Foglift MCP Server

Why an MCP server?

The Model Context Protocol is an open specification published by Anthropic in November 2024. It standardizes how AI agents — Claude Code, Cursor, Windsurf, and any client built on the official SDK — call external tools over JSON-RPC, the way the Language Server Protocol standardized editor/language tooling a decade earlier.

For AI search optimization specifically, MCP matters because the work is iterative: you fix an FAQ schema, then re-check whether ChatGPT cites the page, then adjust prompts, then re-scan. Every dashboard round-trip breaks the loop. With Foglift’s MCP server, the agent runs the whole cycle inside the editor: ask Claude Code “why is my pricing page not cited in Perplexity?” and it can call scan_website, get_ai_results, and get_sentiment in sequence, then propose schema fixes you can apply in the same session.

The cost of not shipping an MCP server is concrete: a16z’s 2026 developer tooling brief flagged MCP support as a leading indicator of which dev tools survive the agent-native shift. AI agents are becoming a meaningful fraction of tool callers, and tools that require custom REST glue per agent will be skipped in favor of those that don’t.

The 13 tools

Each tool wraps a Foglift API endpoint with a typed Zod schema. The agent picks tools based on the conversation; you don’t hand-write API calls.

Setup

1. Generate an API key

Sign up at foglift.io, then Dashboard → Settings → API Keys. Free plan is fine.

2. Add the server to your MCP client

Claude Code (~/.claude/mcp.json):

{
  "mcpServers": {
    "foglift": {
      "command": "npx",
      "args": ["-y", "foglift-mcp"],
      "env": { "FOGLIFT_API_KEY": "fk_live_..." }
    }
  }
}

Cursor: Settings → MCP → Add server → use the same command and args above.

Windsurf: Cascade → Tools → MCP → use the same configuration.

3. Restart and verify

Restart your client. In a new chat, ask: “Use the foglift get_usage tool and show me my plan and token balance.” You should see a structured response with your plan tier, scan quota, and token balance.

Three workflows worth automating

Audit a competitor in five seconds

“Use the foglift batch_scan tool on stripe.com, ramp.com, mercury.com, brex.com, and rippling.com. Then summarize the AEO score gaps in a markdown table.”

The agent calls batch_scan once, parses the JSON, and writes the table. What used to take five dashboard tabs and a copy-paste pass now takes one prompt.

Diagnose an AI Visibility regression

“Pull the last 30 days of AI Visibility results for foglift.io with get_ai_results. Find the prompt with the largest drop in citation rate, then run get_sentiment on it. Tell me what changed.”

The agent reasons across two endpoints, isolates the regression, and explains the sentiment shift in natural language. This is the kind of work that doesn’t fit a dashboard chart well.

Keep prompts in sync with releases

“Read the changelog entry I just wrote in CHANGELOG.md, then use add_prompt to add tracking prompts for the new feature: one branded query, one generic category query, one comparison query against our top three competitors.”

Prompt management is one of the most-skipped GEO chores because it’s manual. With MCP, it can be a one-liner at the end of every release.

Foglift MCP vs. other AI search optimization tools

MCP support across the AI search optimization category as of April 2026:

See the full ranking in our Best AI Search Tools with MCP Integration 2026 breakdown.

Sources

• Anthropic. Introducing the Model Context Protocol (November 2024). The original MCP specification announcement.
• Anthropic. modelcontextprotocol/servers — reference servers and client list.
• a16z. The Developer Tooling Shift Is Already Here (2026). Discusses MCP as a leading indicator for agent-native dev tools.
• Foglift. foglift-mcp on npm — package, version history, license.