dev.dungbeetle/mcp

Visual regression & snapshot testing for AI agents — list runs, read semantic diffs, review.

What does this entry provide?

dev.dungbeetle/mcp is an MCP server entry in ChatHome Skills, sourced from Official MCP Registry and scored 67/100. 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 dev.dungbeetle/mcp 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: 67/100
Install method: npm
Repo: DungbeetleTech/cloud
Repo updated: Jul 5, 2026

Install command

npx -y dungbeetle-mcp@0.1.0

Install method: npm

Review the upstream source before running this command.

GitHub project Homepage

Quality

Repo stars

Repo updated

Jul 5, 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.

MCP manifest

{
  "_meta": {
    "io.modelcontextprotocol.registry/official": {
      "status": "active",
      "isLatest": true,
      "updatedAt": "2026-07-05T19:23:22.456831Z",
      "publishedAt": "2026-07-05T19:23:22.456831Z",
      "statusChangedAt": "2026-07-05T19:23:22.456831Z"
    }
  },
  "server": {
    "name": "dev.dungbeetle/mcp",
    "$schema": "https://static.modelcontextprotocol.io/schemas/2025-09-29/server.schema.json",
    "version": "0.1.0",
    "packages": [
      {
        "version": "0.1.0",
        "transport": {
          "type": "stdio"
        },
        "identifier": "dungbeetle-mcp",
        "registryType": "npm",
        "registryBaseUrl": "https://registry.npmjs.org",
        "environmentVariables": [
          {
            "name": "DUNGBEETLE_AGENT_TOKEN",
            "isSecret": true,
            "description": "Scoped, revocable agent token minted via `dungbeetle login` (recommended)."
          },
          {
            "name": "DUNGBEETLE_CLIENT_ID",
            "description": "Repository client id (CI-style alternative to an agent token)."
          },
          {
            "name": "DUNGBEETLE_CLIENT_SECRET",
            "isSecret": true,
            "description": "Repository client secret (pairs with DUNGBEETLE_CLIENT_ID)."
          },
          {
            "name": "DUNGBEETLE_SERVER_URL",
            "description": "Server origin; only needed when self-hosting (default https://dungbeetle.dev)."
          }
        ]
      }
    ],
    "repository": {
      "url": "https://github.com/DungbeetleTech/cloud",
      "source": "github"
    },
    "websiteUrl": "https://dungbeetle.dev/mcp/",
    "description": "Visual regression & snapshot testing for AI agents — list runs, read semantic diffs, review."
  }
}

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