructure. Their solutions are targeted toward businesses seeking to leverage advanced AI capabilities to streamline their data operations and achieve faster time-to-market.
Capabilities
What we can do with GibsonAI
GibsonAI provides a suite of features that enable seamless creation and management of cloud databases using AI-driven automation. Users can quickly launch production-ready databases, leverage intelligent optimization, and scale resources on demand without manual intervention.
- Instant database deployment
- Launch serverless, production-grade SQL databases in seconds with AI assistance.
- AI-driven optimization
- Automatically optimize database schemas and queries for peak performance.
- Effortless scaling
- Scale databases up or down automatically based on workload demands.
- Automated backups & security
- Benefit from built-in backup and security features managed by the platform.
- Developer-friendly API
- Integrate and manage databases programmatically via robust APIs and documentation.
How AI Agents Can Benefit from GibsonAI
AI agents and autonomous systems can leverage GibsonAI to manage their backend data infrastructure without human intervention. By utilizing GibsonAI's automated database deployment, optimization, and scaling features, AI agents can focus on high-level tasks such as analytics, decision-making, and feature development. This reduces downtime, prevents bottlenecks, and ensures reliable performance for data-intensive applications.