Webinar
Lessons Learned Building a RAG App on an MCP-Based Architecture
Most "LLM → Cypher" pipelines look clean on paper but collapse fast when exposed to messy, real-world data that is, for instance, encoded, multilingual, or requires several hops across the graph.
In this technical session Orbis Holding walks through how MCP turns that brittle chain into a reliable loop. Instead of hoping the model produces the correct Cypher on the first attempt, MCP gives the LLM a structured way to discover tools, inspect schema, validate queries, check stored values, and issue clarification prompts when something doesn’t line up.
The session also covers the architectural trade offs between classic LLM → Cypher workflows and MCP, and how these design choices translate into higher accuracy, less rework, and safer rollouts.
What You’ll See
What You’ll Learn
In this technical session Orbis Holding walks through how MCP turns that brittle chain into a reliable loop. Instead of hoping the model produces the correct Cypher on the first attempt, MCP gives the LLM a structured way to discover tools, inspect schema, validate queries, check stored values, and issue clarification prompts when something doesn’t line up.
The session also covers the architectural trade offs between classic LLM → Cypher workflows and MCP, and how these design choices translate into higher accuracy, less rework, and safer rollouts.
What You’ll See
- Detailed comparison of traditional pipeline vs MCP-based architecture
- Handling real database issues: encoded values, multilingual fields, inconsistent labels
- How MCP builds and validates multi hop traversal steps over large graph datasets
- How the team benchmarks accuracy and prevents repeating failing query patterns
What You’ll Learn
- How iterative clarification cycles drive correct Cypher generation
- How MCP clarifies ambiguous queries and prevents silent failures
- Practical evaluation and governance patterns for production GraphRAG
