Database · MODEL CONTEXT PROTOCOL
narinzar agents sql mcp
narinzar agents sql mcp is a community-listed Model Context Protocol server. Review the source repository before installation.
WHAT IT CONNECTS
What narinzar agents sql mcp gives an AI Agent
- **Natural Language Queries**: Ask questions about your data in plain English
- **SQL Query Generation**: Automatically generates SQL queries based on user input
- **Query Execution**: Executes the generated queries against a connected SQLite database
- **Result Formatting**: Displays query results in a readable format
- **Error Handling**: Provides clear explanations when queries fail
- **Example Queries**: Includes sample queries to help users get started
- **Web Interface** (`app.py`): A Gradio web interface that allows users to interact with the AI
- **MCP Server** (`mcp_server.py`): A server that handles SQL query execution
- **MCP Client** (`mcp_client.py`): A client that facilitates communication between the web interface and the MCP server
- Python 3.8+
- An Anthropic API key (to access Claude)
- Clone this repository:
- Install dependencies:
- Create a `.env` file in the project root with your Anthropic API key:
- Start the application:
- Open your web browser and navigate to:
- Type in a natural language query and press Enter or click Submit.
- "List all tables in the database"
- "Show me all users"
- "Count how many products cost more than $300"
- "What's the total value of all products in stock?"
- "Insert a new product called 'Tablet' with price $499.99 and stock 5"
- "Update the price of Laptop to $1099.99"
- `id`: INTEGER (Primary Key)
- `name`: TEXT
- `email`: TEXT
- `created_at`: TIMESTAMP
- `id`: INTEGER (Primary Key)
- `name`: TEXT
- `price`: REAL
- `stock`: INTEGER
- Understand natural language queries
- Generate appropriate SQL queries
- Explain query results to users
- Lightweight
- Serverless
- Self-contained
- Zero-configuration
- Replace the database initialization in `app.py` with your own database file path
- Update the sample table documentation in the UI to match your schema
- Currently only supports SQLite databases
- Complex queries might require refinement
- Limited error recovery for malformed queries
SECURITY
An MCP listing is not a security review
An MCP server may receive model context, credentials, local files, or permission to call external systems. Review its code, requested environment variables, network behavior, package provenance, and maintenance status before connecting it to an agent.
README
Repository documentation
A natural language interface for interacting with SQLite databases, powered by Anthropic's Claude AI.
This application allows users to interact with SQLite databases using natural language. Instead of writing SQL queries manually, users can ask questions or give instructions in plain English, and the AI will generate and execute the appropriate SQL queries, returning formatted results.
- Natural Language Queries: Ask questions about your data in plain English
- SQL Query Generation: Automatically generates SQL queries based on user input
- Query Execution: Executes the generated queries against a connected SQLite database
- Result Formatting: Displays query results in a readable format
- Error Handling: Provides clear explanations when queries fail
- Example Queries: Includes sample queries to help users get started
The application consists of three main components:
- Web Interface (
app.py): A Gradio web interface that allows users to interact with the AI - MCP Server (
mcp_server.py): A server that handles SQL query execution - MCP Client (
mcp_client.py): A client that facilitates communication between the web interface and the MCP server
- Python 3.8+
- An Anthropic API key (to access Claude)
-
Clone this repository:
git clone https://github.com/yourusername/ai-sql-assistant.git cd ai-sql-assistant -
Install dependencies:
pip install -r requirements.txt -
Create a
.envfile in the project root with your Anthropic API key:ANTHROPIC_API_KEY=your_api_key_here
-
Start the application:
python app.py -
Open your web browser and navigate to:
http://localhost:7860 -
Type in a natural language query and press Enter or click Submit.
Here are some example queries you can try:
- "List all tables in the database"
- "Show me all users"
- "Count how many products cost more than $300"
- "What's the total value of all products in stock?"
- "Insert a new product called 'Tablet' with price $499.99 and stock 5"
- "Update the price of Laptop to $1099.99"
The application creates a sample SQLite database with the following tables:
id: INTEGER (Primary Key)name: TEXTemail: TEXTcreated_at: TIMESTAMP
id: INTEGER (Primary Key)name: TEXTprice: REALstock: INTEGER
The application uses MCP to manage communication between Claude and the SQLite database. MCP allows Claude to call functions (tools) and receive results, creating an interactive feedback loop.
The application uses Anthropic's Claude 3.7 Sonnet model to:
- Understand natural language queries
- Generate appropriate SQL queries
- Explain query results to users
The application uses SQLite for database operations. SQLite is:
- Lightweight
- Serverless
- Self-contained
- Zero-configuration
To use your own SQLite database instead of the sample one:
- Replace the database initialization in
app.pywith your own database file path - Update the sample table documentation in the UI to match your schema
The system prompt that guides Claude's behavior can be modified in the ChatProcessor class in app.py.
- Currently only supports SQLite databases
- Complex queries might require refinement
- Limited error recovery for malformed queries
Contributions are welcome! Please feel free to submit a Pull Request.
This project is licensed under the MIT License - see the LICENSE file for details.
COMMUNITY