finance_ops · finance · workflow
Ramp builds an MCP server enabling natural-language analysis of business spend data with Claude
Ramp's business data was accessible only through API calls, with no way to query or understand spend using natural language without writing code.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Natural language query trigger
A user submits a natural-language question about business spend data.
Tools used
Ramp MCPFastMCPClaude DesktopClaudeSQLiteRamp developer APIModel Context ProtocolOLAP
Outcome
After switching to a SQL-based paradigm with an in-memory SQLite database, Claude could accurately analyze tens of thousands of spend events, and the solution even worked with the free version of Claude due to reduced token usage.
What failed first
The initial MCP prototype ran into scaling issues—miscalculations, limited context windows, input size limits, and high token usage—and could not reliably handle more than a few hundred transactions.
Grounding & classification
Source type: technical build writeup
22 fields verified against source quotes, 1 dropped as unverifiable.
agentic workflowdata extractionsummarizationbuilder submittedfailure mode describedtools describedfinancial servicessoftwareaccuracy improvementthroughput increasetechnical build writeupback office opsfinance opsagentic task execution