Parquet MCP Server

MCP server for interacting with parquet files in a repository. Provides comprehensive data management with audit logging, rollback capabilities, and semantic search.

Credits

This is a custom MCP server implementation for parquet file management with audit trail support.

Features

• Read/Query: Query parquet files with filters, column selection, and limits
• Add Records: Add new records to parquet files with audit trail
• Update Records: Update existing records matching filters with audit trail
• Upsert Records: Insert or update records (supports enhanced filters for duplicate detection)
• Delete Records: Delete records matching filters with audit trail
• Audit Log: Complete change history with old/new values for all modifications
• Rollback: Undo specific operations using audit IDs
• Schema Discovery: Get schema definitions for data types
• Statistics: Get basic statistics about parquet files
• Efficient Backups: Audit log entries (~1 KB) instead of full snapshots (99%+ storage reduction)
• Optional Full Snapshots: Configurable periodic snapshots for additional safety

Installation

cd mcp-servers/parquet
pip install -r requirements.txt

Configuration

Cursor Configuration

Add to your Cursor MCP settings (typically ~/.cursor/mcp.json or Cursor settings):

Development (Audit Log Only):

{
  "mcpServers": {
    "parquet": {
      "command": "python",
      "args": [
        "$REPO_ROOT/mcp-servers/parquet/parquet_mcp_server.py"
      ],
      "env": {}
    }
  }
}

Production (With Periodic Snapshots):

{
  "mcpServers": {
    "parquet": {
      "command": "python",
      "args": [
        "$REPO_ROOT/mcp-servers/parquet/parquet_mcp_server.py"
      ],
      "env": {
        "MCP_FULL_SNAPSHOTS": "true",
        "MCP_SNAPSHOT_FREQUENCY": "weekly"
      }
    }
  }
}

Claude Desktop Configuration

Add to claude_desktop_config.json (typically ~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "parquet": {
      "command": "python",
      "args": [
        "$REPO_ROOT/mcp-servers/parquet/parquet_mcp_server.py"
      ]
    }
  }
}

Available Tools

list_data_types

List all available data types (parquet files) in the data directory.

get_schema

Get the schema definition for a data type.

Parameters:

• data_type (required): The data type name (e.g., 'flows', 'transactions', 'tasks')

read_parquet

Read and query a parquet file with optional filters. Supports enhanced filtering operators.

Parameters:

• data_type (required): The data type name
• filters (optional): Key-value pairs to filter records. Supports enhanced operators:
  • Simple value: exact match
  • List: in list (["value1", "value2"])
  • {"$contains": "text"}: substring match (case-insensitive)
  • {"$starts_with": "text"}: prefix match (case-insensitive)
  • {"$ends_with": "text"}: suffix match (case-insensitive)
  • {"$regex": "pattern"}: regex pattern match
  • {"$fuzzy": {"text": "query", "threshold": 0.7}}: fuzzy string matching (0-1 similarity)
  • {"$gt": 100}, {"$gte": 100}, {"$lt": 100}, {"$lte": 100}: numeric comparisons
  • {"$ne": "value"}: not equal
• limit (optional): Maximum number of rows to return (default: 1000)
• columns (optional): List of column names to return (default: all columns)

Examples:

{
  "data_type": "flows",
  "filters": {
    "category": "property_maintenance",
    "year": 2025
  },
  "limit": 100
}

{
  "data_type": "tasks",
  "filters": {
    "title": {"$contains": "therapy"},
    "status": {"$ne": "completed"}
  }
}

{
  "data_type": "tasks",
  "filters": {
    "title": {"$fuzzy": {"text": "therapy session", "threshold": 0.7}}
  }
}

add_record

Add a new record to a parquet file. Creates audit log entry and optional snapshot.

Parameters:

• data_type (required): The data type name
• record (required): The record data as a JSON object matching the schema

Example:

{
  "data_type": "flows",
  "record": {
    "flow_name": "Monthly Rent",
    "flow_date": "2025-01-15",
    "amount_usd": 1500.00,
    "category": "housing",
    "flow_type": "recurring_expense"
  }
}

update_records

Update existing records in a parquet file. Creates audit log entry and optional snapshot.

Parameters:

• data_type (required): The data type name
• filters (required): Filters to identify records to update
• updates (required): Fields to update

Example:

{
  "data_type": "tasks",
  "filters": {
    "task_id": "abc123"
  },
  "updates": {
    "status": "completed",
    "completed_date": "2025-01-15"
  }
}

upsert_record

Insert or update a record (upsert). Checks for existing records using enhanced filters (supports all read_parquet filter operators including $contains, $fuzzy, etc.). If found, updates matching records. If not found, creates a new record. Returns whether it created or updated. Useful for preventing duplicates when adding contacts, tasks, or other records.

Parameters:

• data_type (required): The data type name
• filters (required): Enhanced filters to identify existing records (supports all read_parquet filter operators)
• record (required): The record data to insert or update

Returns:

• action: "created" or "updated"
• audit_id or audit_ids: Audit log entry ID(s)
• record_id: The ID of the created/updated record

Example (exact match):

{
  "data_type": "contacts",
  "filters": {
    "email": "galina@secod.com"
  },
  "record": {
    "name": "Galina Semakova",
    "email": "galina@secod.com",
    "category": "legal",
    "last_contact_date": "2025-12-24"
  }
}

Example (fuzzy match):

{
  "data_type": "contacts",
  "filters": {
    "name": {"$fuzzy": {"text": "Galina Semakova", "threshold": 0.8}}
  },
  "record": {
    "name": "Galina Semakova",
    "email": "galina@secod.com",
    "category": "legal",
    "last_contact_date": "2025-12-24"
  }
}

Example (contains match):

{
  "data_type": "tasks",
  "filters": {
    "title": {"$contains": "therapy payment"}
  },
  "record": {
    "title": "Pay for therapy session",
    "status": "pending",
    "due_date": "2025-12-25"
  }
}

delete_records

Delete records from a parquet file. Creates audit log entry and optional snapshot.

Parameters:

• data_type (required): The data type name
• filters (required): Filters to identify records to delete

Example:

{
  "data_type": "tasks",
  "filters": {
    "status": "canceled"
  }
}

get_statistics

Get basic statistics about a parquet file.

Parameters:

• data_type (required): The data type name

read_audit_log

Read audit log entries with optional filters. View complete history of all data modifications.

Parameters:

• data_type (optional): Filter by data type
• operation (optional): Filter by operation (add, update, delete)
• record_id (optional): Filter by specific record ID
• limit (optional): Maximum number of entries to return (default: 100)

Example:

{
  "data_type": "transactions",
  "operation": "update",
  "limit": 50
}

rollback_operation

Rollback a specific operation using its audit ID. Creates inverse operation to undo changes.

Parameters:

• audit_id (required): The audit ID of the operation to rollback

Rollback Logic:

• add operation → Delete the record
• update operation → Restore old values
• delete operation → Restore the record

Example:

{
  "audit_id": "abc123def456"
}

search_parquet

Semantic search using embeddings. Searches text fields for semantically similar records.

Parameters:

• data_type (required): The data type name
• query (required): Search query text
• text_fields (optional): List of text fields to search (default: auto-detect)
• limit (optional): Maximum number of results (default: 10)
• min_similarity (optional): Minimum cosine similarity threshold 0-1 (default: 0.7)
• additional_filters (optional): Additional filters to apply (same format as read_parquet)

Prerequisites:

• Must run generate_embeddings first to create embeddings for the data type
• Requires OPENAI_API_KEY environment variable

Example:

{
  "data_type": "tasks",
  "query": "pay for therapy session",
  "limit": 5,
  "min_similarity": 0.7
}

generate_embeddings

Generate and store embeddings for text fields in a data type. Creates embeddings parquet file for semantic search.

Parameters:

• data_type (required): The data type name
• text_fields (optional): List of text fields to generate embeddings for (default: auto-detect)
• force_regenerate (optional): Force regeneration of all embeddings (default: false)

Prerequisites:

• Requires OPENAI_API_KEY environment variable

Example:

{
  "data_type": "tasks",
  "text_fields": ["title", "description", "notes"]
}

Note: Embeddings are cached. Only missing embeddings are generated unless force_regenerate is true.

Backup & Recovery

Audit Log (Default)

All write operations create lightweight audit log entries in data/logs/audit_log.parquet:

• Storage: ~1 KB per operation (99%+ reduction vs full snapshots)
• Content: Operation type, record ID, affected fields, old/new values, timestamp
• Recovery: Rollback specific operations using rollback_operation tool

Optional Full Snapshots

Configure periodic full snapshots for additional safety:

Environment Variables:

• MCP_FULL_SNAPSHOTS: Set to "true" to enable periodic snapshots (default: false)
• MCP_SNAPSHOT_FREQUENCY: "daily", "weekly", "monthly", "never" (default: weekly)

Snapshot Location:

data/snapshots/[data_type]-[YYYY-MM-DD-HHMMSS].parquet

Storage Comparison

Approach	Storage per Operation	100 Operations
Full snapshots (old)	10 MB	1 GB
Audit log (new)	~1 KB	~100 KB
Savings	99.99%	99.99%

Recovery Options

1. Recent Changes: Use rollback_operation with audit ID
2. Multiple Changes: Rollback operations in reverse chronological order
3. Full Restore: Restore from periodic snapshot (if enabled)
4. Point-in-Time: Restore snapshot + replay audit log to specific timestamp

See AUDIT_LOG_GUIDE.md for detailed documentation.

Data Types

The server automatically discovers data types by scanning data/ for directories containing [type].parquet files. Common data types include:

• flows - Cash flow and expense data
• transactions - Transaction data
• tasks - Task management data
• contacts - Contact/merchant information
• income - Income data
• fixed_costs - Fixed cost data
• And many more...

Error Handling

The server returns structured error messages in JSON format when operations fail. Common errors include:

• File not found errors
• Schema validation errors
• Column not found errors
• Filter matching errors

Security Notes

• All write operations create audit log entries for traceability
• Audit logs are stored in data/logs/audit_log.parquet
• Optional full snapshots can be configured for additional safety
• Never commit sensitive data files to version control

Troubleshooting

1. File Not Found Errors

   • Verify the data type exists in data/[type]/[type].parquet
   • Check file permissions

2. Schema Validation Errors

   • Ensure records match the schema defined in data/schemas/[type]_schema.json
   • Check required fields are present

3. Filter Matching Errors

   • Verify filter syntax matches supported operators
   • Check column names exist in the schema

Testing

After installation/updates, run the test script:

python3 mcp-servers/parquet/test_audit_log.py

This validates:

• Audit log creation
• Schema compliance
• Operation tracking

See IMPLEMENTATION_SUMMARY.md for manual testing procedures.

Documentation

• README.md - This file, overview and quick reference
• AUDIT_LOG_GUIDE.md - Complete audit log documentation
• IMPLEMENTATION_SUMMARY.md - Implementation details and testing
• SETUP.md - Setup and configuration instructions

Notes

• The server uses audit log for efficient change tracking (99%+ storage reduction)
• All date fields are automatically converted to ISO format strings in responses
• Null/NaN values are converted to null in JSON responses
• The server runs in stdio mode for MCP communication
• Audit log entries are never automatically deleted (manual archival if needed)

License

MIT

Support

• GitHub Issues