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glippy-mcp

glippy-mcp

Local
glippy.devBusiness & Marketing
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About glippy-mcp

GEO (Generative Engine Optimization) analysis: check any domain's AI-readiness for LLM search

Configuration

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

mcp.json · Universal config
{
  "mcpServers": {
    "jbobbink-glippy": {
      "command": "npx",
      "args": [
        "-y",
        "glippy-mcp"
      ],
      "env": {
        "GLIPPY_LICENSE_KEY": "<GLIPPY_LICENSE_KEY>",
        "GLIPPY_RATE_LIMIT": "<GLIPPY_RATE_LIMIT>"
      }
    }
  }
}
Run commandnpx -y glippy-mcp
Environment variables to set
GLIPPY_LICENSE_KEYSecretYour Glippy MCP license key (GLMCP-XXXX-XXXX-XXXX)
GLIPPY_RATE_LIMITMax requests per second per domain for batch tools (default 5)

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
npmglippy-mcp0.9.0

Tools

10

Saved tool list · updated 1w ago

ParameterTypeRequiredDescription
domainstringYesThe domain to analyse, e.g. "example.com". Do not include https:// prefix.
max_pagesintegerNoMaximum pages to crawl (1 = homepage only, up to 10 for multi-page analysis). Defaults to 10.
render_modeenumNoHow to fetch HTML. "static" (fast, plain Node fetch) is default for this tool. "auto" tries static first and falls back to a local headless Chrome for bot-blocked responses (401/403/407/429/503 or empty 2xx). "chrome" always renders via Chrome. Chrome modes need a local Chrome binary (CHROME_PATH) or an attached instance (CHROME_REMOTE_URL).static | chrome | auto
output_formatenumNoOutput format. "text" (default) returns a human-readable report. "json" returns the raw analysis result object that can be passed to export_report's analysis_result parameter.text | json

Details

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

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

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.