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:
- Install dependencies:
- Start Ollama:
- Run the server:
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
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
Development
Project Structure
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:
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
This server cannot be installed
local-only server
The server can only run on the client's local machine because it depends on local resources.
Enables natural language database operations and semantic document search through SQLite and vector database integration. Converts plain English instructions into SQL queries and provides RAG capabilities for uploaded documents.
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