mcp-neo4j-entity-graph

# MCP Neo4j Entity Graph Server [](https://pypi.org/project/mcp-neo4j-entity-graph/) [](https://www.python.org/downloads/) [](https://opensource.org/licenses/MIT) MCP server for extracting entities and relationships from graph nodes using LLM structured output, creating entity graphs directly in Neo4j. **Supports 100+ LLM providers via LiteLLM** (OpenAI, Anthropic, Google, Azure, Bedrock, Ollama, etc.) ## Features - **Dual extraction pipeline**: Text-only (LLM) and visual (VLM) extraction auto-routed per chunk - **Grammar-enforced structured output**: Pydantic models used as `response_format` — the LLM cannot violate the schema - **Async background processing**: Long extractions run in background with job tracking - **Multi-provider LLM support**: Use any LLM via LiteLLM (OpenAI, Claude, Gemini, etc.) - **Schema-driven**: Define entity types and relationships to extract - **Provenance tracking**: `EXTRACTED_FROM` relationships link entities to source chunks - **High parallelism**: Configurable concurrency (text: up to 50, VLM: up to 50) - **Batched writes**: Optimized Neo4j writes (configurable batch size) - **Incremental**: Only processes nodes without prior extraction (unless `force=true`) - **Multi-pass ready**: Architecture supports entity-only, relationship-only, and corrective passes (v2) ## Tools ### `convert_schema` Converts data model output from the Data Modeling MCP to a Pydantic extraction schema. **Parameters:** | Parameter | Required | Description | |-----------|----------|-------------| | `modeling_output` | Yes | JSON output from the Data Modeling MCP server | | `output_path` | Yes | Path to save the Pydantic `.py` file (e.g. `/path/to/schema.py`) | **Output:** - `{output_path}` — Strongly-typed Pydantic models used as `response_format` fo…