AnyDB MCP Server
A Model Context Protocol (MCP) server that provides intelligent database operations through natural language processing. This server integrates SQLite databases with Ollama for AI-powered SQL generation and execution.
Features
Core Database Operations
- Natural Language to SQL: Convert plain English instructions into SQL queries using Ollama
- Universal Database Operations: Works with any SQLite table/entity without predefined schemas
- Automatic Schema Evolution: Dynamically adds columns to tables when new data requires them
- MCP Integration: Seamlessly integrates with Claude Desktop and other MCP-compatible clients
- Async Operations: Built on modern Python async/await for high performance
- Safety First: Separate tools for read and write operations
Vector Database & RAG
- File Embedding: Automatically convert files into vector embeddings for semantic search
- Semantic Search: Find relevant content using natural language queries instead of exact keyword matching
- RAG Support: Enable Claude Desktop to answer questions about uploaded documents with context
- Smart Chunking: Intelligently splits large documents into overlapping chunks for better retrieval
- Persistent Storage: ChromaDB-powered vector database with automatic embedding generation
Web Scraping & Knowledge Base (NEW!)
- URL Scraping: Extract and store content from web pages automatically
- Smart Content Extraction: Clean HTML and extract meaningful text with metadata
- URL Security: Built-in validation and security checks to prevent malicious URLs
- Semantic Web Search: Query scraped web content using natural language
- Web Knowledge Management: List, search, and manage scraped web pages
Available Tools
Database Tools
1. query_entity
Query any table with natural language instructions.
Parameters:
entity_name(required): Name of the table to queryinstruction(optional): Natural language query instruction (default: "SELECT all records")
Example: Query users table for active accounts
2. insert_entity
Insert records into any table using natural language descriptions. Automatically adds new columns if the data contains fields not present in the current table schema.
Parameters:
entity_name(required): Name of the tabledata(required): Data to insert (JSON or natural description)
Example: Insert a new user with email and name Auto-Schema: If inserting "user with name John, email john@test.com, and premium status" into a table that only has name/email columns, the system will automatically add a "status" column
3. update_entity
Update records in any table with conditions. Automatically adds new columns if the update introduces new fields.
Parameters:
entity_name(required): Name of the tableinstruction(required): Update instructionconditions(optional): WHERE conditions
Example: Update user status to active where email matches Auto-Schema: If updating with "set premium status to gold and loyalty points to 100" on a table without these columns, they will be added automatically
4. delete_entity
Delete records from any table with optional conditions.
Parameters:
entity_name(required): Name of the tableconditions(optional): WHERE conditions for deletion
Example: Delete inactive users older than 30 days
5. create_table
Create new tables with AI-generated schemas.
Parameters:
entity_name(required): Name of the new tableschema_description(required): Description of table schema
Example: Create a products table with name, price, and category
6. sql_query
Execute raw SQL SELECT queries directly.
Parameters:
query(required): SQL query to execute
Example: Direct SQL for complex joins and analytics
7. sql_execute
Execute raw SQL modification queries (INSERT, UPDATE, DELETE, CREATE, etc.).
Parameters:
query(required): SQL query to execute
Example: Direct SQL for complex data modifications
Vector Database Tools (NEW!)
8. add_file_to_vector_db
Add a file to the vector database for semantic search and RAG (Retrieval Augmented Generation).
Parameters:
filename(required): Name of the filecontent(required): Content of the file (text)metadata(optional): Optional metadata for the file
Example: Add a document about machine learning for later semantic search
9. search_vector_db
Search the vector database for relevant file content using semantic similarity.
Parameters:
query(required): Search query for semantic similaritymax_results(optional): Maximum number of results to return (default: 5)
Example: Find documents related to "neural networks and AI"
10. list_vector_files
List all files stored in the vector database.
Parameters: None
Example: View all documents available for search
11. remove_file_from_vector_db
Remove a file from the vector database.
Parameters:
filename(required): Name of the file to remove
Example: Delete outdated documents from the knowledge base
Web Scraping Tools (NEW!)
12. scrape_url
Scrape content from a web page and store it in the vector database for semantic search and RAG.
Parameters:
url(required): URL of the web page to scrapecustom_filename(optional): Custom filename for the scraped content
Example: Scrape a Wikipedia article or documentation page for later querying
13. query_web_content
Query scraped web page content using semantic search to find relevant information.
Parameters:
query(required): Search query for finding relevant web contentmax_results(optional): Maximum number of results to return (default: 5)
Example: Search scraped web pages for information about specific topics
14. list_scraped_pages
List all scraped web pages stored in the vector database with metadata.
Parameters: None
Example: View all websites you've scraped and stored
15. remove_scraped_page
Remove a scraped web page from the vector database.
Parameters:
filename(required): Filename of the scraped page to remove
Example: Clean up outdated or unwanted scraped web content
Installation
Prerequisites
- Python 3.8+
- Ollama running locally
- Claude Desktop (for MCP integration)
Setup
- Clone the repository:
git clone https://github.com/iamayuppie/AnyDbApp.git
cd AnyDbApp
- Install dependencies:
pip install -r requirements.txt
- Start Ollama:
ollama serve --port 1434
ollama pull llama3.1 # or your preferred model
- Run the server:
python main.py
Claude Desktop Integration
Add this server to Claude Desktop by editing your config file:
Windows: %APPDATA%\Claude\claude_desktop_config.json
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
{
"mcpServers": {
"anydb": {
"command": "python",
"args": ["C:\\Path\\To\\AnyDbApp\\mcp_server_stdio.py"],
"env": {
"PYTHONPATH": "C:\\Path\\To\\AnyDbApp"
}
}
}
}
Restart Claude Desktop to connect the server.
Configuration
Ollama Settings
Default configuration in mcp_server.py:
- Host: localhost
- Port: 1434
- Model: llama3.1
Database Settings
- Default DB:
anydb.sqlite(created automatically) - Location: Same directory as the server
- Type: SQLite with foreign key constraints enabled
Usage Examples
Once integrated with Claude Desktop, you can use natural language:
Database Operations
- "Create a users table with id, name, email, and created_at fields"
- "Show me all active users from the last 30 days"
- "Insert a new product: iPhone 15, price $999, category Electronics"
- "Update all pending orders to processed where amount > 100"
- "Delete test users where email contains 'test'"
Smart Schema Evolution
- "Insert a book: Title 'AI Handbook', Author 'Jane Doe', ISBN '123456', Format 'Hardcover'" (automatically adds Format column if missing)
- "Add employee with name John, salary 75000, department IT, and remote status" (adds department and remote columns as needed)
- "Update product with warranty period 2 years and eco-friendly rating A+" (dynamically expands product schema)
Vector Database & File Operations
- "Add this document to the knowledge base" (when attaching a file in Claude Desktop)
- "Search for information about machine learning algorithms"
- "Find documents related to user authentication and security"
- "What does the uploaded contract say about payment terms?"
- "Show me all documents I've added to the database"
- "Remove the old privacy policy document"
Web Scraping & Knowledge Base
- "Scrape this Wikipedia article about neural networks: https://en.wikipedia.org/wiki/Neural_network"
- "Save the content from this blog post for later reference"
- "What information did I scrape about machine learning from that research paper website?"
- "Search my scraped web pages for information about Python best practices"
- "Show me all the web pages I've scraped and stored"
- "Remove the outdated documentation I scraped last month"
Architecture
┌─────────────────┐ ┌──────────────┐ ┌─────────────────┐
│ Claude │────│ MCP Server │────│ Ollama │
│ Desktop │ │ (stdio) │ │ (localhost) │
│ + File Upload │ │ │ │ │
└─────────────────┘ └──────────────┘ └─────────────────┘
│ │
▼ ▼
┌──────────────────┐ ┌─────────────────┐
│ Dual Storage │ │ Web Scraper │
│ │ │ Engine │
│ ┌──────────────┐ │ │ │
│ │ SQLite │ │◄───┤ • URL Validation│
│ │ Database │ │ │ • Content Extract│
│ └──────────────┘ │ │ • Security Checks│
│ │ └─────────────────┘
│ ┌──────────────┐ │ │
│ │ ChromaDB │ │◄────────────┘
│ │ Vector Store │ │ ← Web Content +
│ └──────────────┘ │ Document Embeddings
└──────────────────┘ & Semantic Search
Development
Project Structure
AnyDbApp/
├── main.py # Clean entry point with startup info
├── mcp_server.py # MCP server setup and tool routing
├── dbtool.py # Database operations and SQL tools
├── filetool.py # Vector database and file operations
├── webscrapertool.py # Web scraping and content extraction
├── requirements.txt # Python dependencies
├── pyproject.toml # Project metadata
└── README.md # This file
Key Components
Core Modules:
- main.py: Entry point with dependency checking and startup information
- mcp_server.py: MCP protocol implementation, tool registration, and request routing
- dbtool.py: Database operations, SQL generation, and data management
- filetool.py: Vector database operations, file processing, and semantic search
- webscrapertool.py: Web scraping, content extraction, and URL processing
Business Logic Classes:
- DatabaseManager: Handles async SQLite operations and database connections
- DatabaseTools: High-level database operations with natural language support
- OllamaClient: Manages AI model communication for SQL generation
- VectorDatabaseManager: Manages ChromaDB operations and document embeddings
- FileTools: High-level file operations and semantic search functionality
- WebScraperManager: Handles web page fetching, content extraction, and URL validation
- WebScraperTools: High-level web scraping operations with vector database integration
Troubleshooting
Common Issues
- Server won't start: Check if Ollama is running on port 1434
- No tools showing in Claude: Verify MCP config path and restart Claude Desktop
- SQL errors: Check table names and ensure proper natural language descriptions
- Ollama connection failed: Confirm Ollama model is installed and accessible
Debug Mode
Run with Python's verbose mode for detailed logs:
python -v main.py
License
This project is open source. See LICENSE file for details.
Contributing
- Fork the repository
- Create a feature branch
- Make your changes
- Add tests if applicable
- Submit a pull request
Support
For issues and questions:
- Check the troubleshooting section
- Review Ollama and MCP documentation
- Open an issue on the repository