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avivek-dwivedi

Admissions MCP Hub

by avivek-dwivedi
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Python
Remote

Admissions MCP Hub

A governed Model Context Protocol server that exposes course, batch, fee, lead, and callback capabilities to two independent AI chat apps via a single MCP contract.

What this proves

  • One MCP server, two independent clients (learner + counsellor) — no duplicated integrations

  • DB credentials, auth, audit, validation — all centralized on the server

  • Writes require a confirmation gate (prepare → confirm) — an LLM cannot create a lead alone

  • Every tool call is audited (actor, client, args hash, result, latency)

  • A no-MCP comparison demo shows what you'd lose without MCP

Architecture

Related MCP server: SchoolBridge

Quick start

# 1. PostgreSQL
docker compose up -d postgres

# 2. Migrate + seed
uv sync
uv run alembic upgrade head
uv run python scripts/seed_demo.py

# 3. Start services (4 terminals)
uv run uvicorn services.mcp_server.app:asgi_app --port 8010
uv run uvicorn services.learner_host.api:app --port 8020
uv run uvicorn services.counsellor_host.api:app --port 8030
uv run streamlit run ui/app.py --server.port 8501

Open http://localhost:8501 — two chat tabs (Learner + Counsellor).

No-MCP comparison: uv run streamlit run ui/no_mcp_demo.py --server.port 8502

Try it

🎓 Learner Assistant

Prompt

What happens

What courses do you have?

Lists 4 courses

Tell me about the agentic AI course

Batch dates + fee quote + policy

What is the admissions policy?

Returns policy text

I'd like a callback

✅/❌ confirmation gate before creating lead

🎧 Counsellor Console

Prompt

What happens

What courses are available?

Lists 4 courses

Show me upcoming batches for agentic AI

3 batches with seats

Generate a fee quote for mlops

Quote ID + total (INR)

List my leads

Shows assigned leads

Update stage for SCAI-XXXXXXXX to enrolled

✅/❌ confirmation gate

See RUN_GUIDE.md for full prompts + expected answers.

Architecture

Port

Service

Role

5433

PostgreSQL

Source of truth (courses, batches, leads, audit)

8010

MCP Server

Tools (11) + Resources (8) + Prompts (2), JWT auth, RBAC, audit

8020

Learner Host

LangGraph app — learner JWT, confirmation gate for writes

8030

Counsellor Host

LangGraph app — counsellor JWT, lead management

8501

Streamlit UI

Two chat tabs (MCP-based)

8502

No-MCP Demo

Same flow, direct DB — shows what MCP protects against

Stack: Python 3.11 · MCP SDK · LangGraph · FastAPI · SQLAlchemy 2 · PostgreSQL 16 · Pydantic v2 · Ollama (qwen3.5:2b) · Streamlit

Key concepts

Concept

Where

Why it matters

Confirmation gate

leads_prepare → leads_confirm_create

LLM can't create a lead without human ✅

Idempotency

IdempotencyRepository (payload hash)

Network retries don't create duplicates

RBAC

ROLE_TOOLS map in _runner.py

Learner can't see other people's leads

Audit

ToolAuditEvent table

Every call logged: who, what, result, latency

Statelessness

Server-minted IDs (quote_id, lead_id)

Horizontal scaling without sessions

Project structure

scai-mcp-admissions/
├── services/
│   ├── mcp_server/          # MCP server (tools, resources, prompts, auth, audit)
│   ├── learner_host/        # LangGraph learner app (port 8020)
│   └── counsellor_host/     # LangGraph counsellor app (port 8030)
├── ui/
│   ├── app.py               # Streamlit — 2 chat tabs (MCP)
│   └── no_mcp_demo.py       # Streamlit — no-MCP comparison (direct DB)
├── packages/
│   ├── contracts/           # Pydantic tool inputs/outputs + domain models
│   ├── shared/              # Config, LLM adapter, JWT tokens
│   └── observability/       # Structured logging, tracing
├── scripts/
│   ├── seed_demo.py         # Seed 4 courses, 4 batches, 4 fee plans, 3 policies
│   ├── issue_dev_token.py   # Issue dev JWTs for manual testing
│   └── run_demo_checks.py   # Smoke tests against running server
├── tests/                   # unit, contract, integration, security, e2e
├── migrations/              # Alembic migrations
├── data/demo_seed/          # Seed data + knowledge_base.json
├── mcp_concept.ipynb        # MCP concept notebook (what/why/how/scale/use cases)
├── mcp_flow_diagram.mmd     # Mermaid source for architecture diagram
├── mcp_flow_diagram.png     # Rendered architecture diagram
├── RUN_GUIDE.md             # Step-by-step run guide with test prompts
└── docker-compose.yml        # PostgreSQL 16

Tests

uv run pytest              # all tests
uv run pytest -m unit      # just unit tests
uv run pytest -m contract  # contract tests

References

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

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