JAOT MCP Server
Click on "Install 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., "@JAOT MCP ServerSolve a knapsack: items (w,v): (2,5),(3,7),(1,3); capacity 4"
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.
JAOT — Just Another Optimization Tool
A self-hostable optimization platform. Describe a problem in natural language or JSON, get the optimal solution back — no solver expertise required. Build models with an AI assistant, share them in a marketplace, expose them to AI agents over MCP, or just hit the REST API.
What it is
JAOT wraps industrial MIP/LP solvers (SCIP, HiGHS) behind a multi-tenant API,
a visual builder, and an LLM formulation assistant. It is a platform, not a
library and not a hosted SaaS — you run it yourself with docker compose up.
Solver-agnostic core — an
OptimizationProblemschema that stays independent of the solver. Ships SCIP (via PySCIPOpt) and HiGHS (via highspy); an optional Hexaly adapter is bring-your-own-license.LLM formulation assistant — turn a natural-language description into a runnable model, grounded in a RAG index over the template library (Qdrant + local sentence-transformers; no data leaves your box except the Claude calls you opt into).
Solution explainer + sensitivity — don't just solve, understand. Every solve reports shadow prices, binding constraints, and variable reduced costs (exact for LP, approximate for MIP), and a one-click AI explanation translates the result into plain language grounded strictly in your actual numbers.
Model marketplace — a free, collaborative gallery: publish your models and activate community ones. No prices or commissions.
MCP server — exposes solver tools to AI agents (Claude, etc.) via the Model Context Protocol.
102 templates + 27 problem generators — knapsack, vehicle routing, scheduling, production planning, portfolio, a full MDPDP-TW formulation, and more.
Credits ledger, multi-tenant auth, admin panel, i18n (en/es/ca/fr/de), and a Prometheus/Grafana/Alertmanager monitoring stack — included.
Monetization is off by default (MONETIZATION_ENABLED=false): the
marketplace is free and collaborative. A self-hosted deployment can enable the
paid marketplace by flipping the flag and bringing its own Stripe keys — that
billing code is complete but has never been exercised against live Stripe,
so test before you charge real money.
Related MCP server: dymola-mcp-server
Quickstart
git clone https://github.com/avallavall/jaot.git && cd jaot
cp .env.example .env # includes first-run admin credentials — change the password
docker compose up -d # migrates, seeds the catalog, creates your admin on first bootThen check what still needs configuring (SMTP, AI key…):
docker compose exec api python scripts/doctor.pySee Configuration for the full guide.
Open http://localhost:3000 and log in with your SEED_ADMIN_* credentials — or
mint an API key and solve over HTTP:
docker compose exec api python scripts/ensure_admin_api_key.py # prints your API key
curl -X POST http://localhost:8001/api/v2/solve \
-H "Authorization: Bearer <your-api-key>" \
-H "Content-Type: application/json" \
-d '{"name":"test","variables":[{"name":"x","type":"continuous","lower_bound":0,"upper_bound":10}],"objective":{"sense":"maximize","expression":"3*x"},"constraints":[{"name":"c1","expression":"x <= 5"}]}'Returns {"status":"optimal","objective_value":15.0,...}. Full setup guide →
docs/getting-started/QUICKSTART.md.
Architecture
┌──────────────────────────────────────────────┐
│ Next.js 16 frontend (5 locales) │
└───────────────┬──────────────────────────────┘
│ REST + SSE + WebSocket
┌───────────────▼──────────────────────────────┐
│ FastAPI (Python 3.12) │
│ auth · solve · LLM/RAG · credits · │
│ marketplace · triggers · MCP server │
└──┬─────────┬──────────┬──────────┬────────────┘
│ │ │ │
┌──▼──┐ ┌────▼────┐ ┌──▼──┐ ┌─────▼─────┐ ┌────────────┐
│ Pg │ │RabbitMQ │ │Redis│ │ Qdrant │ │ Anthropic │
│ 18 │ │+ Celery │ │ │ │ (RAG) │ │ Claude API │
└─────┘ │ workers │ └─────┘ └───────────┘ └────────────┘
│ SCIP / │
│ HiGHS / │
│ Hexaly │
└─────────┘A modular monolith: the solver is the first extracted bounded context
(app/domains/solver/), behind a SolverAdapter protocol enforced by
import-linter contracts. Adding a solver means writing one adapter — see
docs/ARCHITECTURE/OVERVIEW.md.
Documentation
Doc | Description |
From zero to first solve | |
Self-hosting config: | |
System design, components, data model | |
Dev setup and conventions | |
Test strategy, coverage, mutation scores | |
Incident response runbook | |
A worked mathematical formulation |
Built with
JAOT stands on the SCIP Optimization Suite (Zuse Institute Berlin) and HiGHS — full attributions in THIRD_PARTY_LICENSES.
Built solo and AI-accelerated. What you can verify rather than take on faith:
tests run against real PostgreSQL (no mocked DB), domain boundaries are enforced
by import-linter contracts, and every change is gated by lint, tests, and
security scans (bandit, pip-audit, npm audit). Details, coverage, and
mutation-test scores in Testing & Quality.
Maintained best-effort — monthly issue triage, quarterly dependency/CVE pass. Issues and focused PRs welcome; see CONTRIBUTING.md and SECURITY.md.
Citing the solvers
JAOT is powered by SCIP 10 (via PySCIPOpt) and HiGHS. As the SCIP team requests, any work that uses SCIP should acknowledge and cite it. If JAOT helps your research or product, please cite the underlying solvers:
@misc{scip10,
title = {The {SCIP} Optimization Suite 10.0},
author = {Christopher Hojny and Mathieu Besançon and Ksenia Bestuzheva and Sander Borst and João Dionísio and Johannes Ehls and Leon Eifler and Mohammed Ghannam and Ambros Gleixner and Adrian Göß and Alexander Hoen and Jacob von Holly-Ponientzietz and Rolf van der Hulst and Dominik Kamp and Thorsten Koch and Kevin Kofler and Jurgen Lentz and Marco Lübbecke and Stephen J. Maher and Paul Matti Meinhold and Gioni Mexi and Til Mohr and Erik Mühmer and Krunal Kishor Patel and Marc E. Pfetsch and Sebastian Pokutta and Chantal Reinartz Groba and Felipe Serrano and Yuji Shinano and Mark Turner and Stefan Vigerske and Matthias Walter and Dieter Weninger and Liding Xu},
year = {2025},
howpublished = {Optimization Online preprint, arXiv:2511.18580},
url = {https://arxiv.org/abs/2511.18580}
}
@article{achterberg2009scip,
title = {{SCIP}: solving constraint integer programs},
author = {Achterberg, Tobias},
journal = {Mathematical Programming Computation},
volume = {1},
number = {1},
pages = {1--41},
year = {2009},
doi = {10.1007/s12532-008-0001-1}
}
@article{huangfu2018highs,
title = {Parallelizing the dual revised simplex method},
author = {Huangfu, Qi and Hall, J. A. Julian},
journal = {Mathematical Programming Computation},
volume = {10},
number = {1},
pages = {119--142},
year = {2018},
doi = {10.1007/s12532-017-0130-5}
}License
Apache License 2.0 — see also NOTICE. Third-party license attributions are in THIRD_PARTY_LICENSES.
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