Dental Clinic Loan Verification MCP Server
Enables automated dental clinic loan verification by combining rule-based ID validation with LLM-powered document analysis and fraud detection. It supports provider-agnostic vision and reasoning tools to assess document consistency, verify credentials like PAN and GST, and generate comprehensive risk narratives.
README
Dental Clinic Loan Verification — FastMCP Server
Provider-Agnostic LLM · Local (Slingshot) → Cloud (Horizon)
Switch LLM Provider — Zero Code Changes
# Use Anthropic Claude (default)
LLM_PROVIDER=anthropic
ANTHROPIC_API_KEY=sk-ant-...
# Use Google Gemini
LLM_PROVIDER=google
GOOGLE_API_KEY=AIza...
| Provider | Default model | Override |
|---|---|---|
| anthropic | claude-sonnet-4-20250514 | LLM_MODEL=claude-opus-4-5 |
| gemini-2.0-flash | LLM_MODEL=gemini-1.5-pro |
Phase 1 — Local Dev with VS Code / Slingshot
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env # add your API key
.vscode/mcp.json is already in the project — VS Code and Slingshot auto-discover it.
Open the folder in VS Code and the tools appear automatically in agent mode.
To switch to Gemini in VS Code, edit .vscode/mcp.json:
"env": { "LLM_PROVIDER": "google", "GOOGLE_API_KEY": "${env:GOOGLE_API_KEY}" }
python demo.py # end-to-end demo
mcp dev server.py # browser tool inspector
Phase 2 — Cloud Deploy on Horizon
# 1. Push to GitHub (.env in .gitignore)
git add server.py llm_tools.py llm_client.py requirements.txt .vscode/mcp.json
git push
# 2. Connect repo at https://horizon.prefect.io
# Horizon auto-detects requirements.txt
# 3. Set secrets in Horizon dashboard:
# LLM_PROVIDER=anthropic
# ANTHROPIC_API_KEY=sk-ant-...
# 4. Get live URL: https://dental-loan-verifier.fastmcp.app/mcp
Switch Slingshot from local → cloud by updating .vscode/mcp.json:
{
"servers": {
"dental-loan-verifier": {
"type": "http",
"url": "https://dental-loan-verifier.fastmcp.app/mcp"
}
}
}
Zero code changes between local and cloud.
File Structure
dental_loan_mcp/
├── server.py ← FastMCP server + rule-based tools
├── llm_tools.py ← LLM sub-agent tools (provider-agnostic)
├── llm_client.py ← Provider switch: Anthropic / Google
├── demo.py ← Local end-to-end demo
├── requirements.txt
├── .env.example ← Copy → .env, add keys
└── .vscode/
└── mcp.json ← Auto-discovered by VS Code + Slingshot
Tool Reference
| Tool | Type | LLM task |
|---|---|---|
doc_validate_document_set |
Rule-based stub | — |
pan_validate |
Rule-based stub → NSDL | — |
fraud_registry_check |
Rule-based stub | — |
aadhaar_verify |
Rule-based stub → UIDAI | — |
dci_credential_verify |
Rule-based stub → DCI | — |
gst_verify |
Rule-based stub → GST | — |
llm_health_check |
LLM | Shows active provider/model |
llm_analyze_document |
LLM Vision | Reads doc image, detects photo gender |
llm_cross_check_documents |
LLM Reasoning | Cross-doc inconsistencies |
llm_assess_name_match |
LLM Language | Indian name judgment |
llm_fraud_reasoning |
LLM Reasoning | Fraud signal clustering |
llm_generate_risk_narrative |
LLM Language | Final report writing |
orchestrate_stage2_verification |
Orchestrator | Full execution plan |
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