Smarter MCP Servers: Using a Graph to Solve the Context Window Problem
summary
Compares three MCP server patterns—full-list, lazy-loading, and a graph-backed registry—and shows concrete savings: modeling an Aura API with 26 endpoints burns ~5,000 tokens at startup but returning the top eight common tools plus discovery and execution drops startup cost to ~2,000 tokens (~62% saved). Uses Neo4j (Neo4j Aura) to store tools, categories, co‑occurrence and usage weights, updates usage on each call, and surfaces top-ranked tools first so the approach adapts and reduces token burn.
AI-written summary · about
chapters (8)
- 0:00Introduction to MCP servers
- 3:55Context window problem explained
- 7:50Full list tool approach
- 11:04Lazy loading tools pattern
- 14:56Graph-backed tool registry
- 21:40Choosing the right approach
- 25:55Key design considerations
- 27:22Q&A on tool ordering
related talks
49mSolving Context Bloat: Semantic Tool Routing in Multi-Server MCP Environments
20mFrom Data Dumps To Smart Context: Building MCP Servers That AI Can Actually Use
27mYour MCP Server is Bad (and You Should Feel Bad)
51mThe MCP Megalith: Surviving the 1,000-Tool Explosion and Auth Fragmentation
02h 49mMCP Servers: Best Practices, Design Choices and Their Consequences
25mTools in your AI's Toolbox : An introduction to MCP Servers
53mAgents, Tools, and Mcp, Oh My! Next Level AI Concepts for Developers
30mThe Fastest Path To Building An Agent For Your Knowledge Graph Is By Using MCP