How to Use the SketricGen MCP in LangChain

Q: Can I see past interactions when using SketricGen and LangChain?

Absolutely. You can use listconversations or getconversation to pull conversation history, letting your agent build decisions based on previous user exchanges.

Q: What if I need to delete old chat history in SketricGen and LangChain?

You can explicitly use the deleteconversation tool. This gives you granular control over your data retention policy, which is always smart practice.

Q: Is there a way to check the overall status of the SketricGen MCP Server with LangChain?

Yes, calling checksketricgenstatus verifies connectivity right off the bat. It's the first thing you should run in any new deployment to make sure everything is talking.

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Connect SketricGen MCP to LangChain

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Setup SketricGen with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Sequential Action Chains

When you use `get_contact`, your agent pulls a contact's details. That output can then immediately feed into running a complex process via `run_workflow`. This allows you to build chains where the result of one tool call dictates the next required action. Because LangChain is focused on state management, it handles the necessary data passing between steps like magic. You don't have to manually manage context; the chain does it for you.

Workflow Observability

Need to debug a complex agent call? Just use `list_traces` and `get_trace`. These tools show exactly what happened during an execution, down to the inputs and outputs of every single tool. You can pinpoint latency issues or unexpected data flow instantly. This level of visibility means you spend less time debugging failed runs and more time building out smarter agents. It's critical for production systems.

Resource and Agent Management

Before running anything, check what tools are available. Call `list_agents` to see all the AI agents currently connected to your MCP Server instance. You can also use `get_agent` to pull deep details on a specific agent's configuration. Don't forget resource checks; you can monitor usage with `get_trace_credits` before running expensive, high-volume workflows.

Setup guide

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

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Common questions about SketricGen MCP in LangChain

SketricGen provides the underlying tools your agent needs. The LangChain framework then defines the order—the 'chain'—for calling these tools, ensuring data moves correctly from one step to the next.

Absolutely. You can use `list_conversations` or `get_conversation` to pull conversation history, letting your agent build decisions based on previous user exchanges.

The server touches various structured data types, including contact details (`get_contact`), conversation records, and agent metadata. It's critical to know what you're tracking.

You can explicitly use the `delete_conversation` tool. This gives you granular control over your data retention policy, which is always smart practice.

Yes, calling `check_sketricgen_status` verifies connectivity right off the bat. It's the first thing you should run in any new deployment to make sure everything is talking.

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