MCP For Claude Marketplace
Data Extraction Schema Evolver

Data Extraction Schema Evolver MCP Connector for Claude

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Automatically detect schema drift and propose structural updates to JSON schemas.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner
More Details Connect to Claude

The Data Extraction Schema Evolver solves the problem of schema drift in unstructured data extraction. By analyzing discrepancies between existing schemas and new data samples, it identifies new fields, type mismatches, and missing fields. Use analyze_schema_drift to detect changes, propose_schema_evolution to generate updated schemas with deterministic type inference, and validate_evolution_safety to ensure changes maintain structural integrity. This bridge connects your data pipelines to intelligent, self-healing schema management.

json-schemaschema-driftdata-engineeringautomationtype-inference

3 tools expose this connector's capabilities to your AI agent.

analyze_schema_drift

Identifies discrepancies between a provided JSON schema and a collection of new data examples

validate_evolution_safety

Checks if the proposed schema changes are safe and do not destroy existing functionality

propose_schema_evolution

Generates a modified JSON schema based on drift analysis results

See how to talk to your AI agent using Data Extraction Schema Evolver.

I have a new set of JSON data. Can you check if it matches my current schema?

I have analyzed the new data using `analyze_schema_drift`. I detected 2 new fields and 1 type mismatch.

Propose a new schema based on these recent data examples.

The proposed schema has been generated. It includes the new fields as optional and has updated the 'status' field to a union type.

Is this proposed schema change safe to apply?

Yes, the change is safe. It only adds new optional fields and does not remove any existing mandatory fields.

Schema drift occurs when the structure of unstructured data changes over time, causing existing extraction schemas to fail or miss new information.

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