INDIVIDUAL MCP TOOL

get_scores

Headline scores for every model RealRun tested on a task, best first, with cost tier and the metric definition.

baseledgerip.comnone authenticationAvailability not checked

LIVE ENDPOINT

https://realrun.baseledgerip.com/mcp

No auth detected

Connect to this endpoint to inspect the live schema for get_scores and invoke it with your own arguments.

{}

Inferred read-only · medium confidence · heuristic, not a guarantee.

No write-capable action terms were found; this is not proof that invocation has no side effects.

CONNECT WITH APPROVAL

Review this server and its permissions before adding it. Secret placeholders must be set locally.

Codex

~/.codex/config.toml

[mcp_servers.realrun]
url = "https://realrun.baseledgerip.com/mcp"
enabled = true

Claude Code

.mcp.json

{
  "mcpServers": {
    "realrun": {
      "type": "http",
      "url": "https://realrun.baseledgerip.com/mcp"
    }
  }
}

Claude Desktop

Settings → Connectors → Add custom connector

Name: realrun
Remote MCP URL: https://realrun.baseledgerip.com/mcp

Add this remote URL as a custom connector in Claude Desktop. Availability depends on the user plan and workspace policy.

Cursor

.cursor/mcp.json

{
  "mcpServers": {
    "realrun": {
      "url": "https://realrun.baseledgerip.com/mcp"
    }
  }
}

Visual Studio Code

.vscode/mcp.json

Add to Visual Studio Code

{
  "servers": {
    "realrun": {
      "type": "http",
      "url": "https://realrun.baseledgerip.com/mcp"
    }
  }
}

Generic MCP

Client-specific MCP configuration

{
  "name": "realrun",
  "transport": "streamable-http",
  "url": "https://realrun.baseledgerip.com/mcp"
}

MCP Inspector

Run the official MCP Inspector locally and enter the indexed Streamable HTTP endpoint.

list_tasks — List the everyday tasks RealRun has tested AI models on, with item counts, test dates, results pages and playbook prices.
recommend_model — Recommend which AI model to use for a task given a priority: 'accuracy', 'cost' or 'balanced'.
get_warnings — Models RealRun flagged as not recommended or needing caution on a task (scam failures, invented data, inconsistency).