What can you do with this server?

This server provides aerodynamic simulation capabilities for flapping-wing aircraft analysis through MCP (Model Context Protocol) tools. Core capabilities: * Aerodynamic force calculation: Compute thrust, lift, and torque for wing geometries by specifying wing parameters (span, chord), flapping schedules (frequency, amplitude), and flight conditions (velocity, air density) via the pterasim.simulate method * Dual solver support: Automatically uses high-fidelity UVLM (Unsteady Vortex Lattice Method) when PteraSoftware is installed, with automatic fallback to fast analytical surrogate models if unavailable or convergence fails * Provenance tracking: Returns metadata indicating which solver was used (analytic or pterasoftware_uvlm) and performance deltas between methods (e.g., thrust/lift differences in percentages) * Parametric design sweeps: Support for varying wing geometry and flapping parameters across multiple simulations to explore design space and optimize performance * Integration with MCP workflows: Compatible with other MCP tools like ctrltest-mcp for control studies and openvsp-mcp for geometry-to-aerodynamics pipelines * Structured data outputs: Generate experiment logs (JSONL) suitable for control systems, reinforcement learning agents, regression dashboards, and machine learning training datasets * Multiple deployment options: Run as STDIO service, HTTP streamable service, FastAPI REST server, or integrated into MCP toolchains

Which integrations are available for this server?

Provides a REST API wrapper for the pterasim aerodynamics solver, enabling HTTP-based access to UVLM and surrogate aerodynamic simulations. Supports routing solver provenance metadata and aerodynamic simulation results into Grafana dashboards for visualization and monitoring.

How do I use Pterasim MCP Server?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Pterasim MCP Server calculate lift and drag for a 0.8m wingspan at 12Hz flapping" 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.

pterasim-mcp - UVLM + surrogate aerodynamics for MCP agents

TL;DR: Expose PteraSoftware's analytical/UVLM solvers through MCP so agents can request aerodynamic coefficients with provenance metadata.

What it provides

Scenario	Value
Surrogate aerodynamics	Evaluate lightweight rigid-vortex-lattice models without a GUI, returning aerodynamic coefficients as structured data.
High-fidelity UVLM	When PteraSoftware is installed, the service switches to its UVLM solver and records provenance metadata.
Controller integration	Provide consistent outputs that downstream MCP tools (e.g., `ctrltest-mcp`) can ingest for control and evidence pipelines.

Quickstart

1. Install

uv pip install "git+https://github.com/Three-Little-Birds/pterasim-mcp.git"

2. Run an analytic solve

from pterasim_mcp import PterasimInput, simulate_pterasim

request = PterasimInput(
    span_m=0.8,
    mean_chord_m=0.12,
    stroke_frequency_hz=12.0,
    stroke_amplitude_rad=0.55,
    cruise_velocity_m_s=6.0,
    air_density_kg_m3=1.225,
    cl_alpha_per_rad=6.2,
    cd0=0.03,
    planform_area_m2=0.18,
)
response = simulate_pterasim(request)
print(response.metadata["solver"])            # "analytic" or "pterasoftware_uvlm"
print(response.thrust_N, response.lift_N, response.torque_Nm)

Typical metadata payload:

```json
{
  "solver": "pterasoftware_uvlm",
  "thrust_delta_pct_vs_analytic": -4.1,
  "lift_delta_pct_vs_analytic": -2.7
}


If a Python 3.13 environment with `PteraSoftware` (≥3.2) is available, the wrapper will prefer UVLM and note the solver in the metadata. Install it inside a dedicated environment:

```bash
uv python install 3.13
uv venv .venv-pterasim --python 3.13
source .venv-pterasim/bin/activate
pip install pterasoftware==3.2.0
uv pip install "git+https://github.com/Three-Little-Birds/pterasim-mcp.git"

If the UVLM solve fails for any reason (missing binaries, convergence issues, or the current PteraSoftware regression that removes geometry.airfoil), the wrapper logs a warning and falls back to the analytic surrogate—you will see solver: "analytic" in the metadata and no delta fields. UVLM runs with thousands of timesteps can take minutes; batch analytic sweeps first and promote only promising cases to the high-fidelity environment. Analytic-only mode works on Python 3.11 without PteraSoftware. Until the UVLM API stabilizes upstream, treat the analytic path as the supported/default mode.

Run as a service

CLI (STDIO / Streamable HTTP)

uvx pterasim-mcp  # runs the MCP over stdio
# or python -m pterasim_mcp
python -m pterasim_mcp --transport streamable-http --host 0.0.0.0 --port 8000 --path /mcp

Use python -m pterasim_mcp --describe to emit metadata without starting the server.

FastAPI (REST)

uv run uvicorn pterasim_mcp.fastapi_app:create_app --factory --port 8003

python-sdk tool (STDIO / MCP)

from mcp.server.fastmcp import FastMCP
from pterasim_mcp.tool import build_tool

mcp = FastMCP("pterasim-mcp", "Wing UVLM & surrogate solver")
build_tool(mcp)

if __name__ == "__main__":
    mcp.run()

ToolHive smoke test

uvx --with 'mcp==1.20.0' python scripts/integration/run_pterasim.py
# ToolHive 2025+ defaults to Streamable HTTP; match that transport when registering
# the workload manually so IDE clients avoid the SSE 502 bug.

Agent playbook

Scenario sweeps - vary span, frequency, or flapping amplitude and log derivatives for control studies.
Solver comparison - leverage metadata to benchmark surrogate vs UVLM deltas and store the comparisons for regression dashboards.
Design flows - combine with openvsp-mcp to generate geometry + aerodynamics pipelines.

Stretch ideas

Generate JSONL experiment logs that feed directly into ctrltest-mcp or reinforcement-learning agents.
Use the metadata to route results into Grafana dashboards for solver provenance.
Auto-promote surrogate runs to UVLM once a high-fidelity environment is detected.

Accessibility & upkeep

Tests simulate solver responses; run uv run pytest before pushing.
Keep .venv-pterasim aligned with the PteraSoftware version you report in metadata.

Contributing

uv pip install --system -e .[dev]
Run uv run ruff check . and uv run pytest
Include sample metadata/CSV artefacts in PRs so reviewers can confirm provenance handling.

MIT license - see LICENSE.

Available Tools

1 tool

pterasim.simulateB

Generate aerodynamic coefficients with UVLM fallback. Supply wing geometry, flapping schedule, and timestep count. Returns forces, torques, and solver metadata. Example: {"span_m":0.8,"chord_m":0.12,"num_timesteps":180}

Inputs: request (required). Returns: lift_N, metadata, thrust_N, torque_Nm

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

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Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.4/5.0

Scored 2026-04-18 08:15 across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of confusion or overlap between tools. The tool has a clear, singular purpose focused on aerodynamic simulation with specific inputs and outputs.

Naming Consistency5/5

Since there is only one tool, naming consistency is inherently perfect. The tool name 'pterasim.simulate' follows a clear and logical pattern that would be consistent if more tools existed.

Tool Count2/5

A single tool is too few for a server named 'Pterasim MCP Server', which suggests a broader aerodynamic simulation domain. This minimal toolset limits functionality and likely leaves significant gaps in coverage.

Completeness2/5

The server appears to focus on aerodynamic simulation, but with only a simulate tool, there are obvious gaps. Missing operations might include geometry creation, parameter analysis, result visualization, or data export, making the surface incomplete for typical simulation workflows.

Maintenance

ActivityInactive

ResponsivenessOutdated data

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