AnyDbApp by iamayuppie - MCP Server

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 query
instruction (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 table
data (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 table
instruction (required): Update instruction
conditions (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 table
conditions (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 table
schema_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 file
content (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 similarity
max_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 scrape
custom_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 content
max_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

iamayuppie/AnyDbApp

AnyDB MCP Server

Features

Core Database Operations

Vector Database & RAG

Web Scraping & Knowledge Base (NEW!)

Available Tools

Database Tools

1. query_entity

2. insert_entity

3. update_entity

4. delete_entity

5. create_table

6. sql_query

7. sql_execute

Vector Database Tools (NEW!)

8. add_file_to_vector_db

9. search_vector_db

10. list_vector_files

11. remove_file_from_vector_db

Web Scraping Tools (NEW!)

12. scrape_url

13. query_web_content

14. list_scraped_pages

15. remove_scraped_page

Installation

Prerequisites

Setup

Claude Desktop Integration

Configuration

Ollama Settings

Database Settings

Usage Examples

Database Operations

Smart Schema Evolution

Vector Database & File Operations

Web Scraping & Knowledge Base

Architecture

Development

Project Structure

Key Components

Troubleshooting

Common Issues

Debug Mode

License

Contributing

Support

1. `query_entity`

2. `insert_entity`

3. `update_entity`

4. `delete_entity`

5. `create_table`

6. `sql_query`

7. `sql_execute`

8. `add_file_to_vector_db`

9. `search_vector_db`

10. `list_vector_files`

11. `remove_file_from_vector_db`

12. `scrape_url`

13. `query_web_content`

14. `list_scraped_pages`

15. `remove_scraped_page`