Architecture

Architecture

System Overview

AI Agent (Claude, Cursor, Copilot, etc.)
       │
       │ MCP Protocol (stdio or SSE)
       ▼
┌─────────────────────────────────┐
│   MCP Server (FastMCP)           │
│   ├─ 20 built-in tools           │
│   ├─ Custom tools (from YAML)    │
│   ├─ Session manager             │
│   ├─ Security validator          │
│   └─ Result formatter            │
└──────────┬──────────────────────┘
           │
┌──────────▼──────────────────────┐
│   Job Executor                    │
│   ├─ Hybrid sync/async execution  │
│   ├─ Timeout-based promotion      │
│   └─ Progress injection           │
└──────────┬──────────────────────┘
           │
┌──────────▼──────────────────────┐
│   Engine Pool Manager             │
│   ├─ Elastic scaling (min→max)    │
│   ├─ Health checks                │
│   ├─ Proactive warmup             │
│   └─ Idle scale-down              │
└──────────┬──────────────────────┘
           │
┌──────────▼──────────────────────┐
│   MATLAB Engines (2020b+)         │
│   Engine 1 │ Engine 2 │ ... │ N   │
└───────────────────────────────────┘

Component Details

MCP Server (server.py)

The entry point. Uses FastMCP to handle MCP protocol details. Responsibilities:

• Register all 20 tools + custom tools
• Manage server lifecycle (startup, shutdown, drain)
• Route tool calls to implementation modules
• Run background tasks (health checks, cleanup)

Engine Pool Manager (pool/manager.py)

Manages a pool of MATLAB engine instances:

• Elastic scaling: Starts with min_engines, scales up to max_engines under load
• Proactive warmup: When utilization exceeds proactive_warmup_threshold (80%), starts a new engine before it's needed
• Scale-down: Engines idle longer than scale_down_idle_timeout (15 min) are stopped, down to min_engines
• Health checks: Periodic 1+1 eval to verify engines are responsive. Unhealthy engines are replaced
• Queue: Requests wait in an async queue when all engines are busy

Engine Wrapper (pool/engine.py)

Wraps a single matlab.engine instance:

• Start/stop lifecycle
• Execute code (sync or background)
• Workspace reset between sessions
• Health check ping
• State tracking (idle, busy, error)

Job Executor (jobs/executor.py)

Hybrid sync/async execution:

1. Code is security-checked (blocked functions scan)
2. Job context is injected (__mcp_job_id__, __mcp_temp_dir__)
3. Execution starts synchronously
4. If sync_timeout exceeded → auto-promote to async, return job_id
5. Background task monitors completion, stores result

Job Tracker (jobs/tracker.py)

In-memory store for job metadata:

• Create/get/list/cancel jobs
• Prune completed jobs older than job_retention_seconds
• Thread-safe with asyncio locks

Session Manager (session/manager.py)

Per-user session isolation:

• Each session gets a unique temp directory
• Workspace cleared between sessions (when workspace_isolation=true)
• Expired sessions cleaned up after session_timeout
• stdio transport uses a single "default" session

Security Validator (security/validator.py)

Pre-execution security checks:

• Function blocklist: Scans code for blocked functions (system, unix, dos, !, eval, feval, evalc, evalin, assignin, perl, python). Smart enough to strip string literals and comments first to avoid false positives
• Filename sanitization: Prevents path traversal in upload filenames
• Upload size limits: Enforces max_upload_size_mb

Result Formatter (output/formatter.py)

Structures tool responses:

• Text output formatting with length limits
• Variable formatting from workspace queries
• Success/error response builders
• Delegates to Plotly converter and thumbnail generator

Plotly Converter (output/plotly_convert.py, output/plotly_style_mapper.py + matlab_helpers/mcp_extract_props.m)

Converts MATLAB figures to interactive Plotly JSON:

1. MATLAB-side: mcp_extract_props.m extracts raw figure properties (line, scatter, bar, histogram, surface, image)
2. Python-side: plotly_style_mapper.py converts MATLAB styles (line styles, markers, colormaps, fonts, colors) to Plotly equivalents, with WebGL support for large datasets (10,000+ points)
3. Python-side: plotly_convert.py / load_plotly_json() reads the saved JSON file
4. Result includes: Plotly JSON + static PNG + optional thumbnail

Data Flow

Sync Execution

Agent → execute_code("x = magic(3)")
  → Security check (OK)
  → Acquire engine from pool
  → Inject job context
  → Engine.eval("x = magic(3)")
  → Complete in <30s
  → Format result
  → Release engine
  → Return result to agent

Async Promotion

Agent → execute_code("long_simulation()")
  → Security check (OK)
  → Acquire engine
  → Engine.eval (background=True)
  → 30s timeout exceeded
  → Return {job_id: "abc123", status: "running"}
  → Agent polls get_job_status("abc123") → progress: 45%
  → Agent polls get_job_status("abc123") → progress: 90%
  → Agent calls get_job_result("abc123") → full result
  → Engine released

Transport Modes

stdio (Default)

• One agent, one session
• Communication via stdin/stdout
• Simplest setup, no network

SSE (Server-Sent Events)

• Multiple agents, multiple sessions
• HTTP-based, supports remote connections
• Session isolation via session IDs
• Production: Put behind a reverse proxy with auth (require_proxy_auth: true)