ORMCP

The Model Context Protocol (MCP) is an open standard that provides a unified way for AI models to interact with external tools and data sources. It standardizes communication, making it easier to integrate LLMs into complex workflows without building custom API integrations for every use case.

- ✅ Standardized Interface:Fully compliant with the Model Context Protocol (MCP) specification
- 🌐 Database Agnostic:Works with any JDBC-compliant database (e.g., PostgreSQL, MySQL, Oracle, SQL Server, DB2, SQLite)
- ↔️ Bi-directional Data Flow:Seamless AI ↔ Database communication with optional support for only READONLY operations
- 🔄 Object-Relational Mapping (ORM):JSON object operations (CRUD) transparently mapped to relational data
- 🔒 Secure Data Access: Domain model-specific operations promote data protection
- 🧾 Declarative ORM Specification:Intuitive, non-intrusive, and flexible ORM specification based on a simple grammar
- 🕸️ Support for Complex Object Modeling:Including one-to-one, one-to-many, and many-to-many relationships, and path-expressions
- 🖇️ Flexible Queries:Deep and Shallow queries, Various operational directives similar toGraphQLcapabilities to refine the shape and scope of returned objects
- 🚀 Highly Optimized and Lightweight Mapping Engine:Connection pooling, Prepared statements, Optimized SQL statements, Minimal database trips, Caching of metadata
- 🔌 Compatible with Existing Data and Databases:Works with existing schemas and data in any database; Does not require any native JSON data type
- 📚 Comprehensive Documentation:Detailed User manual and README files, API documentation, sample apps
- ☁️ Cloud Agnostic:Deploy anywhere with Docker support
- ⚡ High Performance:Built on versatile Gilhari microservice architecture and optimised ORM engine
- 🛡️ Robust Error Handling:Clear error messages and recovery mechanisms
- 📈 Scalable:Handles multiple concurrent requests efficiently; Scalable Docker deployment

+---------------------+ +----------------------+ +-------------------------+ | AI App / LLM Client | <---> | ORMCP Server | <---> | Relational Database | | (MCP-compliant tool)| | (MCP + Gilhari) | | (Postgres, MySQL, etc.) | +---------------------+ +----------------------+ +-------------------------+ | | | | JSON (via MCP Tools) | | |------------------------------->| | | | ORM + JDBC | | |-------------------------------->| | | | | JSON result (MCP format) | | |<-------------------------------| |

Important:The AI application (LLM client) translates natural language into MCP tool calls. ORMCP Server then translates these MCP tool calls into REST API calls to Gilhari.

ORMCP Serverbridges the gap between modern AI applications and relational databases through:

- MCP Protocol: Standardized AI-to-tool communication
- Gilhari: Integration layer with relational databases via ORM and JDBC
- JSON Mapping: Transparent object-relational mapping

The sections below cover all platforms together as a complete reference.

- Define lightweight object models for your relevant data
- Write a declarative ORM specification for those models in a text file using a simple (JDX) grammar

- Add models, ORM specification, and JDBC driver to a Dockerfile
- Build the Gilhari Docker image

- Connect ORMCP to the Gilhari microservice
- Start Gilhari, then ORMCP
- Interact with scoped relational data in an intuitive, object-oriented way using an AI Agent or MCP client

- Python 3.12+
- Docker (for Gilhari microservice)
- JDBC driver for your target database

ORMCP Server is available on public PyPI. No account, token, or beta-access request is needed to install it:

pip install ormcp-server # Verify installation pip show ormcp-server

# Create virtual environment (recommended on Linux/Mac) python3 -m venv .venv # Activate — Linux/Mac: source .venv/bin/activate # Activate — Windows (Command Prompt): .venv\Scripts\activate # Activate — Windows (PowerShell): .venv\Scripts\Activate.ps1 # Install pip install ormcp-server

If you have an existing Gemfury tokenfrom an earlier beta install, it will no longer work — Gemfury access has been discontinued. Usepip install ormcp-server, which pulls from public PyPI directly.

Iformcp-servercommand is not found after installation: