mcpField
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waterfallbridge

waterfallbridge

Local
github.com/Icelemon27Data & Analytics
Repository

About waterfallbridge

Explain why any KPI or rate metric changed: bridge/variance analysis by driver, from CSV/Excel

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
  "mcpServers": {
    "waterfallbridge": {
      "command": "npx",
      "args": [
        "-y",
        "@waterfallbridge/mcp"
      ],
      "env": {
        "WB_DEFAULT_URL": "<WB_DEFAULT_URL>",
        "WB_HEADLESS": "<WB_HEADLESS>"
      }
    }
  }
}
Run commandnpx -y @waterfallbridge/mcp
Environment variables to set
WB_DEFAULT_URLWaterfallBridge app URL that open_app uses when no url is passed.
WB_HEADLESSRun the automation Chromium headless (true) or visible (false).

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npm@waterfallbridge/mcp1.2.4

Tools

51

Saved tool list · updated 2w ago

ParameterTypeRequiredDescription
urlstringNoWaterfallBridge app URL. Defaults to the configured WB_DEFAULT_URL (http://127.0.0.1:5173/app/ in dev, https://waterfallbridge.com/app/ in prod).
emailstringNoOptional. When provided alongside a previously saved session for the same URL+email, the saved session is restored instead of the most recently used one. Credentials are NOT required here.
login_timeout_msintegerNoHow long (milliseconds) to wait for the user to finish signing in before timing out. Default 300000 (5 min). Max 1800000 (30 min).
wait_for_manual_loginbooleanNoWhen no saved session exists and this is true (default), the tool opens a visible Chromium window on the sign-in page and waits for the user to authenticate themselves. The AI MUST tell the user to complete sign-in in that window. Set to false to keep the legacy behaviour of returning { needsLogin: true } immediately — useful for headless or CI runs.

Details

The maintainer has not added details yet. Check the README in the repository.

Comments

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An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.