RankParse
Use case · AI SEO Agents

Give your agent real SEO data.

RankParse has an MCP server for Claude Code and Cursor, and a REST API that works with LangChain, AutoGen, and any agent that supports tool use. Backlinks, domain authority, and 20+ endpoints — structured JSON, every response.

Claude Code setupMCP server docs

What your agent can do with RankParse

Competitive authority analysis

"Compare the domain authority of my top 5 competitors and rank them." Agent calls /v1/batch and returns a sorted table.

GEO / AEO monitoring

Track which sites link to AI brand mentions (OpenAI, Anthropic, Perplexity). Find link patterns to target for generative engine optimization.

Link gap discovery

"Find domains linking to competitor-a.com and competitor-b.com but not mine." Agent calls /v1/link-intersect and returns targets.

Prospect qualification

"Score these 50 domains before I do outreach." Agent batch-enriches with authority score and referring domain count.

Content gap via backlinks

"Which competitor pages get the most backlinks that I haven't covered?" Agent calls /v1/top-pages and compares URL patterns.

Tech stack targeting

"Find domains running Webflow with DA above 50." Agent filters tech stack results for outbound ICP matching.

Integration options

MCP server (Claude Code / Cursor)

Add the MCP server to your config. Claude Code discovers all 18 tools automatically. No SDK required.

Setup guide →

LangChain / LlamaIndex

Wrap the REST endpoints as StructuredTool functions. All endpoints return clean JSON — no parsing overhead.

REST API docs →

n8n / Make / Zapier

Use the HTTP Request node with X-API-Key header. No custom integration required — plain GET requests.

Getting started →

Example: LangChain tool registration

Wrap RankParse endpoints as LangChain tools. Any agent with tool use can call them.

from langchain.tools import StructuredTool
import requests

API_KEY = "rp_your_key"

def get_domain_authority(domain: str) -> dict:
    """Get the domain authority score and referring domain count for a domain."""
    return requests.get(
        "https://api.rankparse.com/v1/domain-authority",
        headers={"X-API-Key": API_KEY},
        params={"domain": domain},
    ).json()["data"]

def get_backlinks(domain: str, limit: int = 20) -> list:
    """Get the top backlinks for a domain."""
    return requests.get(
        "https://api.rankparse.com/v1/backlinks",
        headers={"X-API-Key": API_KEY},
        params={"domain": domain, "limit": limit},
    ).json()["data"]

# Register as LangChain tools
authority_tool = StructuredTool.from_function(get_domain_authority)
backlinks_tool = StructuredTool.from_function(get_backlinks)

# Agent can now answer: "Is techcrunch.com a good link target?"
# "Compare authority: linear.app vs height.app vs plane.so"

Why AI teams choose RankParse

Structured JSON every response — no HTML scraping, no regex parsing
MCP server with 18 pre-built tools for Claude Code and Cursor
Credits never expire — agent workloads run at their own pace
Atomic credit deduction — concurrent agent calls don't double-spend
p95 latency under 200ms — fast enough for synchronous agent loops

Give your agent SEO data.

100 free credits. No credit card. MCP setup in 2 minutes.

Get API key — freeClaude Code setup →