How to Use the Raven Tools MCP in LangChain

Build SEO reasoning chains in LangChain by linking Raven Tools data directly into your agent's decision pipeline.

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Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers — Included with Plan

Connect Raven Tools MCP to LangChain

Create your Vinkius account to connect Raven Tools to LangChain — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

GDPR Included with Plan

Setup Raven Tools with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Automate rank tracking in LangChain

Chain your agent's logic to fetch live SEO data. You can feed the output of `get_rank_all` directly into a prompt template to analyze performance trends without manual intervention. Your agent decides when to pull data based on the chain state. It uses `get_rank` to verify specific keyword positions during iterative testing cycles.

Audit site health via LangChain pipelines

Trigger a technical scan using `get_site_audit` as a node in your LangGraph workflow. This allows your agent to detect crawl errors or broken links before moving to the next task. Developers get full observability through LangSmith. You see exactly how `get_site_audit` inputs influence subsequent SEO recommendations.

Manage SEO projects through LangChain

Use `list_projects` to populate your agent's context with active client domains. This keeps your reasoning pipeline grounded in the correct project metadata. Your agent handles project-specific tasks by passing identifiers from `list_projects` into tools like `get_links`. It keeps your logic modular and focused on specific client needs.

Setup guide

Set up Raven Tools MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Raven Tools tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "raven-tools-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Raven Tools transactions"
    })
    print(result["messages"][-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Raven Tools. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

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Real-time monitoring

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visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

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Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Raven Tools MCP in LangChain

Install the necessary MCP adapters and initialize the client with your endpoint. Pass the tools retrieved from the server into your agent constructor to enable direct access.

The server is stateless, but you can maintain context using session management. Store your tool results in a vector store or memory buffer to keep the data available for future chain steps.

Yes, it exposes site audit data as a function tool. Your agent triggers the scan, waits for the response, and incorporates the findings into its final output.

The MCP server returns an error object that your agent handles via standard exception logic. You can build retry loops or fallback chains to ensure your workflow stays active.

Your API key is handled by the Vinkius sandbox, keeping it out of your local code. We touch only your SEO project metrics, keyword rankings, and backlink counts, ensuring no sensitive PII is exposed during the audit.

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