DataDabble MCP Server

A Model Context Protocol (MCP) server that wraps the DataDabble REST API, enabling Claude and other MCP clients to manage databases, fields, entries, visualizations, AI features, notifications, audit logs, and team accounts via natural language.

The server communicates with DataDabble's REST API rather than accessing MongoDB directly, respecting auth, validation, and business logic in the app layer.

Quick Start

Prerequisites

• Python 3.11+
• A running DataDabble instance (default: http://localhost:5000)

Install

pip install -e .

Configure

Copy .env.example to .env and set your values:

DATADABBLE_BASE_URL=http://localhost:5000/api/v1
DATADABBLE_EMAIL=user@example.com
DATADABBLE_PASSWORD=password
DATADABBLE_AUTO_LOGIN=true

Run

# As a module
python -m datadabble_mcp

# Or via the entry point
datadabble-mcp

Claude Desktop Configuration

Add to your Claude Desktop config (claude_desktop_config.json):

{
  "mcpServers": {
    "datadabble": {
      "command": "python",
      "args": ["-m", "datadabble_mcp"],
      "env": {
        "DATADABBLE_BASE_URL": "http://localhost:5000/api/v1",
        "DATADABBLE_EMAIL": "user@example.com",
        "DATADABBLE_PASSWORD": "password"
      }
    }
  }
}

Tools (42)

Auth (4)

Tool	Description
auth_login	Log in with email and password
auth_logout	Log out and clear tokens
auth_whoami	Get current user profile
auth_register	Register a new account

Databases (5)

Tool	Description
db_list	List all databases
db_create	Create a new database
db_get	Get a database by slug
db_update	Update title or description
db_delete	Delete a database

Fields (7)

Tool	Description
field_list	List fields in a database
field_create	Add a field (BOOL, INT, DEC, STR, DATE, EMAIL, URL, DICT, LIST)
field_get	Get a field by ID
field_update	Update a field (with optional data loss confirmation)
field_delete	Delete a field
field_reorder	Reorder fields
field_preview_type_change	Preview impact of changing a field's type

Entries (6)

Tool	Description
entry_list	List entries with pagination and filtering
entry_create	Create an entry
entry_get	Get an entry by ID
entry_update	Update an entry's values
entry_delete	Delete an entry
entry_validate_filter	Validate a filter expression

Visualizations (7)

Tool	Description
viz_list	List saved visualizations
viz_create	Create a visualization (bar, line, pie, scatter)
viz_get	Get a visualization by ID
viz_update	Update a visualization
viz_delete	Delete a visualization
viz_data	Get computed data for a visualization
viz_adhoc_data	Get visualization data without saving

AI (3)

Tool	Description
ai_insights	Get AI-generated insights about a database
ai_ask	Ask a natural language question about data
ai_suggest_query	Get a suggested filter from a description

Notifications (7)

Tool	Description
notification_list	List notifications
notification_unread_count	Get unread count
notification_mark_read	Mark one as read
notification_mark_all_read	Mark all as read
notification_delete	Delete a notification
notification_prefs_get	Get notification preferences
notification_prefs_update	Update notification preferences

Audit Logs (6)

Tool	Description
audit_list	List audit logs for a database
audit_stats	Get audit statistics for a database
audit_account_list	List account-level audit logs
audit_account_stats	Get account-level audit statistics
audit_account_users	List users in audit logs
audit_export	Export audit logs as CSV

Accounts & Teams (7)

Tool	Description
account_get	Get current account details
account_list	List all accounts
account_switch	Switch account context
account_members	List team members
account_invite	Invite a user by email
account_update_member	Update a member's role/permissions
account_remove_member	Remove a team member

Resources

URI	Description
datadabble://field-types	Field type reference
datadabble://filter-syntax	Filter expression language reference
datadabble://databases	List all databases (live)
datadabble://databases/{slug}	Database schema with fields and entry count
datadabble://databases/{slug}/sample	First 10 entries for context

Prompts

Name	Description
create-database	Create a database and add fields from a spec
analyze-database	Fetch schema, sample data, and AI insights
query-builder	Build a filter expression from natural language
bulk-create-entries	Create multiple entries from a JSON array

Development

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Lint
ruff check src/ tests/

Architecture

src/datadabble_mcp/
├── server.py          # FastMCP server, lifespan, entry point
├── config.py          # Environment variable configuration
├── auth.py            # JWT auth manager (login, refresh, logout)
├── client.py          # httpx client with auth injection + auto-refresh
├── resources.py       # MCP resources (field types, filter syntax, schemas)
├── prompts.py         # MCP prompts (workflow templates)
└── tools/
    ├── auth_tools.py
    ├── database_tools.py
    ├── field_tools.py
    ├── entry_tools.py
    ├── viz_tools.py
    ├── ai_tools.py
    ├── notification_tools.py
    ├── audit_tools.py
    └── account_tools.py

The server uses stdio transport and auto-logs in on startup when credentials are configured. All API errors are returned as structured dicts (never raised as exceptions), so MCP clients always get a useful response.