Yelp data for agents vs NannyKeeper — Household Employer Tax API

In-depth architectural comparison of the Yelp data for agents and NannyKeeper — Household Employer Tax API MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.

Which MCP Server Should You Choose?

Y

Choose Yelp data for agents when:

• You need dedicated capabilities in the Finance & Fintech domain.
• You prefer local stdio subprocess transport architecture.
• Your security boundary fits: No auth required (Free / Open Source).

Explore Yelp data for agents Details

Choose NannyKeeper — Household Employer Tax API when:

• You need dedicated capabilities in the Finance & Fintech domain.
• You prefer local stdio subprocess transport architecture.
• Your security boundary fits: No auth required (Free / Open Source).

Explore NannyKeeper — Household Employer Tax API Details

Feature & Specification Comparison

Specification	Y Yelp data for agents Finance & Fintech	NannyKeeper — Household Employer Tax API Finance & Fintech
Summary	4 Yelp endpoints. Pay per call in USDC via x402.	Calculate US household employer (nanny) taxes + run payroll via AI agents. All 50 states.
Category & Scope	Finance & Fintech	Finance & Fintech

Tools & Capabilities Breakdown

Ready-to-Paste Client Configurations

Paste either (or both) of these JSON server blocks into your client config file (e.g. claude_desktop_config.json or ~/.cursor/mcp.json).

{
  "mcpServers": {
    "yelp-data-for-agents": {
      "command": "npx",
      "args": [
        "-y",
        "yelp-data-for-agents"
      ]
    }
  }
}

{
  "mcpServers": {
    "mcp-server-36": {
      "command": "npx",
      "args": [
        "@nannykeeper/mcp-server"
      ]
    }
  }
}

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