tigl-mcp
Provides MCP tools for CPACS-oriented TiGL workflows, enabling lifecycle management, inspection, export, and parameter manipulation of aircraft geometry models without native geometry runtimes.
README
tigl-mcp
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.
- Examples index:
examples/README.md - Tool discovery:
examples/client/tool_discovery.py - Session lifecycle:
examples/cpacs/session_lifecycle.py - Export snapshot:
examples/cpacs/export_snapshot.py
Docs
- Docs source:
docs/index.rst - Published docs: https://cmudrc.github.io/tigl-mcp/
Build the docs locally with:
make docs
Current Capability Boundaries
- The default tests and examples target the deterministic stand-ins in
tigl_mcp.cpacs_stubs. - Tool names, schemas, and JSON payload shapes are stable.
- Geometry values are intentionally simplified; they reflect the current stub contract rather than full native TiGL fidelity.
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, bounding boxes, and STEP export metadata
— into //analysisResults/tigl.
| 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 (trajectory/fuel) | cmudrc/mission-mcp |
Contributing
Contribution guidelines live in CONTRIBUTING.md.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.