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Comparison

canvas-apps vs Kalshi Prediction Markets

canvas-apps is a plugin bundle for AI coding tools published by microsoft. Marketplaces we read that list it: 1 (Copilot CLI: github/copilot-plugins). Kalshi Prediction Markets is an MCP server published by cejor6. Marketplaces we read that list it: 2 (Official MCP Registry, PyPI (MCP servers)). As of our marketplace pass on 2026-10-25. This pair was picked by a reader, not by our comparison rule, so search engines are asked not to index the page.

Where each is published

Where each is published
Marketplacecanvas-appsKalshi Prediction Markets
Copilot CLI: github/copilot-plugins Listed
Version: not stated
Forks (GitHub): 211
Stars (GitHub): 1 053
Not marked · First seen
Not listed
Official MCP Registry Not listed Listed
Version: 0.1.8
Downloads/month (PyPI): 702
Downloads/week (PyPI): 186
Forks (GitHub): 3
Stars (GitHub): 3
Not marked · First seen
PyPI (MCP servers) Not listed Listed
Version: 0.3.4
Downloads/month (PyPI): 702
Downloads/week (PyPI): 186
Forks (GitHub): 3
Stars (GitHub): 3
Not marked · First seen

Each figure is the marketplace's own and is never added up across marketplaces. A marketplace that publishes no figure shows none: no number is not zero.

The facts side by side

canvas-apps vs Kalshi Prediction Markets
Factcanvas-appsKalshi Prediction Markets
Marketplaces that list it (differs)1 (worse)2 (better)
Stars (GitHub) (differs)1 053 (better)3 (worse)
Forks (GitHub) (differs)211 (better)3 (worse)
Last releaseNot publishedNot published
Where it runs (differs)Installs as a plugin bundleOn your machine (kalshi-mcp-server)
Transport (differs)Not statedstdio
Tools it declaresNot publishedNot published
Environment variables it asks for (differs)Not published6, secrets among them: 1
LicenceMITMIT
Repository (differs)github.com/microsoft/power-platform-skillsgithub.com/cejor6/kalshi-mcp-server
Changes in the last 7 days00
First seen2026-10-112026-10-11

Lines where the two differ have a tinted background and bold values. Where the data itself says which value is better (more marketplaces, a larger figure of the same marketplace, a fresher release, a verified mark on a marketplace that lists both), the better one is also marked green with ▲ and the worse one red with ▼. Where it runs, the transport, the tools, the settings, hooks, the bundle's parts, the licence and the week's changes are marked only as different: more or fewer is not better in itself.

What each says it does

canvas-apps

In its own words, on Copilot CLI: github/copilot-plugins:

Build Power Apps Canvas Apps using the Canvas Authoring MCP server.

Kalshi Prediction Markets

In its own words, on Official MCP Registry:

MCP server for Kalshi prediction markets: native RSA-PSS auth, rate limiting, demo/prod safety.

How to run each

canvas-apps

No data for an automatic command: what its marketplaces declare does not say unambiguously how to connect it.

Kalshi Prediction Markets As a local MCP server: PyPI package

Claude Code
claude mcp add --env "KALSHI_API_KEY_ID=<YOUR_KALSHI_API_KEY_ID>" --env "KALSHI_PRIVATE_KEY_PEM=<YOUR_KALSHI_PRIVATE_KEY_PEM>" --transport stdio kalshi-mcp-server -- uvx kalshi-mcp-server

Requires: KALSHI_API_KEY_ID, KALSHI_PRIVATE_KEY_PEM (secret)

Commands built only from what the marketplaces declare; the full set for every client is on each card.

Tools each declares

Neither one's marketplaces publish a list of tools, so there is nothing to compare.

Settings each asks for

The marketplaces of canvas-apps publish no settings it asks for, so we do not compare settings.

What it can do: canvas-apps · What it can do: Kalshi Prediction Markets · Compare two other plugins

Data comes from the public marketplace file https://github.com/github/copilot-plugins/blob/HEAD/.github/plugin/marketplace.json. Data comes from the Official MCP Registry (registry.modelcontextprotocol.io), CC0 1.0. Package data comes from the Python Package Index (pypi.org). Figures come from our own regular passes over the marketplaces, last read on . We publish no contact details for plugin publishers.

Where the data comes from: how the catalog is built