Asked Thrice

Measure whether AI assistants actually recommend a brand, from measured answers.

What does this entry provide?

Asked Thrice is an MCP server entry in ChatHome Skills, sourced from Official MCP Registry and scored 73/100. As a Model Context Protocol server, it exposes tools that MCP-compatible LLM clients such as Claude, Cursor, and Copilot can call directly, extending an agent's reach beyond its built-in capabilities. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding Asked Thrice to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.

Snapshot facts

Source: Official MCP Registry
Quality: 73/100
Install method: streamable-http
Repo: nahuelsoria/llmaudit-mcp
Repo updated: Aug 30, 2026

Install command

mcp add com.askedthrice/askedthrice https://mcp.askedthrice.com/mcp

Install method: streamable-http

Review the upstream source before running this command.

GitHub project Homepage

Quality

Repo stars

Repo updated

Aug 30, 2026

How is this score calculated?

Weighted across six dimensions, scored 0-100.
Provenance (25): listed in an official or curated registry.
Installability (25): a runnable install command beats a declared transport, which beats a source checkout.
Maintenance (20): recency of the last push; archived entries score zero here.
Adoption (20): repository stars on a log curve.
Documentation (10) and description quality (10): parsed manifest or SKILL.md frontmatter, and a real description rather than a placeholder.

MCP manifest

{
  "_meta": {
    "io.modelcontextprotocol.registry/official": {
      "status": "active",
      "isLatest": true,
      "updatedAt": "2026-08-30T18:47:42.738174Z",
      "publishedAt": "2026-08-30T18:47:42.738174Z",
      "statusChangedAt": "2026-08-30T18:47:42.738174Z"
    }
  },
  "server": {
    "name": "com.askedthrice/askedthrice",
    "title": "Asked Thrice",
    "$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
    "remotes": [
      {
        "url": "https://mcp.askedthrice.com/mcp",
        "type": "streamable-http"
      }
    ],
    "version": "1.1.0",
    "packages": [
      {
        "version": "0.1.0",
        "transport": {
          "type": "stdio"
        },
        "identifier": "askedthrice-mcp",
        "registryType": "npm",
        "registryBaseUrl": "https://registry.npmjs.org"
      }
    ],
    "repository": {
      "url": "https://github.com/nahuelsoria/llmaudit-mcp",
      "source": "github"
    },
    "websiteUrl": "https://askedthrice.com",
    "description": "Measure whether AI assistants actually recommend a brand, from measured answers."
  }
}

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