MCP server

RankPine ships a hosted MCP (Model Context Protocol) server. Point any MCP client at it and your AI agent gets RankPine's capabilities as native tools — no HTTP glue code, no SDK.

• Endpoint: https://rankpine.com/api/mcp
• Transport: Streamable HTTP (stateless)
• Auth: the same API keys as the REST API, sent as Authorization: Bearer rp_live_…

Setup

Claude Code

claude mcp add rankpine https://rankpine.com/api/mcp \
  --transport http \
  --header "Authorization: Bearer rp_live_your_key_here"

Claude Desktop / claude.ai

Add a custom connector with URL https://rankpine.com/api/mcp and an Authorization: Bearer rp_live_… header (Settings → Connectors → Add custom connector).

Cursor

{
  "mcpServers": {
    "rankpine": {
      "url": "https://rankpine.com/api/mcp",
      "headers": { "Authorization": "Bearer rp_live_your_key_here" }
    }
  }
}

Any other client

Every MCP client that speaks Streamable HTTP works the same way: URL + authorization header. The server is stateless, so no session juggling.

Available tools

Things agents should know

• Generation is asynchronous. generate_article returns immediately; a full article takes several minutes. Poll get_article until status is "ready".
• Credits are real. Each generated article consumes one of the site's ~30 monthly credits — check get_subscription before batch-generating.
• One generate can produce several articles, but costs one credit. If get_site reports additional_languages, the article is also localised into each of them — those are covered by a paid add-on, not metered. The versions are separate articles sharing a translation_group_id; list_articles returns one row per version, so filter by language if you want just one.
• Rate limits are shared with the REST API (120/min, 10k/day per key).
• Errors come back as tool results, not protocol failures, so your agent can read the reason (quota_exceeded, rate_limited, …) and adapt.