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This server enables you to define nodes, relationships, and properties to design graph database schemas that can be visualized interactively.\n\nThis MCP server facilitates data modeling workflows like the one detailed below.\n\n* Blue steps are handled by the agent\n* Purple by the Data Modeling MCP server\n* Green by the user\n\n![data-modeling-workflow](./assets/images/data-modeling-process-v2.png)\n\n## Demo\n\nFor an end to end demo using the Data Modeling and Cypher MCP servers to develop a data model, generate an ingest script, and validate use cases please check out [this Github Repo](https://github.com/neo4j-field/data-modeling-assistant-demo).\n\n## 🧩 Components\n\n### 📦 Resources\n\nThe server provides these resources:\n\n#### Schema \n\n- `resource://schema/node`\n   - Get the JSON schema for a Node object\n   - Returns: JSON schema defining the structure of a Node\n\n- `resource://schema/relationship`\n   - Get the JSON schema for a Relationship object\n   - Returns: JSON schema defining the structure of a Relationship\n\n- `resource://schema/property`\n   - Get the JSON schema for a Property object\n   - Returns: JSON schema defining the structure of a Property\n\n- `resource://schema/data_model`\n   - Get the JSON schema for a DataModel object\n   - Returns: JSON schema defining the structure of a DataModel\n\n#### Example Data Models\n\n- `resource://examples/patient_journey_model`\n   - Get a real-world Patient Journey healthcare data model in JSON format\n   - Returns: JSON DataModel for tracking patient encounters, conditions, medications, and care plans\n\n- `resource://examples/supply_chain_model`\n   - Get a real-world Supply Chain data model in JSON format\n   - Returns: JSON DataModel for tracking products, orders, inventory, and locatio…","publisher":null,"homepage":null,"repository":null,"version":"0.7.0","license":null,"protocols":["mcp"],"tags":["mcp"],"pricing":null,"endpoints":[{"url":"pypi:iflow-mcp_mcp-neo4j-data-modeling","type":"package_pypi","auth":null,"probeable":false}],"skills":null,"tools":null,"extra":null,"attribution":{"kind":"pypi","name":"pypi","summary":"pypi","version":"pypi","description":"pypi"}},"derived":{"capabilities":[{"slug":"commerce.ecommerce","name":"E-commerce Operations","confidence":1,"provenance":"derived"},{"slug":"knowledge.knowledge-graph","name":"Knowledge Graphs","confidence":1,"provenance":"derived"}],"categories":["commerce","knowledge"],"language":"en"},"observed":{"status":"unknown","statusReason":"Distributed as a package to run locally; no network endpoint to check.","lastOkAt":null,"lastProbedAt":null,"statusComputedAt":null,"reliability30d":null,"latestObservations":[],"tools":null,"package":{"name":"iflow-mcp_mcp-neo4j-data-modeling","registry":"pypi","observedAt":"2026-10-28T05:24:07.810Z","publishedAt":"2025-12-01T10:21:10.330225Z","latestVersion":"0.7.0"},"toolSurface":null,"endpointFacts":[]},"verification":{"claimed":false,"claimedAt":null,"proofs":[]},"provenance":{"sources":[{"source":"pypi","key":"iflow-mcp_mcp-neo4j-data-modeling","url":"https://pypi.org/project/iflow-mcp_mcp-neo4j-data-modeling/","firstSeenAt":"2026-09-09T20:23:30.329Z","fetchedAt":"2026-10-28T05:22:54.689Z","normalizedAt":"2026-10-28T05:22:54.689Z"}]},"firstSeenAt":"2026-09-09T20:23:30.329Z","updatedAt":"2026-10-28T05:24:07.810Z"}