ETL-D MCP Server: Deterministic CSV Parsing for Claude to Prevent Financial Hallucinations

A developer has open-sourced ETL-D, an MCP server for Claude Desktop designed to prevent Claude from hallucinating decimal points when parsing financial CSVs and other structured B2B data formats. The tool addresses the "token tax" of sending raw formats to an LLM's context window and the "hallucination risk" where misplaced commas can turn $100.50 into $10,050.00.

Architecture: The Three-Layer Waterfall

The server processes files through three strict layers when Claude is asked to parse them:

• Layer 1 (Heuristics): Uses 100% Python with regex, dateutil, and strict structural parsers for known formats. The developer reports a load test with 200 parallel requests achieving ~70ms response times with 0 LLM calls and zero hallucination risk.
• Layer 2 (Semantic Routing): If CSV headers are obfuscated, a lightweight router maps columns to strict Pydantic schemas.
• Layer 3 (LLM Fallback): Only triggered for high-entropy "free-text" noise, using Llama 3.3 70b under the hood to enforce JSON schemas.

The result is a perfectly clean, flattened JSON array returned to Claude for reasoning.

Setup and Availability

The tool has been approved on the official Anthropic MCP Registry. To use it, developers need to configure their claude_desktop_config.json. The source code is available on GitHub at pablixnieto2/etld-mcp-server.

The developer built this after identifying that "LLM-first" is the wrong architecture for structured B2B data like broker trade histories, bank statements (Norma 43), or SEC XBRL files, arguing that AI agents shouldn't read CSVs directly but should query deterministic middleware instead.