Configurable strict mode for production environments
Security-aware normalization with malicious pattern detection
Automatic Python-style JSON fixes (single quotes, True/False, None, trailing commas)

🛡️ Parameter Binding Interface

Enhanced MCP tools with optional params arrays for secure parameter binding
SQL injection prevention using SQLite's built-in parameter binding protection
Backward compatible - existing queries continue to work unchanged

📦 Automatic Parameter Serialization

Direct object/array parameters - no more manual JSON.stringify() required
Seamless integration - dict/list objects automatically serialized to JSON
Cleaner API for both humans and AI assistants

🧠 Enhanced JSON Error Diagnostics

Intelligent error categorization - structural, security, encoding issues identified
Contextual guidance with specific suggestions for fixing complex JSON problems
Security violation detection with clear messaging for suspicious patterns

📊 By the Numbers

73 Tools (up from 69) across 14 Categories (up from 13)
6 New JSON Helper Tools for simplified operations
5 Major Feature Enhancements across the platform
Zero Breaking Changes - all existing code continues to work

🚀 Quick Start

# Docker (Recommended)
docker pull writenotenow/sqlite-mcp-server:v2.6.1
docker run -i --rm -v $(pwd):/workspace writenotenow/sqlite-mcp-server:v2.6.1 --db-path /workspace/database.db

# Test all 73 tools
python test_runner.py --quick

🎯 JSON Helper Tools Examples

// Insert JSON with auto-normalization
json_insert({
  "table": "products",
  "column": "metadata",
  "data": {"name": "Product", "active": true, "tags": ["new"]}
})

// Update specific JSON paths
json_update({
  "table": "products", 
  "column": "metadata",
  "path": "$.pricing.discount",
  "value": 15.5
})

// Extract with multiple output formats
json_select({
  "table": "products",
  "column": "metadata", 
  "paths": ["$.name", "$.pricing.base_price"],
  "output_format": "structured"
})

🛡️ Enhanced Security

// Before v2.6.1 - Manual serialization required
write_query({
  "query": "INSERT INTO table (data) VALUES (?)",
  "params": [JSON.stringify({"key": "value"})]
})

// After v2.6.1 - Automatic serialization!
write_query({
  "query": "INSERT INTO table (data) VALUES (?)", 
  "params": [{"key": "value"}]  // Auto-serialized with security checks
})

🔧 Migration Guide

v2.6.1 is fully backward compatible! All existing code continues to work unchanged. New features are additive and optional.

To use new JSON Helper Tools:

Update to v2.6.1: docker pull writenotenow/sqlite-mcp-server:v2.6.0
Use new json_* tools for simplified operations
Enjoy automatic parameter serialization and enhanced security

📚 Documentation

Complete Documentation - Comprehensive guide with examples
Quick Start Guide - Get up and running in minutes
Docker Hub - Container images and deployment guides

🙏 Contributors

Special thanks to the community for feedback and testing that made this release possible!

Full Changelog: v2.5.0...v2.6.0

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21 Sep 03:26

neverinfamous

v2.5.0

6bb1503

🛡️ SQLite MCP Server v2.5.0 - Enhanced Security Release

September 21, 2025
🔒 Major Security Enhancement
This release introduces a comprehensive parameter binding interface to prevent SQL injection attacks, further hardening against the type of vulnerability found in the original Anthropic SQLite MCP server implementation.
🛡️ New Parameter Binding Interface
Enhanced Security for All Query Tools:
✅ read_query - Secure SELECT operations with parameter binding
✅ write_query - Protected INSERT/UPDATE/DELETE with parameters
✅ create_table - Safe table creation with parameter support
Usage Example:
🧪 Comprehensive Security Testing
NEW: SQL Injection Protection Test Suite
🎯 11 Attack Vectors Tested - Multiple statements, UNION injection, blind injection, comment-based attacks
🛡️ Parameter Binding Protection - All malicious payloads safely neutralized
📊 Security Assessment - Overall security posture: STRONG
⚡ Quick Validation - Run python tests/test_sql_injection.py to verify protection
🔄 Backward Compatibility
✅ Zero Breaking Changes - All existing queries continue to work
✅ Optional Parameters - Add params array only when needed
✅ Seamless Migration - No code changes required for existing implementations
✅ Performance Benefits - Query plan caching and optimization
📊 Tool Count Correction
Corrected Documentation - Updated all references to show accurate count of 69 tools
Client Display Note - Added explanation for MCP clients showing different counts
Comprehensive Coverage - 69 specialized tools across 13 categories
🚀 What's New
Security Features
🛡️ SQL Injection Prevention - Parameter binding interface
🔒 Attack Vector Testing - Comprehensive security validation
📝 Best Practice Compliance - Follows secure coding standards
⚡ Enhanced Performance - Query optimization and caching
Documentation Updates
📚 Parameter Binding Guide - Complete usage examples and best practices
🔢 Accurate Tool Count - Corrected documentation to show 69 tools
🛡️ Security Documentation - Comprehensive security testing guide
📖 Usage Examples - Secure vs insecure query patterns
Testing Enhancements
🧪 SQL Injection Test Suite - 11 attack vectors with comprehensive coverage
✅ Security Validation - Automated testing for injection protection
📊 Test Results - Clear security posture assessment
🚀 Quick Testing - 30-second validation with --quick flag
📦 Installation & Usage
Docker (Recommended)
Security Testing
Quick Validation
🔗 Resources
📚 Complete Documentation - Full feature reference
🛡️ Security Guide - SQL injection protection testing
🐳 Docker Hub - Container images
📖 Parameter Binding Demo - Interactive security demonstration
🏆 Why This Release Matters
🛡️ Enterprise Security - Production-ready SQL injection protection
🔄 Zero Disruption - Seamless upgrade with full backward compatibility
📊 Accurate Documentation - Corrected tool counts and comprehensive guides
🧪 Validated Protection - Comprehensive testing against real attack vectors
⚡ Performance Benefits - Enhanced query optimization and caching
Full Changelog: v2.4.0...v2.5.0

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20 Sep 16:10

neverinfamous

v2.4.0

5ed27b8

SQLite MCP Server v2.4.0 - Windows GeomFromText Compatibility + Major Optimizations

SQLite MCP Server v2.4.0 🚀

Released September 19, 2025

🎯 Major Improvements

✅ Windows SpatiaLite Compatibility Resolved

New Feature: Automatic GeomFromText preprocessing for Windows compatibility
Smart Conversions: Seamlessly converts GeomFromText calls in INSERT/UPDATE statements
Zero Breaking Changes: Existing code continues to work unchanged
Standards Compliant: Uses proper OGC GeomFromText syntax

📦 Massive Container Optimization

34% Size Reduction: Compressed container size reduced from 74.1MB to 47.3MB
Improved Performance: Faster pulls and deployments
Resource Efficiency: Lower storage and bandwidth requirements

🔧 What's New

Automatic Spatial Function Preprocessing

The server now intelligently converts GeomFromText calls for Windows compatibility:

\\sql
-- These now work seamlessly on Windows:
INSERT INTO spatial_table (geom) VALUES (GeomFromText('POINT(1 2)', 4326));
UPDATE spatial_table SET geom = GeomFromText('POINT(10 20)', 3857) WHERE id = 1;
\\

Automatic Conversions:

\GeomFromText('POINT(x y)', srid)\ → \MakePoint(x, y, srid)\
\GeomFromText('POINT(x y z)', srid)\ → \MakePointZ(x, y, z, srid)\ (3D points)
\GeomFromText('LINESTRING(...)', srid)\ → \GeomFromWKB(GeomFromText(...))\ (complex geometries)

🐳 Docker Images

\\�ash

Latest stable release

docker pull writenotenow/sqlite-mcp-server:latest
docker pull writenotenow/sqlite-mcp-server:v2.4.0
\\

📊 Release Stats

Container Size: 47.3MB (down from 74.1MB)
New Features: 1 major (GeomFromText preprocessing)
Test Coverage: +9 comprehensive test cases
Breaking Changes: None
Platform Support: Enhanced Windows compatibility

Full Changelog: v2.3.0...v2.4.0

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18 Sep 23:00

neverinfamous

v2.3.0

3010421

# 🚀 SQLite MCP Server v2.3.0 - "Testing & Validation"

🎯 Major New Feature: Comprehensive Test Suite

Test all 67 tools in 30 seconds!

This release introduces a complete testing infrastructure that makes it easier than ever to validate your SQLite MCP Server installation and discover the full feature set.

✨ What's New

🧪 Comprehensive Test Suite

67 tools tested across 13 feature categories
Smart environment detection - automatically detects capabilities
Multi-level testing: Quick (30s), Standard (2-3min), Full (5-10min)
Professional reporting with clear pass/fail status

🚀 Standalone Test Runner

No pytest required - runs independently
Easy CLI interface with multiple options
CI/CD ready with proper exit codes
Environment validation before testing

📊 New CLI Commands

# Quick validation (30 seconds)
python test_runner.py --quick

# Standard comprehensive test (recommended)
python test_runner.py --standard  

# Full test suite with edge cases
python test_runner.py --full

# Environment check only
python test_runner.py --check-env

# New CLI command (after pip install)
sqlite-mcp-test --quick

📚 Enhanced Documentation

Professional testing guide in tests/README.md
Updated main README with prominent testing section
Integration examples for CI/CD pipelines
Troubleshooting guides for common issues

🔧 Technical Improvements

Test Coverage

✅ Core Database Operations (8 tools) - CRUD, schema, transactions
✅ JSON Operations (12 tools) - JSONB, validation, extraction
✅ Text Processing (8 tools) - Regex, fuzzy matching, similarity
✅ Statistical Analysis (8 tools) - Descriptive stats, percentiles
✅ Vector/Semantic Search (6 tools) - Embeddings, similarity, hybrid
✅ Virtual Tables (6 tools) - CSV, R-Tree, series generation
✅ Backup/Restore (3 tools) - Database backup, integrity
✅ PRAGMA Operations (4 tools) - Configuration, optimization
⚠️ SpatiaLite Geospatial (8 tools) - Platform dependent
✅ Enhanced Virtual Tables (4 tools) - Smart CSV/JSON import
✅ Vector Optimization (4 tools) - ANN search, clustering
✅ MCP Resources/Prompts (4 tools) - Meta-awareness features

Smart Environment Detection

SQLite version and JSONB support detection
Python version compatibility checking
Optional dependencies availability (numpy, requests, Pillow)
SpatiaLite extension detection (graceful skip if unavailable)
MCP version reporting

Integration Features

Pytest compatibility - works alongside existing tests
Docker support - runs in containerized environments
Windows/Linux/macOS cross-platform support
CI/CD integration with proper exit codes

📈 Impact on User Experience

For New Users

Instant confidence - validate functionality in 30 seconds
Feature discovery - see all 67 tools in action
Environment troubleshooting - identify setup issues quickly

For Existing Users

Upgrade validation - ensure new versions work correctly
Regression testing - catch issues before they impact workflows
Professional deployment - enterprise-ready validation tools

For Developers

Contribution confidence - comprehensive test coverage
Integration testing - works with existing test infrastructure
Release validation - automated testing for all features

🐳 Docker Images

Updated Docker images are available:

# Latest version
docker pull writenotenow/sqlite-mcp-server:latest
docker pull writenotenow/sqlite-mcp-server:v2.3.0

# Test in Docker
docker run -i --rm writenotenow/sqlite-mcp-server:v2.3.0 --help

📦 Installation & Usage

Quick Start

# Install/upgrade
pip install mcp-server-sqlite

# Test everything works
python test_runner.py --quick

# Expected output:
# 🎉 EXCELLENT: 12/12 tools tested successfully!
# 💡 Your SQLite MCP Server is ready for production use!

From Source

git clone https://github.com/neverinfamous/sqlite-mcp-server.git
cd sqlite-mcp-server
pip install -r requirements.txt
python test_runner.py --standard

🔄 Migration from v2.2.0

No breaking changes! This is a pure feature addition:

✅ All existing functionality unchanged
✅ Same MCP tools and APIs
✅ Same configuration options
✅ Same Docker usage patterns
➕ New testing capabilities added

Simply upgrade and enjoy the new testing features!

🎯 Why This Release Matters

This release transforms the SQLite MCP Server from "feature-rich database tool" to "enterprise-ready, professionally tested database solution":

🏆 Professional credibility through comprehensive testing
📈 Easier adoption with instant validation
🔧 Better debugging with environment detection
🚀 Production confidence with thorough validation

🙏 Acknowledgments

Thanks to the Model Context Protocol team for the foundational SQLite MCP server that this project extends.

📋 Full Changelog

➕ Add comprehensive test suite covering all 67 tools
➕ Add standalone test runner with multi-level testing
➕ Add smart environment detection and capability validation
➕ Add professional testing documentation and guides
➕ Add CI/CD integration with proper exit codes
➕ Add sqlite-mcp-test CLI command for easy testing
🔄 Update all version references to v2.3.0
📚 Enhanced README with prominent testing section
📚 New comprehensive testing guide in tests/README.md

Docker Tags: latest, v2.3.0
Python Support: 3.10+
SQLite Support: 3.0+ (3.45+ recommended for JSONB)
Platforms: Windows, Linux, macOS

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17 Sep 11:39

neverinfamous

v2.2.0

69cd3ed

# 🎉 SQLite MCP Server v2.2.0: Advanced Text Processing

🎉 SQLite MCP Server v2.2.0: Advanced Text Processing

🚀 Major Feature Release

This release introduces a comprehensive Advanced Text Processing toolkit, transforming SQLite MCP Server into a powerful text analysis and processing platform with 67 total tools.

✨ NEW: Advanced Text Processing (8 Tools)

Pattern Extraction & Replacement

regex_extract: PCRE-style regex pattern extraction with capture groups and flags
regex_replace: Text replacement with backreferences and safe preview mode

Similarity & Matching

fuzzy_match: Levenshtein distance similarity matching with configurable thresholds
phonetic_match: Soundex and Metaphone phonetic matching for name variations
text_similarity: Multi-algorithm similarity analysis (Cosine, Jaccard, Levenshtein)

Text Processing & Validation

text_normalize: Multi-operation text normalization (case, Unicode, whitespace, punctuation)
advanced_search: Multi-method search combining exact, fuzzy, and phonetic techniques
text_validation: Comprehensive pattern validation (email, phone, URL, custom regex)

🔧 Key Features

PCRE Regex Support: Full Perl-compatible regex with flags (case-insensitive, multiline, dotall)
Fuzzy Matching: Intelligent similarity scoring with configurable thresholds
Phonetic Algorithms: Soundex implementation for sound-alike matching
Multiple Similarity Metrics: Cosine, Jaccard, and Levenshtein distance calculations
Safe Preview Modes: Preview destructive operations before execution
Comprehensive Validation: Email, phone, URL, and custom pattern validation
Performance Optimized: Configurable limits and efficient algorithms

🐛 Bug Fixes

Fixed: Duplicate WHERE clause SQL bug in text_normalize function
Improved: Rowid handling for better table compatibility
Enhanced: Error handling and validation across all text processing functions

📊 Expanded Capabilities

Total Tools: 67 (59 existing database/analytics + 8 new text processing)
Pure SQLite: No external dependencies required
MCP Compatible: Full Model Context Protocol integration
Enterprise Ready: Production-ready with comprehensive error handling

🔗 Integration Examples

# Extract email addresses with regex
regex_extract(table="contacts", column="info", pattern=r"[\w.-]+@[\w.-]+\.\w+")

# Find similar customer names with fuzzy matching  
fuzzy_match(table="customers", column="name", search_term="John Smith", threshold=0.8)

# Validate email addresses in bulk
text_validation(table="users", column="email", validation_type="email")

# Normalize text data with multiple operations
text_normalize(table="reviews", column="comment", 
               operations=["lowercase", "trim", "remove_extra_spaces"])

🎯 Perfect For

Data Cleaning: Normalize and validate text data
Fuzzy Matching: Find similar records with typos or variations
Pattern Extraction: Extract structured data from unstructured text
Data Validation: Ensure data quality with pattern validation
Text Analysis: Comprehensive text similarity and phonetic analysis

📈 Performance

Efficient Algorithms: Optimized for large datasets
Configurable Limits: Control performance vs accuracy trade-offs
Memory Optimized: Streaming operations for large text processing
SQLite Native: Leverages SQLite's performance optimizations

Full Documentation: See README.md for complete feature documentation and examples.

Docker: docker pull ghcr.io/neverinfamous/sqlite-mcp-server:v2.2.0

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17 Sep 09:40

neverinfamous

v2.1.0

f3eeec9

SQLite MCP Server v2.1.0 - Statistical Analytics Release

SQLite MCP Server v2.1.0 - Statistical Analytics Release
🔬 Major New Feature: Comprehensive Statistical Analysis
This release adds a complete statistical function library with 8 powerful tools for data analysis and business intelligence:
📊 New Statistical Functions:
descriptive_statistics - Complete statistical summary (mean, std dev, variance, quartiles, etc.)
correlation_analysis - Pearson correlation coefficient with relationship interpretation
percentile_analysis - Quartiles, percentiles, and interquartile range calculation
outlier_detection - IQR and Z-score methods for anomaly detection
regression_analysis - Linear regression with slope, intercept, and R² values
hypothesis_testing - One-sample t-tests with statistical significance testing
distribution_analysis - Distribution summary with histogram binning
moving_averages - Time series analysis with trend detection
🎯 Key Benefits:
Advanced Analytics: Professional-grade statistical analysis capabilities
Business Intelligence: Correlation analysis, regression modeling, and trend detection
Data Quality: Outlier detection and distribution analysis for data validation
Scientific Computing: Hypothesis testing with statistical significance
Time Series: Moving averages and trend analysis for temporal data
💡 Example Use Cases:
🔧 Technical Improvements:
Simplified SQL queries for better performance and reliability
Comprehensive error handling and edge case management
Clear, formatted output with statistical interpretation
Full integration with existing MCP framework
🚀 What's Next:
Medium Priority: Advanced text processing with PCRE regex support

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17 Sep 07:26

neverinfamous

v2.0.0

61e3385

🚀 v2.0.0: SpatiaLite Geospatial Analytics Integration

Major Release: Enterprise-Grade GIS Capabilities
✅ NEW FEATURES
SpatiaLite Integration with 7 new geospatial tools
Full Spatial Table Creation and indexing capabilities
Comprehensive Geometry Operations (distance, buffer, area, intersection, union)
Advanced Spatial Analysis (nearest neighbor, spatial joins, clustering)
Shapefile Import capabilities for real-world GIS data
Complete GIS Functionality for location-based business intelligence
🔧 ENHANCEMENTS
Automatic SpatiaLite Extension Loading with Windows DLL support
Persistent Spatial Metadata initialization across connections
Enhanced PATH Management for Windows compatibility
Robust Error Handling for spatial operations
📊 STATISTICS
Total Tools: 51 (was 44)
New Geospatial Tools: 7
Platform Support: Windows ✅, macOS ✅, Linux ✅
🎯 IMPACT
Transforms SQLite into enterprise-grade GIS platform
Enables Sophisticated location-based data analysis
Provides Professional spatial indexing and operations
Supports Complex geospatial business intelligence workflows
📝 USAGE NOTE
Minor limitation with GeomFromText() in INSERT statements has simple workaround documented in README.

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17 Sep 05:25

neverinfamous

v1.9.3

b00af75

SQLite MCP Server v1.9.3 - Enhanced Virtual Tables

🚀 SQLite MCP Server v1.9.3 - Enhanced Virtual Tables

🎯 NEW: Enhanced Virtual Tables (4 New Tools)

✨ Smart CSV Import with Type Inference

create_enhanced_csv_table - Automatic data type detection (INTEGER, REAL, TEXT, DATE)
analyze_csv_schema - Deep CSV analysis with statistics and confidence scores
Configurable null value handling and sampling
Clean column name sanitization for SQL compatibility

✨ JSON Collection Virtual Tables

create_json_collection_table - Support for JSONL and JSON array files
analyze_json_schema - Comprehensive nested structure analysis
Auto-format detection (JSONL vs JSON arrays)
Nested object flattening with configurable depth
Smart schema inference from sample records

🧠 INTELLIGENT FEATURES

Smart Type Inference Engine: Statistical analysis of sample data
Flexible Null Handling: Configurable null value patterns
Nested Data Support: Flatten JSON objects with dot notation
Performance Optimized: Configurable sampling for large files
Error Resilient: Graceful handling of malformed data
Schema Validation: Pre-flight analysis before table creation

📊 ENHANCED CAPABILITIES

44 Tools Total (was 40) - 4 new enhanced virtual table tools
Intelligent Type Detection for CSV numeric, date, boolean patterns
JSON Structure Analysis with nested object flattening
File Format Auto-Detection for JSON collections
Comprehensive Schema Reports with statistics and examples

🎯 PERFECT FOR

Data Import Workflows: Smart CSV/JSON ingestion with type inference
Schema Discovery: Analyze file structure before import
ETL Processes: Automated data type conversion and validation
Data Analysis: Quick exploration of file-based datasets
Business Intelligence: Seamless data source integration

🚀 QUICK START

// Analyze CSV structure
analyze_csv_schema({"csv_file_path": "./data.csv"})

// Create table with smart type inference
create_enhanced_csv_table({
  "table_name": "imported_data",
  "csv_file_path": "./data.csv"
})

// Import JSON collections with flattening
create_json_collection_table({
  "table_name": "events", 
  "json_file_path": "./events.jsonl",
  "flatten_nested": true
})

Transform any CSV or JSON file into a queryable SQLite table with intelligent type inference!

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Releases: neverinfamous/sqlite-mcp-server

v2.6.3 - Production/Stable

Uh oh!

v2.6.2 - Metadata Update

📦 Metadata Updates

🔧 Technical Changes

Uh oh!

SQLite MCP Server v2.6.1: Complete JSON Operations Suite

🆕 What's New in v2.6.1

🎯 JSON Helper Tools (6 New Tools)

🤖 JSON Auto-Normalization Enhanced

🛡️ Parameter Binding Interface

📦 Automatic Parameter Serialization

🧠 Enhanced JSON Error Diagnostics

📊 By the Numbers

🚀 Quick Start

🎯 JSON Helper Tools Examples

🛡️ Enhanced Security

🔧 Migration Guide

📚 Documentation

🙏 Contributors

Uh oh!

🛡️ SQLite MCP Server v2.5.0 - Enhanced Security Release

Uh oh!