idnow-mcp-server
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@idnow-mcp-server list my verification flows"
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.
IDnow Trust Platform MCP Server (Assignment)
Introduction
An MCP server that lets an LLM agents operate the IDnow Trust Platform sandbox - list the
verification flows on the account, kick off a new identity-verification session, check on
existing sessions, and pull full detail on one specific session. IDnow's customers are banks
and fintechs that need to verify their own customers remotely: a "flow" is a verification strategy
(video KYC, document scan, etc.), a "session" is one verification attempt for one person, and
the playerUrl a session returns is the link that person opens to actually go through verification.
This server is a small version of how those customers' own agents would eventually orchestrate that flow.
Everything here runs against the real IDnow sandbox (staging environment) - nothing is mocked.
Related MCP server: OpenAPI MCP Server
Setup & Run
git clone https://github.com/DevMohith/idnow-mcp-server.git
npm install
That's it - no
.env, no config step. Credentials are hardcoded insrc/config.js, which is exactly what the assignment asked for ("fine to put it directly in the code to keep things simple").In a real production integration these would live in a secrets manager and get injected at *runtime, never committed - that's a scope choice for this assignment, not an oversight.
for running it, and pointing an LLM client at it
There are three ways i designed to actually talk to this server, depending on what you're trying to check.
Before jumping I want to let you know - This server uses #stdio transport pipes, so it only works when the client and server are on the same machine :
the client spawn the server for you using the path in its config,
no manual "run the server" step needed.
A cloud-based client would need the server hosted separately over HTTP (Cloudflare Tunnel or VPS server)
Option A - Claude Desktop (the real end-to-end demo) with prettier print
Edit your Claude Desktop config:
macOS:
~/Library/Application Support/Claude/claude_desktop_config.jsonWindows:
%APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"idnow": {
"command": "node",
"args": ["/ABSOLUTE/PATH/TO/idnow-mcp-server/src/server.js"]
}
}
}For e.g. - Absolute Path looks like below ("C:\Users\mohithtummala\Desktop\idnow-mcp-server\src\server.js")
Restart Claude Desktop fully (not just the window - check it's not still running in the
background tasks or tray), and the 4 tools show up under [+ icon -> connectors -> manage connectors -> idnow] .
This is the setup transcript.md was captured against.
Option B - Claude Code (CLI)
If you've got the claude CLI installed, this is the fastest way to try it:
cd idnow-mcp-server
claude mcp add idnow -- node src/server.js
claudeThen just talk to it naturally - "which verification flows can I use?" and so on.
Option C - MCP Inspector (protocol-level, no LLM involved, Developer tool)
Useful for checking the raw tool schemas and responses without an LLM in the loop at all:
npm run mcp-inspectoropens a browser UI automatically, or for scripting/CI-style checks, the CLI mode works well:
This is how the tool schemas and error responses in this project were actually verified during development, independent of any particular chat client.
Other Options (e.g. Cursor, Cline)
Use mcp config provided in option A, to connect to the local mcp clients uses STDIN, STDOUT pipelines.
Architecture
Four files, each with one job:
src/config.js - The sandbox credentials and URLs, in one place
src/auth.js - OAuth2 client-credentials token manager — in-memory cache, 60s early-refresh buffer, in-flight request de-duplication, forceRefresh for 401 recovery
src/api.js - 4 endpoint functions, a shared request() helper with one-time 401 retry, and toCleanError() centralizing all error message formatting
src/server.js - FastMCP tool registration - 4 tools with Zod schemas and descriptions written for chaining, UserError wrapping so failures surface as clean tool errors
Transport is stdio, schemas are Zod, HTTP is axios - this is the stack I followed, and there's no database or Docker involved; state lives in-memory only, which is explicitly stated fine in Assignment.
The 4 tools in server.js
| Tool | Purpose | Endpoint |
list_flows - List available verification flows (GET /api/v1/flows/staging)
create_session - Start a new session for a flow (POST /api/v1/flows/{flowId}/staging/sessions)
list_sessions - List sessions for a flow (defaults to the first flow if none given) (GET /api/v1/flows/{flowId}/staging/sessions)
get_session - Full detail on one session (GET /api/v1/staging/sessions/{sessionId})
Results / Verification
See transcript.md for verification of the three prompts working against the sandbox
all four tools working end-to-end, including tool chaining and error handling tests.
Testing
npm test # tests/auth.test.js - proves getToken() fetches and caches it rather than re-authenticatrin on every call.
npm run smoke # scripts/smoke.js - This is where i started testing, walks all 4 endpoints against the live sandbox, prints resultsThe two are kept separate on purpose: npm test should be fast, deterministic, and side-effect
free, while the smoke script deliberately creates a real session every time it runs, which isn't
something you want happening on every npm test.
Design choices & trade-offs
Stack: Node.js + official MCP SDK + FastMCP SDK + Zod Input Validation + axios + Node test + assert runtime-checks
Transport: stdio, MCP's default - kept simple; an HTTP/SSE transport would remove the local-config step for a real deployment, I would extend in production.
MCP SDK: used FastMCP abstraction of official MCP SDK, which the brief explicitly allows either, mainly for the Zod-schema-to-tool-schema generation.
Client secret: hardcoded in
config.js, per the assignment's own instruction ("fine to put it directly in the code to keep things simple") - not an oversight, i would use secret manager and .env in prod.Real sandbox only: every response shape documented above, and everything in
transcript.md, came from actually hittingidnow.sx- nothing here is mocked or guessed.list_sessionsstatus filter runs client-side, not server-side: Verified directly against the live sandbox — and cross-checked IDnow's own docs - that the sessions-listing endpoint has no working status filter parameter; So added filter expression onlistSessions()fetches the full list first and filters bysessionStatusin JavaScript instead.
A note on AI usage
This was built by contesting on AI-generated code rather than completely accepting everything it given - for example, the initial auth module re-authenticated on every call; I added expiry tracking and in-flight deduplication.
Available Tools
4 toolscreate_sessionA
Create a new identity-verification session for a given flow. Requires the flowId (from list_flows) and a subjectId (your internal reference for the person being verified - never a real name or other PII). Returns the sessionId and a playerUrl and the initial sessionStatus, which is the link the end-user opens to complete verification. Session starts in CREATED and moves to RUNNING once the user begins.
Inputs: flowId (required), subjectId (required), locale.
get_sessionA
Retrieve full detail and current status for ONE verification session by its sessionId. Use this after create_session (or after finding a sessionId via list_sessions) to check the progress or outcome of a specific session.
Inputs: sessionId (required).
list_flowsA
List the identity-verification flows available to this IDnow account. Returns each flow's id, name, and description. Call this first to discover a flowId before creating or listing sessions.
Inputs: no named parameters.
list_sessionsA
List verification sessions for a flow, newest first. If flowId is omitted, the account's first available flow is used automatically. Returns each session's sessionId, sessionStatus (CREATED/RUNNING/COMPLETED/EXPIRED/ABORTED/ERROR), outcome, and createdAt. Use this to find the sessionId of a session you just created, then pass it to get_session for full detail.
Inputs: flowId, status.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 4 tools
Each tool targets a distinct resource and action: listing flows, listing sessions, creating a session, and retrieving a single session. There is no overlap or ambiguity between them.
All tools follow a consistent verb_noun pattern (list_flows, list_sessions, create_session, get_session) with no mixed conventions or deviations.
Four tools is well-scoped for an identity verification server, covering discovery, creation, and status retrieval without unnecessary bloat.
The core workflow is fully covered: discover flows, create sessions, list and retrieve session details. However, an explicit cancel or abort session operation is missing, which might be a minor gap given the session statuses include ABORTED.
Maintenance
Related MCP Connectors
An MCP server that provides an API to LLMs to manage their JumpCloud resources.
MCP server for secureFlows: token-free URL builders and integration-linting tools for AI agents.
MCP server for verifying EUDI/Talao wallet data via OIDC4VP (pull) for AI agents.
MCP server for AI agents to plan, verify, and deploy Cloudflare-native apps.
Related MCP Servers
- AlicenseAqualityDmaintenanceAn MCP server that enables LLMs to interact with Agent-to-Agent (A2A) protocol compatible agents, allowing for sending messages, tracking tasks, and receiving streaming responses.528MIT
- AlicenseAqualityDmaintenanceA generic MCP server that dynamically exposes any OpenAPI-documented REST API to LLMs by auto-discovering endpoints. It provides tools for exploring API capabilities and making authenticated requests directly through natural language interfaces.211 npmMIT
- AlicenseAqualityFmaintenanceMCP server for AgentFolio — the identity and reputation layer for AI agents. Query agent profiles, trust scores, verification status, and marketplace listings through 8 MCP tools.9118 npm1MIT
- AlicenseNot gradedqualityDmaintenanceMCP Server for AI agent identity and authorization. Create, verify, and manage agent identities with trust scores and scoped authorization tokens.48 PyPIMIT