How do I install Optim Engine?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Optim Engine need an API key?

No required environment variable is listed on this record.

What transport does Optim Engine use?

Optim Engine runs locally over stdio.

When was Optim Engine last checked?

Last checked 2026-10-28. Production OR-Tools optimization service exposing 11 tools across 4 intelligence levels (scheduling, routing, packing, sensitivity, robust, stochastic, Pareto, prescriptive). Dual-stack MCP + REST.

Optim Engine MCP server

⚡ OptimEngine

Production optimization platform — 4-layer architecture, OR-Tools CP-SAT, MCP-native, OAuth 2.1, observability-first.

Overview

OptimEngine solves NP-hard optimization problems (scheduling, routing, packing) and quantifies risk under uncertainty (sensitivity, robust, stochastic, Pareto). It exposes 11 tools across 4 intelligence levels (L1 deterministic → L3 prescriptive) over both REST and MCP (SSE + Streamable HTTP with OAuth 2.1).

The system is built around three engineering principles: mathematical correctness (real CP-SAT solvers, not LLM approximations), production hygiene (full observability stack, test coverage on business logic, load-tested), and agent-friendliness (MCP dual-stack, OAuth, rate limiting, JSON-only contracts).

Architecture

flowchart TB
    Client[AI Agents / MCP Clients / REST] --> Edge[Edge Proxy<br/>Vercel]
    Edge --> L2[L2 Core Gateway<br/>FastAPI · orchestration]
    L2 --> L1[L1 Solver Layer<br/>OR-Tools 9.15 CP-SAT<br/>FJSP · CVRPTW · Packing · Sensitivity · Robust · Stochastic · Pareto · Prescriptive]
    L2 --> L3[L3 Thin Proxies<br/>Payment rails · MCP discovery]
    L1 --> Obs[Observability<br/>OpenTelemetry · Prometheus · Grafana Cloud]
    L2 --> Obs
    L3 --> Obs

Four layers plus edge proxy, deployed across 6 Railway services + 1 Vercel function. All layers instrumented with OpenTelemetry tracing, Prometheus middleware, and Telegram alerting on payment events.

Tech Stack

Layer	Technologies
Runtime	Python 3.12, FastAPI, Uvicorn
Solvers	OR-Tools 9.15 CP-SAT, NumPy, custom Monte Carlo
MCP	FastMCP (SSE + Streamable HTTP), ScaleKit OAuth 2.1, PyJWT
Validation	Pydantic v2
Observability	OpenTelemetry SDK, Prometheus client, Grafana Cloud
Load testing	Locust 2.43
CI	GitHub Actions, pytest, pytest-cov
Deploy	Railway (compute), Vercel (edge), GitHub Actions (CI)

Capabilities

L1 — Deterministic Optimization

Solver	Problem	Key features
Scheduling	Flexible Job Shop (FJSP)	Precedence, time windows, per-machine durations, availability windows, quality gates, setup matrix
Routing	CVRPTW	Capacity, time windows, GPS distances, drop visits, 4 objectives
Bin Packing	Multi-dim packing	Weight + volume constraints, item quantities, groups, partial packing, 4 objectives

L2 — Optimization under Uncertainty

Module	Capability	Output
Sensitivity Analysis	One-at-a-time parameter perturbation	Fragility map, sensitivity scores, critical flags, risk ranking
Robust Optimization	Uncertainty ranges → worst-case protection	Robust solution, price of robustness, feasibility rate
Stochastic Optimization	Probability distributions → Monte Carlo	Expected value, VaR, CVaR (90/95/99 %), distribution summary

L2.5 — Multi-objective

Module	Capability	Output
Pareto Frontier	2-4 competing objectives	Non-dominated solutions, trade-off ratios, correlation analysis

L3 — Prescriptive

Module	Capability	Output
Prescriptive Advise	Historical data → Forecast → Optimize → Risk → Recommend	Actions, risk-adjusted makespan, feasibility risk assessment

Performance

Production benchmark summary (full report: BENCHMARKS.md):

757 requests served, 0 failures across 20 minutes of cumulative load
Infrastructure floor: ~200 ms (FastAPI + edge proxy + auth + observability overhead)
Saturation knee at 5 concurrent users on current Railway tier; system degrades gracefully past it
Bottleneck: solver CPU, not infrastructure — scaling path is horizontal at L1, not vertical at the gateway

Live Grafana dashboard: public-dashboards/optimengine

Testing & CI

121 tests across 7 solver modules, all passing
77 % overall coverage, 88 % average on business-logic engines
Runs on every push and pull request via GitHub Actions
Coverage reports uploaded as build artifacts (HTML + XML)

The test suite exercises real CP-SAT solves, not mocks: assertions verify mathematical properties (CVaR ordering, capacity constraints, monotonicity under perturbation) rather than just code paths.

v9.0.0 Highlights

Four scheduling-engine upgrades that close the gap between academic FJSP and how real workshops operate. All backward compatible with v8 requests.

Duration per machine — same task, different times on different machines:

{
  "task_id": "milling",
  "duration": 100,
  "eligible_machines": ["CNC-1", "CNC-2"],
  "duration_per_machine": {"CNC-1": 120, "CNC-2": 80}
}

Availability windows — multiple shifts and maintenance gaps per machine:

{
  "machine_id": "CNC-1",
  "availability_windows": [
    {"start": 0, "end": 480},
    {"start": 510, "end": 960}
  ]
}

Quality gates — jobs require minimum quality, machines have yield rates:

{ "job_id": "ORD-BOSCH", "quality_min": 0.97 }
{ "machine_id": "CNC-4", "yield_rate": 0.99 }

Setup-time matrix — sequence-dependent changeover costs:

{
  "setup_times": [
    {"machine_id": "M1", "from_job_id": "J1", "to_job_id": "J2", "setup_time": 15}
  ]
}

Quick Start

claude.ai (Remote MCP connector)

Add as a Remote MCP connector under Settings → Integrations:

https://optim-engine-production.up.railway.app/mcp

For OAuth-protected endpoint:

https://optim-engine-production.up.railway.app/mcp/v2

REST (curl)

curl -X POST https://optim-engine-production.up.railway.app/optimize_schedule \
  -H "Content-Type: application/json" \
  -H "X-Engine-Key: $ENGINE_API_KEY" \
  -d '{
    "jobs": [
      {"job_id": "J1", "tasks": [
        {"task_id": "cut", "duration": 30, "eligible_machines": ["M1", "M2"]},
        {"task_id": "weld", "duration": 20, "eligible_machines": ["M2"]}
      ]}
    ],
    "machines": [{"machine_id": "M1"}, {"machine_id": "M2"}],
    "objective": "minimize_makespan"
  }'

MCP client config

{
  "mcpServers": {
    "optim-engine": {
      "url": "https://optim-engine-production.up.railway.app/mcp"
    }
  }
}

Validation Case Study: MetalPrecision (synthetic)

Synthetic dataset modeled after Italian precision-machining workshops — 4 CNC centers (2015-2023 vintages), 5 client orders modeled on real automotive/industrial customers, 15 tasks. Used to validate solver behavior end-to-end across all 7 modules.

Tool	Result
`optimize_schedule`	Optimal makespan 225 min, 5/5 on-time, 83.3 % avg utilization, 0.04 s
`validate_schedule`	Zero violations, 2 improvement suggestions
`analyze_sensitivity`	73 solves in 3.6 s, most sensitive parameter identified (sensitivity score 8.9)
`optimize_robust`	Worst-case 210 min, price of robustness 7.7 %, 100 % feasibility
`optimize_pareto`	Trade-off frontier: 195/15 min vs 220/0 min, correlation -1.0
`optimize_stochastic`	30 Monte Carlo scenarios, CVaR95 = 188.5, CV 4.7 %
`prescriptive_advise`	Forecast +1.3 %/period, risk-adjusted range 167-183

Customer names in the dataset are illustrative. This is not a live customer deployment; it's a benchmark scenario used for solver validation and demo purposes.

Production Deployment

Endpoint	Role
`optim-engine-production.up.railway.app`	L1 + L2 + L3 compute (Railway, 6 services)
Edge proxy (Vercel)	CORS handling, header rewriting, edge caching
MCP connector on claude.ai	Tool surface for Claude clients

Discovery

Listed for agent-economy discovery on x402scan, Smithery, and the402.ai. The system supports x402 micropayments on Base and Solana for paid-tier endpoints; payment integration is documented in the L3 thin-proxy layer but is not the project's primary focus.

License

MIT — see LICENSE.

Built by Michele Campi.

⚡ OptimEngine

Production optimization platform — 4-layer architecture, OR-Tools CP-SAT, MCP-native, OAuth 2.1, observability-first.

Overview

Architecture

flowchart TB
    Client[AI Agents / MCP Clients / REST] --> Edge[Edge Proxy<br/>Vercel]
    Edge --> L2[L2 Core Gateway<br/>FastAPI · orchestration]
    L2 --> L1[L1 Solver Layer<br/>OR-Tools 9.15 CP-SAT<br/>FJSP · CVRPTW · Packing · Sensitivity · Robust · Stochastic · Pareto · Prescriptive]
    L2 --> L3[L3 Thin Proxies<br/>Payment rails · MCP discovery]
    L1 --> Obs[Observability<br/>OpenTelemetry · Prometheus · Grafana Cloud]
    L2 --> Obs
    L3 --> Obs

Tech Stack

Layer	Technologies
Runtime	Python 3.12, FastAPI, Uvicorn
Solvers	OR-Tools 9.15 CP-SAT, NumPy, custom Monte Carlo
MCP	FastMCP (SSE + Streamable HTTP), ScaleKit OAuth 2.1, PyJWT
Validation	Pydantic v2
Observability	OpenTelemetry SDK, Prometheus client, Grafana Cloud
Load testing	Locust 2.43
CI	GitHub Actions, pytest, pytest-cov
Deploy	Railway (compute), Vercel (edge), GitHub Actions (CI)

Capabilities

L1 — Deterministic Optimization

Solver	Problem	Key features
Scheduling	Flexible Job Shop (FJSP)	Precedence, time windows, per-machine durations, availability windows, quality gates, setup matrix
Routing	CVRPTW	Capacity, time windows, GPS distances, drop visits, 4 objectives
Bin Packing	Multi-dim packing	Weight + volume constraints, item quantities, groups, partial packing, 4 objectives

L2 — Optimization under Uncertainty

Module	Capability	Output
Sensitivity Analysis	One-at-a-time parameter perturbation	Fragility map, sensitivity scores, critical flags, risk ranking
Robust Optimization	Uncertainty ranges → worst-case protection	Robust solution, price of robustness, feasibility rate
Stochastic Optimization	Probability distributions → Monte Carlo	Expected value, VaR, CVaR (90/95/99 %), distribution summary

L2.5 — Multi-objective

Module	Capability	Output
Pareto Frontier	2-4 competing objectives	Non-dominated solutions, trade-off ratios, correlation analysis

L3 — Prescriptive

Module	Capability	Output
Prescriptive Advise	Historical data → Forecast → Optimize → Risk → Recommend	Actions, risk-adjusted makespan, feasibility risk assessment

Performance

Production benchmark summary (full report: BENCHMARKS.md):

757 requests served, 0 failures across 20 minutes of cumulative load
Infrastructure floor: ~200 ms (FastAPI + edge proxy + auth + observability overhead)
Saturation knee at 5 concurrent users on current Railway tier; system degrades gracefully past it
Bottleneck: solver CPU, not infrastructure — scaling path is horizontal at L1, not vertical at the gateway

Live Grafana dashboard: public-dashboards/optimengine

Testing & CI

121 tests across 7 solver modules, all passing
77 % overall coverage, 88 % average on business-logic engines
Runs on every push and pull request via GitHub Actions
Coverage reports uploaded as build artifacts (HTML + XML)

The test suite exercises real CP-SAT solves, not mocks: assertions verify mathematical properties (CVaR ordering, capacity constraints, monotonicity under perturbation) rather than just code paths.

v9.0.0 Highlights

Four scheduling-engine upgrades that close the gap between academic FJSP and how real workshops operate. All backward compatible with v8 requests.

Duration per machine — same task, different times on different machines:

{
  "task_id": "milling",
  "duration": 100,
  "eligible_machines": ["CNC-1", "CNC-2"],
  "duration_per_machine": {"CNC-1": 120, "CNC-2": 80}
}

Availability windows — multiple shifts and maintenance gaps per machine:

{
  "machine_id": "CNC-1",
  "availability_windows": [
    {"start": 0, "end": 480},
    {"start": 510, "end": 960}
  ]
}

Quality gates — jobs require minimum quality, machines have yield rates:

{ "job_id": "ORD-BOSCH", "quality_min": 0.97 }
{ "machine_id": "CNC-4", "yield_rate": 0.99 }

Setup-time matrix — sequence-dependent changeover costs:

{
  "setup_times": [
    {"machine_id": "M1", "from_job_id": "J1", "to_job_id": "J2", "setup_time": 15}
  ]
}

Quick Start

claude.ai (Remote MCP connector)

Add as a Remote MCP connector under Settings → Integrations:

https://optim-engine-production.up.railway.app/mcp

For OAuth-protected endpoint:

https://optim-engine-production.up.railway.app/mcp/v2

REST (curl)

curl -X POST https://optim-engine-production.up.railway.app/optimize_schedule \
  -H "Content-Type: application/json" \
  -H "X-Engine-Key: $ENGINE_API_KEY" \
  -d '{
    "jobs": [
      {"job_id": "J1", "tasks": [
        {"task_id": "cut", "duration": 30, "eligible_machines": ["M1", "M2"]},
        {"task_id": "weld", "duration": 20, "eligible_machines": ["M2"]}
      ]}
    ],
    "machines": [{"machine_id": "M1"}, {"machine_id": "M2"}],
    "objective": "minimize_makespan"
  }'

MCP client config

{
  "mcpServers": {
    "optim-engine": {
      "url": "https://optim-engine-production.up.railway.app/mcp"
    }
  }
}

Validation Case Study: MetalPrecision (synthetic)

Tool	Result
`optimize_schedule`	Optimal makespan 225 min, 5/5 on-time, 83.3 % avg utilization, 0.04 s
`validate_schedule`	Zero violations, 2 improvement suggestions
`analyze_sensitivity`	73 solves in 3.6 s, most sensitive parameter identified (sensitivity score 8.9)
`optimize_robust`	Worst-case 210 min, price of robustness 7.7 %, 100 % feasibility
`optimize_pareto`	Trade-off frontier: 195/15 min vs 220/0 min, correlation -1.0
`optimize_stochastic`	30 Monte Carlo scenarios, CVaR95 = 188.5, CV 4.7 %
`prescriptive_advise`	Forecast +1.3 %/period, risk-adjusted range 167-183

Customer names in the dataset are illustrative. This is not a live customer deployment; it's a benchmark scenario used for solver validation and demo purposes.

Production Deployment

Endpoint	Role
`optim-engine-production.up.railway.app`	L1 + L2 + L3 compute (Railway, 6 services)
Edge proxy (Vercel)	CORS handling, header rewriting, edge caching
MCP connector on claude.ai	Tool surface for Claude clients

Discovery

License

MIT — see LICENSE.

Built by Michele Campi.

Add this server

Host a copy with our keys and logs.

Server facts

Tools

Questions

How do I install Optim Engine?

Does Optim Engine need an API key?

What transport does Optim Engine use?

When was Optim Engine last checked?

Documentation

⚡ OptimEngine

Overview

Architecture

Tech Stack

Capabilities

L1 — Deterministic Optimization

L2 — Optimization under Uncertainty

L2.5 — Multi-objective

L3 — Prescriptive

Performance

Testing & CI

v9.0.0 Highlights

Quick Start

claude.ai (Remote MCP connector)

REST (curl)

MCP client config

Validation Case Study: MetalPrecision (synthetic)

Production Deployment

Discovery

License

Add this server

Host a copy with our keys and logs.

Server facts

Tools

Questions

How do I install Optim Engine?

Does Optim Engine need an API key?

What transport does Optim Engine use?

When was Optim Engine last checked?

Documentation

⚡ OptimEngine

Overview

Architecture

Tech Stack

Capabilities

L1 — Deterministic Optimization

L2 — Optimization under Uncertainty

L2.5 — Multi-objective

L3 — Prescriptive

Performance

Testing & CI

v9.0.0 Highlights

Quick Start

claude.ai (Remote MCP connector)

REST (curl)

MCP client config

Validation Case Study: MetalPrecision (synthetic)

Production Deployment

Discovery

License