AuthWeaver MCP

AuthWeaver MCP

Extracts prior-authorization evidence from synthetic FHIR data to create structured evidence packets for clinicians. It utilizes an LLM to identify medical-necessity details and clinical summaries from FHIR resources.

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README

AuthWeaver MCP

AuthWeaver is an MCP server tool that extracts prior-authorization evidence from synthetic FHIR data and returns a structured evidence packet for clinicians.

What It Does

  • Receives SHARP context (patient ID + optional FHIR server URL + token)
  • Queries a synthetic FHIR server for relevant resources
  • Uses an LLM to extract medical-necessity evidence
  • Returns a strict JSON evidence packet

Safety & Privacy

  • Stateless: no patient data stored
  • Data-minimized FHIR queries
  • Output is structured for audit review
  • Synthetic/de-identified data only

Quick Start (Local)

  1. python -m venv venv
  2. venv\Scripts\activate (Windows) or source venv/bin/activate (macOS/Linux)
  3. pip install -r requirements.txt
  4. Copy .env.example to .env and set an LLM key
  5. python server.py

MCP Tool

Tool name: extract_prior_auth_evidence

Inputs:

  • procedure_name (string)
  • sharp_context (object, optional)
  • patient_id (string, optional, dev only)

Output (JSON):

  • procedure
  • criteria_met (list of evidence items)
  • criteria_not_met (list)
  • clinical_summary

Demo Flow

  1. Prompt Opinion agent invokes extract_prior_auth_evidence with SHARP context.
  2. AuthWeaver pulls synthetic FHIR data and extracts evidence.
  3. Agent displays the evidence packet in the clinician workflow.

Notes

  • The MCP SDK wiring may differ based on Prompt Opinion docs. This repo includes a best-effort MCP server plus a FastAPI fallback for local testing.
  • Use only synthetic data (Synthea or public FHIR sandbox).

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