Integrations & MCP
Data Sources
Upload files or connect REST APIs. Schema detection and field profiling included.
MCP Server
Monitor pipelines, check mappings, and review run results from your AI assistant.
REST API
Build datathere into your existing workflows. Trigger runs, query results, and manage sources from your own code.
MCP Client
Every MCP server in the ecosystem becomes a datathere connector. Coming soon.
datathere works with:
Connect your data, however it lives
- REST API connections with API key, OAuth, or bearer token authentication
- Automatic pagination handling for large datasets
- Scheduled refresh with configurable intervals
- Schema auto-detection from response payloads
- Field profiling with type inference and sample values
Your data is never used for AI model training
All AI processing uses zero-retention API agreements. Source data, mapping definitions, transformation outputs, and pipeline results are never included in any training dataset. AI provider data disclosure is tracked per GDPR request.
Monitor your pipelines from your AI assistant
You shouldn't have to open a dashboard to check if last night's sync ran cleanly. datathere's MCP server gives your AI assistant direct, read-only access to your workspace. Ask about mapping status, run results, quality violations, or field schemas — get answers in seconds.
- Works with Claude Desktop, Cursor, Windsurf, VS Code, and any MCP-compatible client
- Read-only access — your assistant can see everything but can't modify mappings or trigger runs
- Scoped API key authentication with organization-level data isolation
- Three-step setup: create an API key, select scopes, paste the config
How did last night's order sync go?
Which mappings still need certification?
Connected in under a minute
No SDK, no webhook configuration, no deployment. Paste a JSON snippet into your AI client and you're live.
- 1 Create an API key
In datathere, go to Settings and generate an API key with MCP scope.
- 2 Select your scopes
Choose which capabilities to expose: mappings, runs, quality, schemas, or all of them.
- 3 Paste the config
Add the JSON snippet to your AI client's MCP configuration. You're connected.
What your AI assistant can access
Mapping overview
All your mappings with status, field coverage percentage, and connected source details.
Run results
Full breakdown of any autopilot execution — records processed, delivered, and quarantined.
Quality violations
Which rules failed, on which fields, with sample data that triggered the violation.
Source schemas
Field names, types, and sample values for any connected data source.
Validation status
Certification state, completeness score, and any open action items for a mapping.
Action items
Open issues and recommendations across your entire workspace.
Integrate datathere into your existing systems
Everything you can do in the UI, you can do programmatically. The REST API gives your engineering team full access to mappings, sources, destinations, pipeline runs, and quality data — secured with scoped API keys.
- Scoped API keys with granular permissions per resource type
- Trigger pipeline runs, fetch results, and query quality violations
- Webhook endpoints for receiving data pushes from external systems
- OAuth2 token exchange for connecting to protected APIs
- Full request logging with audit trail for compliance
Every MCP server becomes a datathere connector
Any system with an MCP server — PostgreSQL, Snowflake, Salesforce, HubSpot, Google Sheets, and hundreds more — becomes a source or destination automatically. No custom integration work. No connector maintenance.
- 500+ MCP servers already available in the ecosystem
- Schema discovery from any connected MCP server
- Every new MCP server published by any vendor automatically becomes a potential connector
- datathere handles the mapping and quality — MCP handles the plumbing