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Great walkthrough. I built a Knowledge Graph MCP server that wraps a Neo4j database with 130k+ nodes — five tools for entity search, contact lookup, session history, fact retrieval, and semantic search. A few things I learned in production that might save you time:
Error handling matters more than you think. When an MCP tool throws an unhandled exception, the agent loses context about what went wrong. Returning structured error dicts keeps the agent productive instead of confused.
Type your parameters narrowly. My first version accepted query: str for everything. The agent kept passing malformed queries. Once I added specific parameters (subject: str, predicate: str) the hit rate went from ~60% to 95%.
One tool per logical operation, not per API endpoint. I started with 12 tools mapping to every Neo4j query I had. Consolidated to 5 by grouping related operations. Agents perform better with fewer, well-documented tools than many overlapping ones.
The start-with-one-tool advice in this article is exactly right. The production version just needs better error boundaries and tighter parameter contracts.
Evan Lausier specializes in enterprise NetSuite ERP implementations and cloud solutions architecture. With 18+ years in software, I lead digital transformations for mid-market companies.
Thank you for posting! So glad you tried it out. Thanks for the lessons learned, especially the parameter typing and consolidating tools. I will have to carry that forward, Very smart points!
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Great walkthrough. I built a Knowledge Graph MCP server that wraps a Neo4j database with 130k+ nodes — five tools for entity search, contact lookup, session history, fact retrieval, and semantic search. A few things I learned in production that might save you time:
Error handling matters more than you think. When an MCP tool throws an unhandled exception, the agent loses context about what went wrong. Returning structured error dicts keeps the agent productive instead of confused.
Type your parameters narrowly. My first version accepted query: str for everything. The agent kept passing malformed queries. Once I added specific parameters (subject: str, predicate: str) the hit rate went from ~60% to 95%.
One tool per logical operation, not per API endpoint. I started with 12 tools mapping to every Neo4j query I had. Consolidated to 5 by grouping related operations. Agents perform better with fewer, well-documented tools than many overlapping ones.
The start-with-one-tool advice in this article is exactly right. The production version just needs better error boundaries and tighter parameter contracts.
Thank you for posting! So glad you tried it out. Thanks for the lessons learned, especially the parameter typing and consolidating tools. I will have to carry that forward, Very smart points!