SmartPark Reservation MCP Server

SmartPark Reservation MCP Server

Provides a secure MCP tool to append confirmed parking reservations to durable storage with validation, audit logging, and concurrency safety.

Category
Visit Server

README

SmartPark Reservation MCP Server — Stage 3

A real MCP (Model Context Protocol) server, built with the official mcp Python SDK, that writes confirmed parking reservations to durable storage once a human administrator has approved them (Stage 2).

This is a separate repository/service from Stages 1 and 2 — Agent 2 calls this server's single tool over the network, the same way any MCP-compliant client would.

What it does

Exposes exactly one MCP tool: write_confirmed_reservation. Once the administrator (Agent 2) approves a reservation, Agent 2 calls this tool, which:

  1. Validates every field (rejects the | delimiter, newlines, null bytes, and overly long values — these would corrupt the file format or let a caller inject a fake extra record).

  2. Appends one line to data/confirmed_reservations.txt in the exact required format:

    Name | Car Number | Reservation Period | Approval Time
    
  3. Does this atomically and safely under concurrent calls (cross-platform file locking), and logs the attempt (success or rejection) to an audit log.

Architecture

 Agent 2 (Stage 2 Admin Agent)              Agent 3 (this repo)
┌──────────────────────────┐   MCP/HTTP    ┌────────────────────────────┐
│ apply_decision(confirmed) │ ────────────▶ │ POST /mcp                  │
│                           │  Bearer token │  BearerAuthMiddleware       │
│ integration/               │  required    │  RateLimitMiddleware        │
│  admin_agent_client.py     │              │  MCPServer                 │
│  -> record_confirmed_      │              │   └─ write_confirmed_       │
│     reservation()          │              │      reservation tool      │
└──────────────────────────┘              │       └─ validate ─┐        │
                                            │                    ▼        │
                                            │      data/confirmed_        │
                                            │      reservations.txt       │
                                            │      (file-locked append)   │
                                            │      + data/audit.log       │
                                            └────────────────────────────┘

Why the official MCP SDK (not just a REST endpoint)

Stage 3 asks for a real MCP server, or a FastAPI stand-in if that's not feasible. The official mcp Python SDK (MCPServer, formerly known as FastMCP) turned out to be fully usable here: it builds a standard streamable-HTTP ASGI app (so it composes with normal Starlette middleware for auth/rate-limiting) and ships with production security features out of the box — DNS-rebinding protection, host/origin allowlisting, and request-body size limits — which is exactly what "secure and resistant to unauthorized access" calls for. Building a hand-rolled JSON-RPC-over-FastAPI server would have meant re-implementing a worse version of these same protections.

Security measures

Concern Mitigation
Unauthorized callers BearerAuthMiddleware — every request needs Authorization: Bearer <MCP_API_KEY>, compared with hmac.compare_digest (no timing side-channel). No key configured → the server generates and prints a random one-time key rather than silently allowing unauthenticated access.
Abuse / DoS RateLimitMiddleware — fixed-window limit per client IP (default 30 req/min, configurable).
DNS rebinding / host spoofing The MCP SDK's built-in TransportSecuritySettings (enabled by default), with configurable ALLOWED_HOSTS/ALLOWED_ORIGINS for production.
Path traversal Structurally impossible — the output file path is fixed by server config and is never accepted as a tool argument.
Record/format injection Every field is validated to reject the `
Concurrent-write corruption Cross-platform atomic file locking (filelock) around every append — tested with 20 simultaneous threads writing with zero interleaved/corrupted lines.
Oversized/malformed requests max_request_body_size capped at 64KB (reservation payloads are tiny; anything bigger is rejected outright).
Traceability without leaking secrets Every write attempt (success or rejection) is appended to data/audit.log, with only a masked token prefix — the real API key is never logged.
Reliability A stuck lock surfaces as a clear, catchable TimeoutError after 5s rather than hanging the server; invalid input raises a specific InvalidReservationField the MCP layer turns into a normal tool-error result rather than crashing the process.

Project structure

parking_mcp_server/
├── app/
│   ├── config.py               # .env-driven configuration
│   ├── reservation_writer.py    # field validation + atomic file append
│   ├── audit_log.py              # append-only audit trail
│   ├── security.py                # BearerAuthMiddleware, RateLimitMiddleware
│   └── server.py                   # MCPServer + tool + ASGI wiring
├── integration/
│   ├── admin_agent_client.py       # what Stage 2 imports to call this server
│   └── db_patch_example.py         # exact diff for Stage 2's decision endpoint
├── tests/                           # 22 pytest tests across 4 modules
├── demo.py                           # end-to-end demo (live server + real MCP client)
├── view_reservations.py               # quick CLI viewer for the output file/audit log
├── run_server.sh / run_server.ps1
├── requirements.txt
├── .env.example
└── .github/workflows/ci.yml           # runs tests on both ubuntu-latest and windows-latest

Setup

Works identically on Windows, macOS, and Linux — pure Python (the official mcp SDK, Starlette/uvicorn, and the cross-platform filelock package; no OS-specific binaries).

python -m venv .venv && source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install -r requirements.txt
cp .env.example .env
# Set MCP_API_KEY in .env for a stable key -- otherwise a random one-time
# key is generated and printed at startup.

Windows-specific notes

  • Use run_server.ps1 instead of run_server.sh.
  • filelock uses msvcrt under the hood on Windows automatically — no extra configuration needed for the concurrency-safety guarantees.
  • CI runs the full test suite on windows-latest as well as ubuntu-latest (see .github/workflows/ci.yml).

Usage

Run the demo (starts its own server, no setup needed):

PYTHONPATH=. python demo.py

Run the real server:

./run_server.sh        # Linux/macOS
.\run_server.ps1        # Windows

It prints a generated MCP_API_KEY on first run if you haven't set one — copy it into your client's Authorization: Bearer <key> header (or into Stage 2's .env as MCP_API_KEY, see Integration below).

View what's been written:

PYTHONPATH=. python view_reservations.py

Run tests:

PYTHONPATH=. python -m pytest tests/ -v

Integration with Agent 2 (Stage 2)

  • integration/admin_agent_client.py — the client Stage 2 imports (record_confirmed_reservation(name, car_number, reservation_period, approval_time)), which opens a real MCP client session, authenticates, and calls the tool.
  • integration/db_patch_example.py — the exact before/after diff for Stage 2's POST /requests/{id}/decision endpoint: once a reservation is marked "confirmed", it now also calls record_confirmed_reservation(...) so the approval is durably recorded here, not just in Stage 2's own request-tracking table.
  • Stage 2's .env needs: MCP_SERVER_URL=http://127.0.0.1:8002/mcp and MCP_API_KEY=<the same key configured on this server>.

Notes / limitations (Stage 3 scope)

  • Rate limiting and the audit log are in-memory/local-file, appropriate for a single-process deployment; a multi-instance deployment would move the rate limiter to a shared store (e.g. Redis) and the audit log to a centralized logging system.
  • MCP_API_KEY is a single shared secret rather than per-client credentials — sufficient for a single trusted caller (Agent 2), but a multi-tenant deployment would want per-client tokens or the MCP SDK's full OAuth 2.1 support (auth_server_provider/AuthSettings, not used here since it requires standing up a separate OAuth issuer).
  • The reservations file is a flat text file, as specified; a higher-volume production deployment would likely write to a database instead, behind the same tool interface.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured