ClaimsProcessingAssistant MCP Server

Created By
chbhargavareddya year ago
This is a Model Context Protocol (MCP) server that provides claims processing capabilities through integration with a Supabase database. The server enables AI assistants to interact with insurance claim processing workflows and data through natural language.
Overview

What is ClaimsProcessingAssistant MCP Server?

ClaimsProcessingAssistant MCP Server is a TypeScript-based backend designed for managing insurance claims through the Model Context Protocol (MCP). It integrates with a Supabase database to facilitate AI-driven interactions in insurance claim processing workflows.

How to use ClaimsProcessingAssistant MCP Server?

To use the server, clone the repository, set up your environment with Supabase and Redis, and run the server locally. You can interact with the API endpoints to submit and validate claims.

Key features of ClaimsProcessingAssistant MCP Server?

  • MCP Protocol Implementation: Provides a standardized API for claim processing.
  • Authentication & Authorization: Ensures secure access for users.
  • Advanced Claim Validation: Includes a rules engine for various checks.
  • AI Document Analysis: Utilizes AI for intelligent document validation.
  • Supabase Integration: Offers a scalable Postgres backend.
  • Redis Caching: Enhances performance with fast access to frequent queries.
  • Audit Trail: Maintains full traceability of claim actions.
  • Comprehensive Testing: Includes unit, integration, and end-to-end tests.

Use cases of ClaimsProcessingAssistant MCP Server?

  1. Automating insurance claim submissions.
  2. Validating claims against policy rules and duplicates.
  3. Analyzing documents for compliance and accuracy.

FAQ from ClaimsProcessingAssistant MCP Server?

  • What technologies are used?

The server is built with TypeScript and integrates with Supabase and Redis.

  • Is there a demo available?

You can clone the repository and run it locally to test its features.

  • How can I contribute?

Fork the repository, make your changes, and submit a pull request after testing.

Project Info
Created At
a year ago
Updated At
a year ago
Author Name
chbhargavareddy
Star
1
Language
TypeScript
License
MIT license

Recommend Servers

View All
AI Work Market — USDC settlement rails for AI labor on Base Mainnet)
@Dario (DME)

AI Work Market is a USDC escrow protocol on Base Mainnet, designed for autonomous AI agents to find work, post jobs, and settle payments without humans in the loop. This MCP server exposes 10 tools: **Escrow lifecycle** - `create_intent_quote` — get calldata + gas estimate for funding a new escrow intent - `submit_proof_quote` — get calldata for the seller to submit a proof URI - `release_funds_quote` — get calldata for the buyer to release payment (or claim/refund) **x402 single-call binding** - `x402_consume` — replaces the 5-step x402 flow with one HMAC-signed POST that returns a delivery URL **Onboarding & discovery** - `agent_onboard` — generate a signed agent card with marketplace attestation - `agent_search` — tf-idf search over the live agent catalog - `agent_reputation` — server-side reputation from on-chain Released/Refunded/Disputed events **Live state** - `system_status` — live on-chain state (nextIntentId, accumulatedFees, contract balance, owner) - `escrow_rules` — contract semantics, lifecycle, call guides, failure modes - `events_subscribe` — SSE stream of new on-chain intent events All endpoints are serverless (Vercel) and return their schema on GET. No browser, no wallet UI required for an agent to integrate. The protocol takes a 1% commission on every settlement; the rest goes to the seller. The full AgentCard is at `/.well-known/agent-card.json` (A2A-compatible). The OpenAPI 3.0.3 spec is at `/.well-known/openapi.json` with `components.securitySchemes` (none, hmacX402). `robots.txt` allows GPTBot, ClaudeBot, anthropic-ai, PerplexityBot, Google-Extended, Applebot-Extended, CCBot, Amazonbot.

7 hours ago
Bring your real authenticated browser session to AI coding agents. Local-first MCP server + Chrome MV3 extension. No cloud. No telemetry.
@Cubenest

peek records the user's actual logged-in browser (DOM via rrweb, console events, network metadata, optional response bodies via opt-in Deep capture) through a Chrome MV3 extension. The extension ships events through a native-messaging stdio bridge to a local MCP server (peek-mcp), which persists them to a SQLite database at ~/.peek/sessions.db. AI coding agents (Claude Code, Cursor, Cline, Windsurf) read sessions from the database via 10 MCP tools: Tool What it does list_recent_sessions List recently recorded sessions (id, origin, ts, event count). get_session_summary LLM-readable narrative summary of a session. get_session_console_errors Console errors recorded in a session. get_session_network_errors Failed/notable network requests in a session. get_user_action_before_error Last N user actions before a console error. generate_playwright_repro Generate a runnable Playwright test from a session. get_dom_snapshot Reconstruct the DOM at a given timestamp. query_dom_history Timeline of attribute/text changes for a selector. request_authorization Side-panel consent for write actions (Level 3). execute_action Dispatch a UI action (gated by permission level + destructive blocklist). Why local-first matters Every other "browser session for AI" tool ships to a vendor cloud. peek's SQLite + extension live on the user's machine — no remote endpoints, no telemetry. The privacy policy (docs/peek/PRIVACY_POLICY.md) is the source of truth. Install # 1. Add the MCP server to Claude Code claude mcp add peek -- npx -y @peekdev/mcp # 2. Install the Chrome extension from the Chrome Web Store # (link added once the CWS listing is approved)

a day ago