Open-source MCP servers connecting AI agents to real aerospace tools: aerodynamics, structures, propulsion, and mission design. No approximations. The agent runs the same code you would.
oas --
ocp --
pyc --
~/projects/e190-wing
$claude mcp add --transport http oas https://mcp.lakesideai.dev/oas/mcp[ok] added server "oas" (keycloak: oidc)$claude# minimize CD at CL=0.5 on a 28 m-span wing,# cap struct mass at 3000 kg, ny=21, nx=3-> create_surface(name="wing", span=28.0, ...)-> run_optimization(dvs=[twist_cp, thickness_cp, alpha])run_id : run-20260414T145425-65b0174econverged : yes(28 iters, SLSQP)CD : 0.02213
CL : 0.500
L/D : 22.60
struct_mass: 3000 kg
provenance : /oas/runs/.../dag.html
Assembling a design study typically means hand-wiring scripts, moving files, and bug-checking glue. Engineers spend more time on plumbing than concepts.
// the shift
Off-the-shelf, scriptable MDAO infrastructure is changing that. Engineers can direct and review concept work instead of wrestling files and configs.
mdao --concept-design
$mdao --state --concept-design- per-tool python scripts; setup takes hours- analysis loops hand-written, brittle to re-run- provenance ad-hoc if anything- cross-tool composition requires bespoke glue- agents can't author; engineers copy-paste examples+ plans, not scripts+ any tool behind one protocol+ every run records a provenance graph+ composition is a plan primitive+ agents author, diff, and re-run
The Hangar is where these tools live together. Each MDAO tool is defined, tested, and demonstrated in its own package; composition happens through a shared plan format. Every run carries a provenance graph, so engineers lead review instead of chasing files.
01 / compose
One plan. Any tools. One run.
hangar-omd is a YAML-first OpenMDAO plan runner. Describe a trade study or MDO problem once; get a reproducible run with recordings, an N2 diagram, a provenance graph, and plots. Today, components from OAS, OCP, and pyCycle compose inside one plan.
A plan describes an OpenMDAO problem without the boilerplate: components, solvers, DVs, constraints, optimizer. Check it into git, re-run it anywhere, diff it against a prior version. The same plan is agent-authorable and human-readable.
cross-tool composeOCP mission + OAS VLM drag + pyCycle surrogate propulsion in one plan
lane patternsame math three ways: direct Python (A), plan YAML (B), agent-interactive (C)
plots ship with factoriesplanform / lift / von-mises for OAS; station properties for pyCycle
export to Pythonomd-cli export emits a standalone OpenMDAO script for deep debugging
$omd-cli run plan.yaml --mode optimize$omd-cli plot <run_id> --type all$omd-cli provenance brelje-fuel-mdo --format html
02 / servers
Three independent tools. One protocol. One endpoint each.
Each server wraps a proven open-source analysis tool built on OpenMDAO. Tool calls return structured envelopes with physics validation, artifact references, and a run_id for provenance.
hangar/oasopenaerostruct
--
Aerostructural analysis and optimization of lifting surfaces. Couples vortex-lattice aerodynamics with finite-element structures for wing planform design and trade studies.
Aircraft conceptual design and mission analysis. Fuel burn, takeoff performance, and battery state-of-charge across full flight profiles for conventional and hybrid-electric architectures.
Gas turbine engine cycle analysis. Design-point sizing and off-design performance with full thermodynamic station modeling. Turbojet today; HBTF, turboshaft, afterburning, and mixed-flow turbofan archetypes in progress.
noteAll servers use Keycloak OIDC. First connection opens a browser login. Google sign-in supported; credentials available from an admin.
omdhangar/omd MCP is in progress. It exposes unified OpenMDAO problems that compose multiple tools with gradients connected end-to-end. Available today via omd-cli; MCP server coming soon.
04 / about
What the hangar is.
march 2026 // lakeside ai
The Hangar wraps established aerospace tools as MCP servers so AI agents can call them directly. Agents compute aerodynamic coefficients, structural loads, fuel burn, engine performance, and full mission profiles using the same code an engineer would run locally.
##the problem
Tools like OpenAeroStruct, OpenConcept, and pyCycle are capable but slow to wield. Building OpenMDAO problems, defining meshes, connecting subsystems, setting solver options, and extracting results takes domain fluency and hours per iteration. Early-stage design exploration is expensive when it should be cheap.
##what mcp gives us
MCP (Model Context Protocol) is an open standard for exposing tools to AI applications with structured input and output. The Hangar uses it to present each analysis tool as a set of callable MCP operations with input validation, physics checks, and versioned result envelopes.
##what an agent can do
explore design space: "fuel burn vs aspect ratio from 9 to 14". Agent creates the surface, runs analyses at each AR, reports the trend.
compare architectures: "turboprop vs series-hybrid, 500 nmi regional mission". Computed, not estimated.
compose across tools: size an engine with pyCycle, feed TSFC into an OCP mission, optimize the wing with OAS.
optimize with gradients: define DVs, set constraints, run SNOPT or SLSQP, interpret convergence.
##what you get back
Every tool call returns a versioned envelope: results, physics validation (convergence, bounds, sanity checks), telemetry, and a run_id for provenance. Every run produces an N2 diagram, an OpenMDAO recorder .sql, and a PROV-Agent graph entry. The agent handles setup boilerplate. You stay on the engineering.
brelje-fuel-mdo-lane-c / provenance
every run records provenance, automatically
05 / case-studies
Reproducing peer-reviewed MDAO.
End-to-end engineering workflows executed by agents through The Hangar. Each study ships with plots, provenance graph, N2 diagram, and the decisions log.
The Hangar wraps these open-source projects. If you use them in published work, cite the original authors.
@article{jasa2018oas,
author = {Jasa, J. P. and Hwang, J. T. and Martins, J. R. R. A.},
title = {Open-source coupled aerostructural optimization using Python},
journal = {Struct. Multidiscip. Optim.}, year = {2018},
volume = {57}, pages = {1815--1827}
}
doigithub
@inproceedings{brelje2018ocp,
author = {Brelje, B. J. and Martins, J. R. R. A.},
title = {Development of a Conceptual Design Model for Aircraft Electric Propulsion with Efficient Gradients},
booktitle = {AIAA/IEEE Electric Aircraft Technologies Symposium},
year = {2018}, number = {AIAA 2018-4979}
}
doigithub
@article{hendricks2019pyc,
author = {Hendricks, E. S. and Gray, J. S.},
title = {pyCycle: A Tool for Efficient Optimization of Gas Turbine Engine Cycles},
journal = {Aerospace}, year = {2019},
volume = {6}, number = {87}
}
doigithub
07 / contribute
Add your tool today.
The Hangar supports new analysis tools beyond the initial three. The SDK provides shared infrastructure for response envelopes, provenance tracking, validation, and artifact storage, so you can focus on the tool-specific logic.
