Integrated Workflows

Building Tools with AI Assistance

Instead of manually exploring schemas, writing queries, and debugging code, you can use your MCP tools with AI to automate the development process. For example, to create a tool that finds actors who worked with a specific director, simply ask the AI to help. It will use your tools to explore the schema, generate and validate the Cypher query, and write the complete Python function.

Using Cursor Rules

Configure Cursor to use your MCP tools through rules in .cursor/rules/mcp-neo4j.md:

# Neo4j MCP Server Integration

## Available Tools
- **neo4j_schema**: Get database schema (labels, relationships, properties)
- **neo4j_query**: Execute and validate Cypher queries

## Best Practices
- Start with schema exploration
- Test queries before implementation
- Avoid OPTIONAL MATCH unless absolutely necessary. Use list comprehension in the RETURN clause instead.
- Add ORDER BY and LIMIT clauses
- Use parameters ($param)
- Include logging and error handling

## Example Usage
"Create a tool to find movies with a specific actor"
1. Check schema: Person-[:ACTED_IN]->Movie
2. Test query: MATCH (p:Person {name: $name})-[r:ACTED_IN]->(m:Movie) RETURN m.title AS title, r.role AS role
3. Generate Python function with proper structure

These rules help Cursor generate reliable code by following the deterministic workflow.

Iterative Development

The ultimate example of MCP’s power is transforming probabilistic AI responses into deterministic graph development. This process eliminates uncertainty through a structured workflow.

Rather than relying on probabilistic AI responses, or generating the same Cypher statements over and over, you can use LLMs to build tools, verify the results, and implement the tools within the project.

Each step builds on verified information from the previous step, creating a reliable chain of development. This transforms potentially unreliable AI suggestions into production-ready code through systematic validation.