Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Description from the maker (original English)
Tiny sphere-physics engine for three.js games: spawn, step, raycast, InstancedMesh-ready positions
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You need
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Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"flit": {
"command": "npx",
"args": [
"-y",
"flit-physics"
]
}
}
}Run this in your terminal
claude mcp add flit -- npx -y flit-physicsPaste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"flit": {
"command": "npx",
"args": [
"-y",
"flit-physics"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"flit": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"flit-physics"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
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Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026
The registry name was verified with the GitHub account brashler, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
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It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (0.2.2) reached the registry 2 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
In the same category: Spec Driven Development, Remote Desktop Commander, TF Rooms
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
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Flit is a tiny little physics engine for 3js. It doesn't do much yet! but I believe!
Spheres, collisions, raycasts, gravity, and a spatial-hash broadphase — nothing more, on purpose. All simulation state lives in flat Float32Arrays (structure-of-arrays), so the CPU path doubles as the reference implementation for a future WebGPU compute backend: same buffers, same kernels, no re-architecting.
index: -1).THIRD_PARTY_NOTICES.md for provenance and licenses.Building a little three.js game or demo? Two ways in:
skills/flit/SKILL.md — copy the skills/flit/ directory into your agent's skills path (e.g. .claude/skills/, ~/.code_puppy/skills/). It carries the 30-second integration recipe, the MCP option, measured performance envelope, and contributor rules.npm run mcp, or point your client at it:{
"mcpServers": { "flit": { "command": "npx", "args": ["vite-node", "mcp/server.ts"], "cwd": " " } } }
Tools: flit_info, flit_reset (gravity/restitution/ground/bounds/ settleSpeed/heightfield), flit_spawn (rain/explosion/grid/fountain presets), flit_add_particles, flit_step, flit_state, flit_raycast — step/state return flat xyz positions shaped for InstancedMesh syncing plus settledCount, so you can watch a pile go quiet.
npm i flit-physics
import { World } from 'flit-physics';
const world = new World({ restitution: 0.4 }); // gravity and a floor at y=0 included
const ball = world.addParticle({ position: [0, 10, 0], radius: 0.5, mass: 1 });
// fixed timestep, e.g. from your rAF loop world.step(1 / 60);
// sync to three.js: positions is a live Float32Array, 3 floats per particle mesh.position.set( world.positions[ball * 3], world.positions[ball * 3 + 1], world.positions[ball * 3 + 2], );
Terrain and sleep are one option each:
import { Heightfield, World } from 'flit-physics';
const terrain = new Heightfield({ rows: 64, cols: 64, cellSize: 1, heights }); const world = new World({ groundY: null, heightfield: terrain }); // bodies collide with the terrain and settle on it; // world.raycast(...) reports terrain hits as index -1
// sleep is on by default (settleSpeed 0.01 m/s); opt out per world: const wideAwake = new World({ settleSpeed: 0 });
npm install
npm test # vitest npm run build # tsc -> dist/ npm run bench # broadphase + full-step micro-benchmarks npm run demo # three.js demo scene (vite dev server)
Deterministic seeds; numbers from a local dev machine, recorded at commit time (see commit messages for the full series, including rejected designs).
bench/broadphase.bench.ts — broadphase only, N=4096: xor-hash 3.4–3.7 ms (444 pairs, ~90% collision bloat) → Morton keys with ordered probing 3.5–3.7 ms (234 pairs, exact).bench/world.bench.ts — full World.step, N=1024: xor-hash 0.85 ms/step → Morton ordered-probing 0.83 ms/step → 0.93 ms/step with the sequential-impulse velocity solver (4 iterations + LUT friction). Exact keys, real contacts, +11%.bench/scaling.bench.ts — two regimes. Fixed box (density rises): pairs scale ~N² from crowding physics. Scaled box (constant *spawn* density): flat O(N) ≈ 1.2 ms per 1000 through N=8000 while bodies are scattered. With gravity on, everything rains into a dense floor pile over ~2-4s (WARMUP=240 to reproduce) — and since 0.3.0 the pile SETTLES: 34.2 → 9.2 ms/step at N=8000 (3.7x, inside the 60fps budget) as bodies fall asleep bottom-up. Settled bodies are skipped by integration, planes, and mutual contacts until a >1 m/s impact wakes them. (docs/issues/001 — resolved; brief kept for the analysis and the remaining ideas: warm starting, adaptive iterations.)bench/raycast.bench.ts — 2000 seeded rays at N=4096: 63–68 µs/ray (vs ~160 µs for a brute-force O(N) oracle at the same density; the grid walk pulls further ahead as N grows).bench/morton-libs.bench.ts — codec bake-off vs npm libs (npm run bench:libs): ours 3.8 ns/encode, fast-morton MB 26.1, fast-morton LUT 43.8, @thi.ng/morton 539.9. In-house wins; the libs stay as devDependencies purely so the bake-off stays runnable.bench/threaded.bench.ts — WorkerWorld over a real worker thread (SAB ping-pong): kick() costs 0.014 ms on the main thread; with a 4 ms render workload a frame costs 8.84 ms pipelined vs 14.96 ms serial at N=8192 (113 fps vs 67). At N=1024 the 0.8 ms step hides entirely behind render (4.03 vs 4.80 ms).npm run demo, 220 balls in a box (?terrain for heightfield hills, ?n=8000 for load testing)WorkerWorld: kick/waitForUpdate, SAB ping-pong zero-copy rendering3173a6f for the analysisMIT — see LICENSE. Third-party portions and their licenses are listed in THIRD_PARTY_NOTICES.md.