How to Use the COR MCP in LangChain

Build multi-step agents in LangChain that track creative agency margins and project time using the COR MCP server.

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Connect COR MCP to LangChain

Create your Vinkius account to connect COR to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup COR with LangChain

Ask AI about this MCP

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Claude

Perplexity

Chain COR task data in LangChain

Feed `list_cor_tasks` directly into your LangChain agent to automate project auditing. The output from one tool call informs the next, letting your chains identify bottlenecks without manual oversight. Your agent parses `list_cor_time_entries` to calculate labor costs per project. This creates a feedback loop where your chain reacts to real-time data instead of static spreadsheets.

Manage projects through your LangChain agent

Use `create_cor_project` to spin up new jobs based on triggers from your other integrated services. Your agent handles the heavy lifting, ensuring every project file contains the right metadata from the start. Once the project exists, `get_cor_project` pulls the latest status back into your chain. You get a clear view of how your team tracks against budget targets.

Monitor infrastructure health with LangChain

Call `check_cor_status` to verify the connection between your pipeline and the platform. Your chain can automatically halt operations if the service reports downtime, preventing errors in your automated reporting. LangSmith traces every call, showing exactly what data moved through your pipeline. You see how your agent interpreted `list_cor_team_members` during the execution process.

Setup guide

Set up COR 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 COR 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({
    "cor-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 COR transactions"
    })
    print(result["messages"][-1].content)

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Common questions about COR MCP in LangChain

Install the necessary adapters and initialize the server via the HTTP transport. Point your client to the Vinkius endpoint and pass the tool definitions into your agent constructor.

Yes, because every tool output acts as a link in your chain. You can pipe the results of one query directly into another function call.

It can list team users and members using the dedicated tools. Your agent performs these lookups whenever it needs to assign tasks or track individual performance.

The server relies on the endpoint token provided by Vinkius. Your agent inherits the permissions tied to that specific session.

Your project and time entry data stays within a V8 isolate sandbox. No other process accesses your agency's financial records.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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