SwingFactor
ai.swingfactorv1.0.0Updated Oct 3, 2026
Work a decision through SwingFactor’s method: build and challenge a model of what could change it.
Overview
AI-generated overview
SwingFactor lets an assistant build and challenge a model of what could change a decision, using its structured method.
- What it does
- SwingFactor exposes a remote MCP endpoint that works a decision through the provider's own method: building a model of the factors that could change the outcome and then challenging that model. The manifest lists no tools, so the exact operations are not documented here. It is a hosted streamable HTTP service rather than a local package.
- When to use it
- Consider it when you want an assistant to reason about a decision by explicitly modelling what could shift it, rather than just listing pros and cons. It suits exploratory or strategic questions where the assumptions behind a choice matter.
- Requirements
- A remote MCP client that supports streamable HTTP, pointed at the provider's hosted endpoint. No packages, environment variables, headers, or authentication are declared in the manifest, and no account setup is described.
Before you install
The manifest declares no authentication, so it is unclear what data the service receives or retains. The tool list is not published, so you cannot see in advance what the assistant will be able to do. Treat decision inputs as information sent to a third party and avoid sharing confidential material until the provider's data handling is clear.
Installation
In SourceWeft
- Open SwingFactor in the dashboard and add it to a workspace.
- Enable the server for the chats that should use its tools.
Web executable via Streamable HTTP. Remote servers run from the web runtime once configured in a workspace.
Other MCP clients
Add this to your client's mcpServers config.
{
"mcpServers": {
"mcp": {
"type": "http",
"url": "https://mcp.swingfactor.ai/mcp"
}
}
}Tools
0Tool metadata has not been indexed yet.
Version history
1- v1.0.0LatestOct 3, 2026


