[Update Request] sqlite-mcp-server - Update from 25 to 73 tools (v2.6.4)

[neverinfamous]

Update Request: sqlite-mcp-server

Current State

The sqlite-mcp-server currently listed in the Docker MCP Catalog is significantly outdated:

• Current listing: 25 tools
• Actual current version (v2.6.4): 73 tools

Server Information

Field	Value
Server Name	sqlite-mcp-server
Docker Image	mcp/sqlite-mcp-server
Author	@neverinfamous
Repository	https://github.com/neverinfamous/sqlite-mcp-server
Current Version	v2.6.4 (December 6, 2025)
License	MIT

What's New Since Last Update

The server has been significantly enhanced with 48 additional tools (from 25 → 73):

New Tool Categories

• JSON Helper Tools (6) - Simplified JSON operations with auto-normalization
• Text Processing (9) - Regex, fuzzy matching, phonetic search, similarity
• Statistical Analysis (8) - Descriptive stats, percentiles, time series
• Virtual Tables (8) - CSV, R-Tree, series generation
• Semantic/Vector Search (8) - AI-native embeddings, cosine similarity, hybrid search
• Geospatial (7) - SpatiaLite spatial indexing and operations
• PRAGMA Operations (5) - Configuration, optimization, introspection
• Vector Optimization (2) - ANN search, clustering

New Features (v2.6.x)

• 🎛️ Tool Filtering - SQLITE_MCP_TOOL_FILTER environment variable for MCP client compatibility
• 🤖 JSON Auto-Normalization - Automatically fixes Python-style JSON
• 🛡️ SQL Injection Protection - Enhanced parameter binding
• 📦 Automatic Parameter Serialization - Direct object/array parameters
• 🧠 Enhanced JSON Error Diagnostics - Intelligent error categorization
• 7 MCP Resources - Database meta-awareness
• 7 MCP Prompts - Guided workflows

Requested Changes

Please update the sqlite-mcp-server entry in the catalog to reflect:

1. Tool count: Update from 25 to 73 tools
2. Description: Update to reflect new capabilities
3. tools.json: Update with complete list of 73 tools
4. Version: Sync with latest release (v2.6.4)

Updated tools.json

I can provide the complete updated tools.json with all 73 tools if needed. The full tool list is documented in the wiki.

Verification

• ✅ All 73 tools tested and verified (python test_runner.py --standard)
• ✅ Docker images available for amd64 and arm64
• ✅ MIT License
• ✅ Active development with regular updates
• ✅ Supply chain security (provenance, SBOM, attestations)

Links

• 📚 Documentation Wiki
• 🐳 Docker Hub (writenotenow)
• 📦 PyPI Package
• 🔍 AI Documentation Search
• 📰 Release Article

Thank you for maintaining the MCP Registry!

[neverinfamous]

Complete tools.json for sqlite-mcp-server v2.6.4

Here is the complete tools.json with all 78 tools (organized into 10 categories):

[
  {
    "name": "read_query",
    "description": "Execute a SELECT query on the SQLite database",
    "arguments": [
      {"name": "query", "type": "string", "desc": "SELECT SQL query to execute"},
      {"name": "params", "type": "array", "desc": "Optional query parameters"}
    ]
  },
  {
    "name": "write_query",
    "description": "Execute an INSERT, UPDATE, or DELETE query on the SQLite database",
    "arguments": [
      {"name": "query", "type": "string", "desc": "SQL query to execute"},
      {"name": "params", "type": "array", "desc": "Optional query parameters"}
    ]
  },
  {
    "name": "create_table",
    "description": "Create a new table in the SQLite database",
    "arguments": [
      {"name": "query", "type": "string", "desc": "CREATE TABLE SQL statement"},
      {"name": "params", "type": "array", "desc": "Optional query parameters"}
    ]
  },
  {
    "name": "list_tables",
    "description": "List all tables in the SQLite database",
    "arguments": []
  },
  {
    "name": "describe_table",
    "description": "Get the schema information for a specific table",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Name of the table to describe"}
    ]
  },
  {
    "name": "fts_search",
    "description": "Perform enhanced full-text search with BM25 ranking and snippets",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Name of the FTS5 table to search"},
      {"name": "query", "type": "string", "desc": "FTS5 search query"}
    ]
  },
  {
    "name": "create_fts_table",
    "description": "Create a FTS5 virtual table for full-text search",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Name for the FTS5 table"},
      {"name": "columns", "type": "array", "desc": "List of columns to include in FTS5 index"}
    ]
  },
  {
    "name": "rebuild_fts_index",
    "description": "Rebuild FTS5 index for optimal performance",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Name of the FTS5 table"}
    ]
  },
  {
    "name": "hybrid_search",
    "description": "Combine FTS5 keyword search with semantic similarity",
    "arguments": [
      {"name": "embeddings_table", "type": "string", "desc": "Name of the embeddings table"},
      {"name": "fts_table", "type": "string", "desc": "Name of the FTS5 table"},
      {"name": "query_text", "type": "string", "desc": "Text query for FTS5 keyword search"},
      {"name": "query_embedding", "type": "array", "desc": "Query embedding for semantic search"},
      {"name": "keyword_weight", "type": "number", "desc": "Weight for keyword results (0-1)"},
      {"name": "semantic_weight", "type": "number", "desc": "Weight for semantic results (0-1)"},
      {"name": "limit", "type": "integer", "desc": "Maximum results to return"}
    ]
  },
  {
    "name": "semantic_search",
    "description": "Perform semantic similarity search using cosine similarity",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Name of the embeddings table to search"},
      {"name": "query_embedding", "type": "array", "desc": "Query embedding vector for similarity search"},
      {"name": "limit", "type": "integer", "desc": "Maximum results to return"},
      {"name": "similarity_threshold", "type": "number", "desc": "Minimum similarity score"}
    ]
  },
  {
    "name": "semantic_query",
    "description": "Natural language query to semantic search",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Embeddings table name"},
      {"name": "query_text", "type": "string", "desc": "Natural language query"},
      {"name": "limit", "type": "integer", "desc": "Maximum results"}
    ]
  },
  {
    "name": "setup_semantic_search",
    "description": "Set up semantic search infrastructure",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Base table name"},
      {"name": "text_columns", "type": "array", "desc": "Columns to index"}
    ]
  },
  {
    "name": "create_embeddings_table",
    "description": "Create a table optimized for storing embeddings with metadata",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Name for the embeddings table"},
      {"name": "embedding_dim", "type": "integer", "desc": "Dimension of the embedding vectors"}
    ]
  },
  {
    "name": "store_embedding",
    "description": "Store an embedding vector with content and metadata",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Target embeddings table"},
      {"name": "content", "type": "string", "desc": "Original content text"},
      {"name": "embedding", "type": "array", "desc": "Embedding vector"},
      {"name": "metadata", "type": "object", "desc": "Additional metadata"}
    ]
  },
  {
    "name": "create_vector_index",
    "description": "Create optimized vector index for ANN search (cluster or grid)",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Source embeddings table"},
      {"name": "embedding_column", "type": "string", "desc": "Column containing embeddings"},
      {"name": "index_type", "type": "string", "desc": "Index type: 'cluster' or 'grid'"},
      {"name": "num_clusters", "type": "integer", "desc": "Number of clusters (for cluster type)"},
      {"name": "grid_size", "type": "integer", "desc": "Grid dimension (for grid type)"}
    ]
  },
  {
    "name": "analyze_vector_index",
    "description": "Analyze vector index statistics and distribution",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Source embeddings table"}
    ]
  },
  {
    "name": "rebuild_vector_index",
    "description": "Rebuild vector index with updated data",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Source embeddings table"},
      {"name": "force", "type": "boolean", "desc": "Force rebuild even if current"}
    ]
  },
  {
    "name": "optimize_vector_search",
    "description": "Perform ANN search using vector index (O(log n) performance)",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Source embeddings table"},
      {"name": "query_embedding", "type": "array", "desc": "Query vector"},
      {"name": "limit", "type": "integer", "desc": "Maximum results"},
      {"name": "search_k", "type": "integer", "desc": "Number of clusters/cells to search"},
      {"name": "similarity_threshold", "type": "number", "desc": "Minimum similarity"}
    ]
  },
  {
    "name": "batch_similarity_search",
    "description": "Perform batch similarity search for multiple query vectors",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Source embeddings table"},
      {"name": "query_embeddings", "type": "array", "desc": "Array of query vectors"},
      {"name": "limit", "type": "integer", "desc": "Maximum results per query"}
    ]
  },
  {
    "name": "calculate_similarity",
    "description": "Calculate cosine similarity between two vectors",
    "arguments": [
      {"name": "vector1", "type": "array", "desc": "First vector"},
      {"name": "vector2", "type": "array", "desc": "Second vector"}
    ]
  },
  {
    "name": "json_query",
    "description": "Query JSON data using JSONPath expressions",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Source table"},
      {"name": "json_column", "type": "string", "desc": "JSON column name"},
      {"name": "json_path", "type": "string", "desc": "JSONPath expression"}
    ]
  },
  {
    "name": "json_select",
    "description": "Select and extract JSON data with optional path",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Source table"},
      {"name": "json_column", "type": "string", "desc": "JSON column name"},
      {"name": "json_path", "type": "string", "desc": "Optional JSON path"}
    ]
  },
  {
    "name": "json_insert",
    "description": "Insert JSON data with automatic normalization",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Target table"},
      {"name": "json_column", "type": "string", "desc": "JSON column name"},
      {"name": "json_data", "type": "any", "desc": "JSON data (object/array/string)"}
    ]
  },
  {
    "name": "json_update",
    "description": "Update JSON values at specific paths",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Target table"},
      {"name": "json_column", "type": "string", "desc": "JSON column name"},
      {"name": "json_path", "type": "string", "desc": "Path to update"},
      {"name": "new_value", "type": "any", "desc": "New value"}
    ]
  },
  {
    "name": "json_merge",
    "description": "Merge JSON objects with conflict resolution",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Target table"},
      {"name": "json_column", "type": "string", "desc": "JSON column name"},
      {"name": "merge_data", "type": "object", "desc": "Data to merge"}
    ]
  },
  {
    "name": "json_validate_path",
    "description": "Validate JSONPath expression",
    "arguments": [
      {"name": "json_path", "type": "string", "desc": "JSONPath to validate"},
      {"name": "json_data", "type": "string", "desc": "Optional JSON to test against"}
    ]
  },
  {
    "name": "validate_json",
    "description": "Validate JSON structure and syntax",
    "arguments": [
      {"name": "json_data", "type": "string", "desc": "JSON string to validate"}
    ]
  },
  {
    "name": "analyze_json_schema",
    "description": "Analyze and infer JSON schema from data",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Source table"},
      {"name": "json_column", "type": "string", "desc": "JSON column name"}
    ]
  },
  {
    "name": "create_json_collection_table",
    "description": "Create virtual table for JSON file collections (JSONL, JSON arrays)",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Name for the JSON collection virtual table"},
      {"name": "json_file_path", "type": "string", "desc": "Path to JSON file"}
    ]
  },
  {
    "name": "create_csv_table",
    "description": "Create a virtual table to access CSV files",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Name for the CSV virtual table"},
      {"name": "csv_file_path", "type": "string", "desc": "Path to the CSV file"}
    ]
  },
  {
    "name": "create_enhanced_csv_table",
    "description": "Create CSV table with automatic type inference",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Name for the table"},
      {"name": "csv_file_path", "type": "string", "desc": "Path to CSV file"}
    ]
  },
  {
    "name": "create_rtree_table",
    "description": "Create R-Tree virtual table for spatial indexing",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Name for the R-Tree table"},
      {"name": "dimensions", "type": "array", "desc": "Dimension names (min/max pairs)"}
    ]
  },
  {
    "name": "create_series_table",
    "description": "Create generate_series virtual table for sequences",
    "arguments": [
      {"name": "start", "type": "integer", "desc": "Start value"},
      {"name": "stop", "type": "integer", "desc": "Stop value"},
      {"name": "step", "type": "integer", "desc": "Step increment"}
    ]
  },
  {
    "name": "list_virtual_tables",
    "description": "List all virtual tables in the database",
    "arguments": []
  },
  {
    "name": "drop_virtual_table",
    "description": "Drop a virtual table",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Virtual table to drop"}
    ]
  },
  {
    "name": "virtual_table_info",
    "description": "Get detailed information about a virtual table",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Virtual table name"}
    ]
  },
  {
    "name": "analyze_csv_schema",
    "description": "Analyze CSV file and infer data types without creating table",
    "arguments": [
      {"name": "csv_file_path", "type": "string", "desc": "Path to the CSV file to analyze"}
    ]
  },
  {
    "name": "load_spatialite",
    "description": "Load SpatiaLite extension for geospatial operations",
    "arguments": []
  },
  {
    "name": "create_spatial_table",
    "description": "Create a table with geometry column and spatial index",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Table name"},
      {"name": "geometry_type", "type": "string", "desc": "Geometry type (POINT, POLYGON, etc.)"},
      {"name": "srid", "type": "integer", "desc": "Spatial reference ID"}
    ]
  },
  {
    "name": "spatial_query",
    "description": "Execute spatial queries (within, intersects, distance)",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Source table"},
      {"name": "geometry_column", "type": "string", "desc": "Geometry column name"},
      {"name": "operation", "type": "string", "desc": "Spatial operation"},
      {"name": "reference_geometry", "type": "string", "desc": "WKT geometry"}
    ]
  },
  {
    "name": "spatial_analysis",
    "description": "Perform spatial analysis operations",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Source table"},
      {"name": "analysis_type", "type": "string", "desc": "Analysis type"}
    ]
  },
  {
    "name": "spatial_index",
    "description": "Create or manage spatial indexes",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Table name"},
      {"name": "geometry_column", "type": "string", "desc": "Geometry column"},
      {"name": "operation", "type": "string", "desc": "Operation (create, drop, check)"}
    ]
  },
  {
    "name": "geometry_operations",
    "description": "Perform geometry operations (buffer, centroid, union, etc.)",
    "arguments": [
      {"name": "operation", "type": "string", "desc": "Operation name"},
      {"name": "geometry", "type": "string", "desc": "WKT geometry input"}
    ]
  },
  {
    "name": "import_shapefile",
    "description": "Import shapefile into SpatiaLite table",
    "arguments": [
      {"name": "shapefile_path", "type": "string", "desc": "Path to shapefile"},
      {"name": "table_name", "type": "string", "desc": "Target table name"}
    ]
  },
  {
    "name": "fuzzy_match",
    "description": "Find fuzzy matches using Levenshtein distance and sequence matching",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Name of the table"},
      {"name": "column_name", "type": "string", "desc": "Name of the column to search"},
      {"name": "search_term", "type": "string", "desc": "Term to find fuzzy matches for"},
      {"name": "threshold", "type": "number", "desc": "Similarity threshold (0-1)"}
    ]
  },
  {
    "name": "phonetic_match",
    "description": "Find phonetic matches using Soundex and Metaphone",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Table name"},
      {"name": "column_name", "type": "string", "desc": "Column to search"},
      {"name": "search_term", "type": "string", "desc": "Search term"}
    ]
  },
  {
    "name": "regex_extract",
    "description": "Extract patterns using PCRE regular expressions",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Source table"},
      {"name": "column_name", "type": "string", "desc": "Column to search"},
      {"name": "pattern", "type": "string", "desc": "PCRE regex pattern"}
    ]
  },
  {
    "name": "regex_replace",
    "description": "Replace patterns using PCRE regular expressions",
    "arguments": [
      {"name": "text", "type": "string", "desc": "Input text"},
      {"name": "pattern", "type": "string", "desc": "PCRE regex pattern"},
      {"name": "replacement", "type": "string", "desc": "Replacement string"}
    ]
  },
  {
    "name": "text_normalize",
    "description": "Normalize text (lowercase, remove punctuation, etc.)",
    "arguments": [
      {"name": "text", "type": "string", "desc": "Text to normalize"},
      {"name": "operations", "type": "array", "desc": "Normalization operations"}
    ]
  },
  {
    "name": "text_similarity",
    "description": "Calculate text similarity between columns or against reference text",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Name of the table"},
      {"name": "column_name", "type": "string", "desc": "Name of the column to analyze"},
      {"name": "reference_text", "type": "string", "desc": "Optional reference text"},
      {"name": "method", "type": "string", "desc": "Similarity method (cosine, jaccard)"}
    ]
  },
  {
    "name": "text_validation",
    "description": "Validate text against patterns and constraints",
    "arguments": [
      {"name": "text", "type": "string", "desc": "Text to validate"},
      {"name": "validation_type", "type": "string", "desc": "Validation type (email, url, etc.)"}
    ]
  },
  {
    "name": "descriptive_statistics",
    "description": "Calculate comprehensive descriptive statistics for a numeric column",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Name of the table"},
      {"name": "column_name", "type": "string", "desc": "Name of the numeric column to analyze"}
    ]
  },
  {
    "name": "correlation_analysis",
    "description": "Calculate correlation coefficient between two numeric columns",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Name of the table"},
      {"name": "column_x", "type": "string", "desc": "First numeric column"},
      {"name": "column_y", "type": "string", "desc": "Second numeric column"}
    ]
  },
  {
    "name": "distribution_analysis",
    "description": "Analyze data distribution with histogram and normality tests",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Table name"},
      {"name": "column_name", "type": "string", "desc": "Numeric column"},
      {"name": "bins", "type": "integer", "desc": "Number of histogram bins"}
    ]
  },
  {
    "name": "hypothesis_testing",
    "description": "Perform statistical hypothesis tests (t-test, chi-square)",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Table name"},
      {"name": "column_name", "type": "string", "desc": "Column to test"},
      {"name": "test_type", "type": "string", "desc": "Test type"},
      {"name": "hypothesis_value", "type": "number", "desc": "Hypothesized value"}
    ]
  },
  {
    "name": "outlier_detection",
    "description": "Detect outliers using IQR method and Z-score analysis",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Name of the table"},
      {"name": "column_name", "type": "string", "desc": "Name of the numeric column to analyze"},
      {"name": "method", "type": "string", "desc": "Detection method (iqr, zscore)"}
    ]
  },
  {
    "name": "percentile_analysis",
    "description": "Calculate percentiles for numeric columns",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Table name"},
      {"name": "column_name", "type": "string", "desc": "Numeric column"},
      {"name": "percentiles", "type": "array", "desc": "Percentiles to calculate"}
    ]
  },
  {
    "name": "moving_averages",
    "description": "Calculate moving averages for time series data",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Table name"},
      {"name": "value_column", "type": "string", "desc": "Value column"},
      {"name": "time_column", "type": "string", "desc": "Time column"},
      {"name": "window_size", "type": "integer", "desc": "Window size"}
    ]
  },
  {
    "name": "regression_analysis",
    "description": "Perform linear regression analysis between two variables",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Name of the table"},
      {"name": "x_column", "type": "string", "desc": "Independent variable column"},
      {"name": "y_column", "type": "string", "desc": "Dependent variable column"}
    ]
  },
  {
    "name": "vacuum_database",
    "description": "Optimize database by reclaiming unused space and defragmenting",
    "arguments": []
  },
  {
    "name": "optimize_database",
    "description": "Run comprehensive database optimization",
    "arguments": []
  },
  {
    "name": "analyze_database",
    "description": "Update database statistics for query optimization",
    "arguments": []
  },
  {
    "name": "integrity_check",
    "description": "Check database integrity and report any corruption",
    "arguments": []
  },
  {
    "name": "backup_database",
    "description": "Create a backup of the database to a file",
    "arguments": [
      {"name": "backup_path", "type": "string", "desc": "Path where backup file will be created"}
    ]
  },
  {
    "name": "restore_database",
    "description": "Restore database from a backup file",
    "arguments": [
      {"name": "backup_path", "type": "string", "desc": "Path to backup file to restore from"},
      {"name": "confirm", "type": "boolean", "desc": "Confirmation flag (required to prevent accidental restores)"}
    ]
  },
  {
    "name": "verify_backup",
    "description": "Verify backup file integrity",
    "arguments": [
      {"name": "backup_path", "type": "string", "desc": "Path to backup file"}
    ]
  },
  {
    "name": "database_stats",
    "description": "Get database performance and usage statistics",
    "arguments": []
  },
  {
    "name": "index_usage_stats",
    "description": "Analyze index usage and efficiency",
    "arguments": []
  },
  {
    "name": "pragma_compile_options",
    "description": "Get SQLite compile-time options",
    "arguments": []
  },
  {
    "name": "pragma_database_list",
    "description": "List attached databases",
    "arguments": []
  },
  {
    "name": "pragma_optimize",
    "description": "Run PRAGMA optimize for query performance",
    "arguments": []
  },
  {
    "name": "pragma_settings",
    "description": "Get/set PRAGMA configuration settings",
    "arguments": [
      {"name": "pragma_name", "type": "string", "desc": "PRAGMA name"},
      {"name": "value", "type": "any", "desc": "Optional value to set"}
    ]
  },
  {
    "name": "pragma_table_info",
    "description": "Get detailed table information via PRAGMA",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Table name"}
    ]
  },
  {
    "name": "append_insight",
    "description": "Add a business insight to the memo resource",
    "arguments": [
      {"name": "insight", "type": "string", "desc": "Business insight discovered from data analysis"}
    ]
  },
  {
    "name": "summarize_table",
    "description": "Generate a comprehensive summary of a table",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Table to summarize"}
    ]
  },
  {
    "name": "hybrid_search_workflow",
    "description": "Guided workflow for setting up hybrid search",
    "arguments": [
      {"name": "base_table", "type": "string", "desc": "Source table"},
      {"name": "text_column", "type": "string", "desc": "Text column for FTS"},
      {"name": "embedding_column", "type": "string", "desc": "Column for embeddings"}
    ]
  },
  {
    "name": "advanced_search",
    "description": "Multi-method advanced search combining multiple techniques",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Table to search"},
      {"name": "column_name", "type": "string", "desc": "Column to search"},
      {"name": "search_term", "type": "string", "desc": "Search term"},
      {"name": "methods", "type": "array", "desc": "Search methods to use"}
    ]
  },
  {
    "name": "test_jsonb_conversion",
    "description": "Test JSONB conversion for a table",
    "arguments": [
      {"name": "table_name", "type": "string", "desc": "Table to test"},
      {"name": "json_column", "type": "string", "desc": "JSON column"}
    ]
  }
]

Tool Summary by Category

Category	Count	Tools
Core	5	read_query, write_query, create_table, list_tables, describe_table
FTS	4	fts_search, create_fts_table, rebuild_fts_index, hybrid_search
Vector	11	semantic_search, semantic_query, setup_semantic_search, create_embeddings_table, store_embedding, create_vector_index, analyze_vector_index, rebuild_vector_index, optimize_vector_search, batch_similarity_search, calculate_similarity
JSON	9	json_query, json_select, json_insert, json_update, json_merge, json_validate_path, validate_json, analyze_json_schema, create_json_collection_table
Virtual Tables	8	create_csv_table, create_enhanced_csv_table, create_rtree_table, create_series_table, list_virtual_tables, drop_virtual_table, virtual_table_info, analyze_csv_schema
Spatial	7	load_spatialite, create_spatial_table, spatial_query, spatial_analysis, spatial_index, geometry_operations, import_shapefile
Text Processing	7	fuzzy_match, phonetic_match, regex_extract, regex_replace, text_normalize, text_similarity, text_validation
Statistics	8	descriptive_statistics, correlation_analysis, distribution_analysis, hypothesis_testing, outlier_detection, percentile_analysis, moving_averages, regression_analysis
Admin	14	vacuum_database, optimize_database, analyze_database, integrity_check, backup_database, restore_database, verify_backup, database_stats, index_usage_stats, pragma_compile_options, pragma_database_list, pragma_optimize, pragma_settings, pragma_table_info
Misc	5	append_insight, summarize_table, hybrid_search_workflow, advanced_search, test_jsonb_conversion

Total: 78 tools

Additional Features Not In Original Listing

• 7 MCP Resources - Real-time database meta-awareness
• 7 MCP Prompts - Guided workflows
• Tool Filtering - SQLITE_MCP_TOOL_FILTER environment variable
• JSON Auto-Normalization - Automatic Python-style JSON fixing
• Enhanced Error Handling - Intelligent JSON error diagnostics
• JSONB Binary Storage - Efficient binary JSON storage

Let me know if you need any additional information!