About etld-mcp-server
ETL-D is an MCP server for deterministic parsing and enrichment of structured B2B and financial data. It is intended for agents and workflows that need validated JSON from files and documents without sending entire raw inputs into an LLM.
Key features
- 01Waterfall parsing with heuristic, semantic, and LLM layers
- 02Parse bank statements, trades, and EDI
- 03Convert PDFs while preserving table structure
- 04Extract invoice and résumé fields
- 05Normalize amounts, dates, and addresses
- 06Map accounting concepts to standards
Use cases
- 01Turn Norma 43 bank files into structured JSON
- 02Extract trades, fees, and dividends from broker exports
- 03Convert a PDF's tables into Markdown for agent processing
- 04Standardize financial amounts and dates
MCP support
Exposes parsing, extraction, conversion, enrichment, and provisioning tools to MCP clients.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
npx -y etld-mcp-serverETLD_API_KEYSecretAPI Key for ETL-D services (Master Key or Provisioned Unkey)Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEETL-D MCP Server 🌊
ETL-D is a deterministic data middleware designed to act as a shield for AI Agents. It stops LLMs from "hallucinating" over structured data by providing a strict, 3-layer parsing architecture via the Model Context Protocol (MCP).
⚠️ The Problem: The "Data Tax" & Hallucinations
Standard LLMs are terrible at reading raw B2B files (CSV, PDF, EDI, Norma 43). They suffer from:
- Token Exhaustion: Sending a 5,000-row CSV to context is a waste of money.
- Precision Loss: LLMs struggle with spatial alignment. A misplaced comma in a bank statement is a financial catastrophe.
- Non-Determinism: You can't build a reliable pipeline if the output format depends on the LLM's "mood."
✅ The Solution: 3-Layer Waterfall Architecture
The ETL-D engine processes every request through a strict hierarchy:
- Layer 1 (Heuristic): 100% Python-native deterministic parsers. 0% Hallucination risk. ~70ms latency.
- Layer 2 (Semantic): Column-to-Schema alignment using embedding-based routing.
- Layer 3 (LLM Shield): Strict JSON enforcement using Llama 3.3 70B with
additionalProperties: falseconstraints for high-entropy noise.
🚀 Quick Start (Claude Desktop)
- Open your
claude_desktop_config.json. - Add the ETL-D server (Ensure you use version
3.3.3):
{
"mcpServers": {
"etld": {
"command": "npx",
"args": [
"-y",
"@pablixnieto2/[email protected]"
],
"env": {
"ETLD_API_KEY": "YOUR_API_KEY_HERE"
}
}
}
}
🔑 How to Get an API Key?
-
Option A: Manual Purchase (Human) Get a Starter Pack ($5 / 5,000 credits) or a Subscription at api.etl-d.net.
-
Option B: Zero-Touch Provisioning (Agent-Led) 🤖 If you don't have a key, simply ask Claude: "I don't have an ETL-D key, can you help me get one?". The agent will call the /provision tool, generate a Stripe Checkout link for you, and automatically set up the key once paid. Zero-touch, human-in-the-loop.
🛠️ Available MCP Tools
- Financial & B2B Heavy Lifting parse_bank_statement: Support for Spanish Norma 43 (N43). Turns raw bank files into clean JSON.
parse_trade_history: Deterministic extraction of trades, fees, and dividends from complex broker exports.
parse_edi: ANSI X12 EDI parser (Optimized for 850 Purchase Orders).
generate_sepa_xml: JSON to PAIN.008 (Direct Debit) XML generator.
- Document Intelligence pdf_to_spatial_markdown: Crucial for Agents. Converts PDFs to Markdown preserving table structures before the LLM reads them.
extract_invoice / extract_resume: High-accuracy schema extraction for standard B2B documents.
- Atomic Enrichment (1 Credit/call) enrich_amount: Cleans "Total: 1.240,50€" into {amount: 1240.50, currency: "EUR"}.
enrich_date: Resolves human-readable dates ("next Friday at 5pm") with Timezone awareness.
enrich_address: Standardizes global messy addresses into structured components.
accounting_map: Maps concepts to ES PGC, US GAAP, or IFRS.
🏗️ Ecosystem
Cloud Engine: Hosted at api.etl-d.net (Python/FastAPI).
Python SDK: pip install etld.
n8n Nodes: Available in the n8n community as n8n-nodes-etld.
⚖️ License
MIT - Created by Pablixnieto2
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.