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, with structured JSON in every response.
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/domain-authority for each one 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 looks up authority score and referring domain count for each one.
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
- Setup guide →
MCP server (Claude Code / Cursor)
Add the MCP server to your config. Claude Code discovers all 18 tools automatically. No SDK required.
- REST API docs →
LangChain / LlamaIndex
Wrap the REST endpoints as StructuredTool functions. All endpoints return clean JSON with no parsing overhead.
- Getting started →
n8n / Make / Zapier
Use the HTTP Request node with X-API-Key header. No custom integration required, just plain GET requests.
- Workflow library →
Cloud prompt workflow templates
Use copy-paste prompts with exact MCP tool mappings, config snippets, and REST fallback templates.
Example: LangChain tool registration
Wrap RankParse endpoints as LangChain tools. Any agent with tool use can call them.
from langchain.tools import StructuredTool
from rankparse import RankParseClient
client = RankParseClient(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 client.domain_authority(domain)["data"]
def get_backlinks(domain: str, limit: int = 20) -> list:
"""Get the top backlinks for a domain."""
return client.backlinks(domain, limit=limit)["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, so 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.