DokaAI MCP Service POC

Unified TypeScript service with:

• local stdio MCP server for Codex / Claude Desktop

• Vercel-ready HTTP MCP endpoint

• REST APIs backed by the same in-memory business data

• Phase 1 production structure for auth, routes, logging, errors, computed metrics, and evals

Setup

npm install
npm run build

Related MCP server: blinkit-mcp

Local MCP

Run the stdio MCP server:

npm start

Codex / Claude Desktop can still use:

{
  "command": "node",
  "args": ["/Users/ayushsrivastava0609/Doka-AI/mcp-server-poc/dist/index.js"]
}

Vercel Deployment

Deploy this folder as the Vercel project root:

/Users/ayushsrivastava0609/Doka-AI/mcp-server-poc

Hosted endpoints:

• GET /health

• GET /api/users

• GET /api/orders

• GET /api/users/:userId/orders

• GET /api/users/:userId/business-profile

• GET /api/business/summary

• POST /mcp

• GET /mcp

• DELETE /mcp

Authentication

For hosted connector authentication, the service exposes a POC OAuth flow:

• /.well-known/oauth-protected-resource/mcp

• /.well-known/oauth-authorization-server

• /oauth/register

• /oauth/authorize

• /oauth/token

When ChatGPT or Claude connects to /mcp without a token, the server returns 401 with a WWW-Authenticate header pointing to the OAuth protected-resource metadata. The connector client can then start the OAuth flow.

Set this environment variable in Vercel:

OAUTH_SIGNING_SECRET=your-long-random-signing-secret

During OAuth authorization, the demo page asks for:

DokaAI Username
DokaAI Password

Default mock login:

username: dokaai_demo_user
password: dokaai_demo_password

Override it in Vercel with:

MOCK_DOKAAI_USERNAME=your-demo-username
MOCK_DOKAAI_PASSWORD=your-demo-password

For this POC, a successful login creates a short-lived signed OAuth token. For production, replace this with a real user/session system.

You can also set this legacy static token for private API testing:

MCP_AUTH_TOKEN=your-long-random-token

/health, /api/health, OAuth metadata, and OAuth endpoints stay public. Everything else requires either an OAuth bearer token or:

Authorization: Bearer your-long-random-token

or:

x-api-key: your-long-random-token

Example:

curl https://your-app.vercel.app/api/users \
  -H "Authorization: Bearer your-long-random-token"

MCP Tools

• get_indian_users: Fetch all Indian test users from the backend API.

• get_orders: Fetch all business orders.

• get_user_orders: Fetch orders for one user, such as usr_001.

• get_user_business_profile: Fetch one user with subscription, orders, invoices, payments, refunds, computed metrics, risk signals, data limitations, and evidence guidance.

• get_business_summary: Fetch aggregate business metrics, risk counts, summary signals, data limitations, and evidence guidance.

The AI client can handle filtering, lookup, grouping, summaries, and cross-dataset reasoning from the returned data.

For better answer accuracy, important calculations are returned by the backend instead of being left to the model:

daysSinceLastLogin
daysSinceLastOrder
daysUntilRenewal
failedPaymentCount
overdueInvoiceCount
riskOrderCount
financialRisk
engagementRisk
renewalRisk
overallCustomerHealth

Every major MCP response also includes:

evidenceGuide
dataLimitations

Phase 1 Structure

The code is split by responsibility:

src/auth      OAuth-shaped auth and token helpers
src/config    runtime constants
src/http      request/response utilities
src/logging   structured JSON logger
src/mcp       MCP tools, prompts, evidence, server setup
src/routes    hosted MCP and REST route handlers
src/services  mock data, business metrics, data limitations
src/vercel.ts thin deployment entrypoint

See:

docs/phase-1-production.md
docs/evals.md
docs/usability.md

MCP Prompts

Prompts are reusable workflows the client can run.

• business_summary_report: Guides the AI to produce a simple business report with overview, revenue snapshot, risk signals, patterns, recommended actions, and evidence used.

• customer_segmentation_report: Guides the AI to analyze users by location, plan, profession, support, and billing signals.

• orders_analysis_report: Guides the AI to analyze revenue, product categories, payments, fulfillment, sales channel, and order risks.

• user_orders_report: Guides the AI to analyze orders for one user. Requires userId.

• customer_health_report: Guides the AI to analyze one customer using profile, subscription, orders, invoices, payments, refunds, and metrics. Requires userId.