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Chalkcast

by FredMichelversion 0.1.0i★ –iRuns on your computer
Registry nameio.github.FredMichel/chalkcast

Install

Overview

Description from the maker (original English)

Author, render and visually verify Manim math animations from natural language

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "chalkcast": {
      "command": "uvx",
      "args": [
        "chalkcast"
      ]
    }
  }
}

Run this in your terminal

claude mcp add chalkcast -- uvx chalkcast

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "chalkcast": {
      "command": "uvx",
      "args": [
        "chalkcast"
      ]
    }
  }
}

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.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "chalkcast": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "chalkcast"
      ]
    }
  }
}

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.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

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

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account FredMichel, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

  • More stars than 0% of the other 32683 servers in this category
  • Published to the registry more recently than 74% of the other 32683 servers in this category
  • Basic checks equal to or better than 100% of the other 32683 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Chalkcast safe?

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.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

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).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.0) reached the registry 12 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, MCP Server Autocad Lisp Helper

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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Runs on your computer★ 440MITUpdated 14 months ago
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Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

Runs in the cloud★ 211Updated 2 days ago
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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Chalkcast

Describe a concept. Get a *verified* animated explainer.

Chalkcast is an MCP server that lets an agent author, render, and visually verify Manim animations — so the output is a video that's actually correct on screen, not a plausible script that renders wrong.

One continuous animation, authored, verified and rendered entirely through the MCP server (passed on the first render): a Fourier series grows harmonic by harmonic into a square wave, and the Gibbs overshoot (the true one, ≈ 1.179) stays at the edges. Reproduce it with python examples/showcase.py .


Who it's for

  • Educators & students — turn "explain the Fourier series" into a clean animated explainer, without learning Manim or writing LaTeX by hand.
  • STEM content creators — produce 3Blue1Brown-style animations in minutes instead of hours of coding and debugging renders.
  • Agents & tooling — a typed, verifiable MCP surface an AI can drive end to end, with a verdict it can act on instead of a video it can't inspect.

Why another Manim MCP?

Existing Manim MCP servers give the agent one tool: *run this code, return the video.* The agent writes Manim blind and hopes — but Manim is unforgiving (a LaTeX typo, an off-screen label, two terms overlapping, a transition firing before its target exists). The video "renders" and is still wrong, and the agent has no way to know.

Chalkcast gives the agent eyes and a correction loop:

author scene ──► render ──► inspect (verification lanes) ──► verdict

▲ │ └──────────────── fix obligations ◄──── needs_fix ─────────┘

Every render returns evidence (the artifact + sampled frames) and a structured verdict. Problems become obligations that block export until resolved. See docs/SPEC.md for the full design.

Install

One command (Claude Code; needs uv):

claude mcp add chalkcast -- uvx --python 3.12 --from "chalkcast[render]" chalkcast serve

--python 3.12 matters: Chalkcast needs Python ≥ 3.11, and uv downloads one if the system Python is older (macOS ships 3.9). Then just ask in natural language.

Or with pip, into any Python ≥ 3.11 environment:

pip install "chalkcast[render]"

claude mcp add chalkcast -- chalkcast serve

[render] brings Manim and a bundled ffmpeg. Equations also need a LaTeX distribution. chalkcast doctor checks everything and prints the exact fix command for your OS:

chalkcast doctor               # Manim / LaTeX / dvisvgm / physics.sty / ffmpeg

chalkcast demo # builds + verifies + renders the demo scene

macOS: pycairo (pulled in by Manim) has no macOS wheel and builds from

source, so it needs brew install cairo pkg-config first. On a machine where

Manim's native deps were built by hand, see scripts/dev-env.sh and

scripts/serve.sh.

What the agent gets

GroupTools
Sessionopen_project · inspect_project · close_project
Authoringadd_scene · add_equation · add_plot · add_area · add_riemann · add_text · add_shape · add_parameter · add_step
Editingupdate_object · remove_object (cascade) · remove_step
Verificationinspect_render (measured, no video) · fix_scene (auto-repair)
Renderingrender_scene · render_frame
Exportexport_gif (blocked while error obligations remain)
Discoveryinspect_environment
  • Continuous animation: add_parameter("t"), use t in plot expressions or shape anchors (at_x="t"), then add_step("tween", param="t", value=6.28). Curves, areas and anchored dots follow it frame by frame. with_previous=True plays a step at the same time as the one before.
  • Fail fast, nothing implicit: expressions are validated when authored (an AST whitelist: x, math functions, np. , declared parameters). Colors, ranges, step targets, timeline order, and arguments that don't apply to a shape or step are rejected with a message saying what to do. Edits are all-or-nothing. Side effects (cascade removals, regrouped steps, redeclared parameters, default ranges) are reported in the result. export_gif refuses a render that predates the latest edit. Thirty-two independent audits (22, then 17, 14, 8, 4, 9, 4, 7, 5, 5, 7, 8, 5, 8, 7, 7, 10, 4, 5, 5, 8, 7, 7, 5, 7, 9, 7, 4, 10, 9, 7 and 6 findings, the last with no high-severity one; in the last thirteen, 9, 10, 8, 10, 9, 9, 9, 9, 9, 10, 8, 9 and 10 of 10 fresh explainers reached a correct GIF within two renders, and in the last one every verdict matched its frames; the last twenty-three built realistic explainers end to end and compared verdicts with frames, one of them over the real MCP stdio protocol) are pinned one test per finding in tests/test_ergonomics.py.
  • Faithful plots: dense sampling, curves split at jumps and poles (no invented risers or overshoot), clipping to the axes. A *fidelity* lane measures the drawn curve against the true function. Areas and Riemann sums are refused when they could not show the integral (function undefined or off the axes, y = 0 not visible).
How verification works

inspect_render builds the real Manim objects without encoding video (1–3 s) and measures them: exact bounding boxes, curve and axis ink, the drawn-vs-true error of every graph, and a strict LaTeX compile (Manim's batch mode silently "recovers" from LaTeX errors). Lanes: framing · collision · legibility · timing · compile · fidelity · range. fix_scene repairs the mechanical problems (moves, rescales, resampling) and leaves the rest as obligations for the agent.

Measured on labelled scenes (chalkcast bench, chalkcast bench --holdout N). Each held-out set was written and labelled (and checked frame by frame) by an author who never read the verifier. The first run of each set is the honest out-of-sample figure:

SetCasesFirst run: detection / false positivesBugs that run exposed
Held-out 139 (24 defective)95.8 % / 7.7 %over-strict safe margin; aliasing taken for a jump
Held-out 247 (31 defective)90.3 % / 12.8 %estimated noise after a compile error; straight arrows and box sides sampled only at their ends; equation size judged by box height
Held-out 344 (26 defective)96.2 % / 0 %an arrow's shaft was never sampled (only its tip)
Held-out 446 (25 defective)96.0 % / 0 %a label written *during* a zoom-in was excused as an intentional crop

After each set's bugs were fixed, every set (and the 40-case development set) scores 100 % / 0 %. From then on they are regression tests (tests/test_measured.py), not independent measures. Held-out 3 was written after the fixes for sets 1 and 2, and held-out 4 (continuous tweens, simultaneous beats, transforms, camera moves) after eleven ergonomics audits; each was run once, untouched. Held-out 4 is the current out-of-sample estimate.

Example (what the agent does)

open_project(theme="chalkboard")

add_scene(title="Area under a parabola") add_text("Area under y = x^2", y=3) add_plot(functions=[{"expr":"x**2","color":"YELLOW"}], x_range=[0,2,1], y=-0.8) add_equation(r"\int_0^1 x^2 \, dx = \tfrac{1}{3}", y=2) add_step("write", targets=["text_1"]) add_step("write", targets=["plot_1"]) add_step("write", targets=["equation_1"]) add_step("highlight", targets=["equation_1"]) inspect_render() # -> measured verdict in ~2 s, no video fix_scene() # -> repairs framing / collision / fidelity if needed render_scene() # -> video + frames + verdict + obligations export_gif() # -> shareable GIF (blocked if error obligations remain)

Status

Alpha. Scientific correctness is tested numerically (tests/test_science.py): shaded areas equal their integrals, Riemann rectangles their sum, drawn curves their function (including sin 30x), and the Gibbs peak its theoretical value. On PyPI and the MCP Registry (io.github.FredMichel/chalkcast). Vision-based intent verification is next (see docs/SPEC.md).

License

MIT.

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