How do I use Dental Clinic Loan Verification MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Dental Clinic Loan Verification MCP Server Verify Dr. Mehta's clinic documents and generate a risk narrative" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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`
google	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