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tigl-mcp

An MCP for TiGL

Install / Use

claude mcp add cmudrc -- npx -y github:cmudrc/tigl-mcp

If the server publishes to npm under a different name, use that package instead — check the repo README.

About this skill
🔌

MCP Server

Model Context Protocol server

Quality Score

67/100

Supported Platforms

Claude Code
Claude Desktop

Our assessment of tigl-mcp

tigl-mcp scores 67/100 on our quality scale, 562nd of 1,283 Development & Engineering skills we index (top 44%).

Its MCP Server is 5.5 KB long, well organised into 14 sections with 5 code examples: a solid amount of guidance for an agent.

It has 3 GitHub stars, so there is little community track record yet; judge it on its content.

Substance
26/30
Structure
20/20
Description
4/15
Adoption
3/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 8 days ago, so tigl-mcp is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 82/100, with 3 cautions from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

tigl-mcp compared with similar skills

All 4 of these similar skills score higher than tigl-mcp; compare them before choosing.

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Frequently asked questions

How do I install tigl-mcp?
Run claude mcp add cmudrc -- npx -y github:cmudrc/tigl-mcp. The install tabs above show the steps for each supported agent.
Which AI agents does tigl-mcp work with?
It is written for Claude Code and Claude Desktop, as a MCP Server file. Other agents that read the same format can often use it too.
Is tigl-mcp safe to use?
It is MIT-licensed and scores 82/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is tigl-mcp still maintained?
The repository was last updated 8 days ago, so tigl-mcp is actively maintained.

tigl-mcp

CI Docs Examples

tigl-mcp is a lightweight Model Context Protocol server for CPACS-oriented TiGL workflows. The current implementation focuses on deterministic, JSON-friendly tooling backed by stubbed CPACS/TiGL behavior so local development, tests, and docs stay stable without native geometry runtimes.

Overview

The project currently provides:

  • A FastMCP-powered server with stdio and HTTP-compatible transports
  • A curated set of CPACS lifecycle, inspection, export, sampling, and parameter tools
  • Pydantic-backed tool validation with structured MCP error payloads
  • Deterministic CPACS/TiGL stand-ins for stable local development and CI

Quickstart

Requires Python 3.12+.

python3 -m venv .venv
source .venv/bin/activate
make dev
make test
make ci

Start the server over stdio:

tigl-mcp --transport stdio

Inspect the non-blocking HTTP transport configuration example:

PYTHONPATH=src python3 examples/server/http_launch_config.py

Examples

The examples are deterministic and aligned with the current stub-backed implementation.

Docs

Build the docs locally with:

make docs

Current Capability Boundaries

This is the authoritative statement of which paths run real TiGL and which do not. Nothing here estimates geometry.

Real TiGL, always. CAD export (export_configuration_cad, STEP and IGES) and mesh export (export_component_mesh) drive the real TiGL library, either through native tigl3/tixi3 bindings or through the tigl-mcp:dev Docker image. The route actually taken is recorded in CPACS as stepExportSource, so exported geometry is traceable. On the D150 this produces a 3.2 MB STEP file and per-component surface meshes of a few thousand triangles.

Read from the CPACS file, so always available. Component UIDs, names, symmetry flags, wing/fuselage/rotor/engine counts, per-component section and segment counts, and the reference area and length. These are values the file states directly.

Requires a real kernel, and reports GeometryUnavailable without one. Bounding boxes, wing span and area, aspect ratio, MAC, fuselage length, surface sampling, and plane/component intersection. Deriving any of these means evaluating profile geometry through the CPACS positioning and transformation chain, which is what TiGL is for. When no kernel is reachable these tools raise a structured error naming the missing dependency. They do not return an estimate.

Until 2026-08-15 several of these returned values derived from a component's index in an array — a wing's bounding box was literally its position in the list. Those were removed, along with a synthetic single-triangle mesh and a CAD "export" that returned the CPACS text relabelled. If you are reading older notes or output that mention stub geometry, they predate this.

Shared-CPACS Integration

This MCP includes a CPACS adapter (src/tigl_mcp/cpacs_adapter.py) that bridges TiGL to the shared-CPACS aircraft analysis pipeline.

What it does

The adapter reads CPACS geometry (wings, fuselages, profiles) and writes analysis results — component counts, per-component section and segment counts, and STEP export metadata — into //analysisResults/tigl. A boundingBox element is written only when a real geometry kernel supplied one, and is omitted otherwise.

| Direction | XPath | |-----------|-------| | Reads | .//vehicles/aircraft/model, .//vehicles/profiles | | Writes | .//vehicles/aircraft/model/analysisResults/tigl |

Running as part of the pipeline

# As part of the full 4-MCP pipeline (with SU2, pyCycle, Mission)
python pipeline/shared_cpacs_orchestrator.py D150_v30.xml --mcps tigl su2 pycycle mission

# TiGL only
python pipeline/shared_cpacs_orchestrator.py D150_v30.xml --mcps tigl

See cmudrc/aircraft-analysis for full pipeline documentation, versioning details, and installation instructions.

Related MCP servers

| MCP | Repository | |-----|-----------| | SU2 (CFD aerodynamics) | cmudrc/su2-mcp | | pyCycle (engine cycle) | cmudrc/pycycle-mcp | | Mission, segment/Breguet | cmudrc/nseg-mcp | | Mission, trajectory-level | cmudrc/aviary-cpacs-mcp |

Contributing

Contribution guidelines live in CONTRIBUTING.md.

Maintainers

Mayank Dixit (@Kugel-Blitz-13), Carnegie Mellon University — mayankd@cmu.edu Christopher McComb, Carnegie Mellon University — Design Research Collective

Related Skills

View on GitHub
GitHub Stars3
CategoryDevelopment
Updated8d ago
Forks1

Languages

Python

Trust signals

82/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

3 low