How to Use the Flotiq MCP in LangChain

Q: Can I trace Flotiq API latency inside LangChain?

Yes, every call to Flotiq tools like listcontentobjects is tracked in LangSmith, giving you exact latency and token counts for every headless CMS query.

Q: How do I chain Flotiq media asset checks in LangChain?

Your agent runs listmediaassets to verify a Flotiq image exists, then passes the resulting asset ID directly to patchcmsobject in a single, continuous LangChain execution sequence.

Q: Can the LangChain agent delete Flotiq content types?

No, the wipecmsobject tool only deletes specific Flotiq content object instances, meaning your core schemas remain untouched during LangChain executions.

Feed live Flotiq content schemas directly into LangChain pipelines to build deterministic multi-step content publishing chains with this MCP server.

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Connect Flotiq MCP to LangChain

Create your Vinkius account to connect Flotiq 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 Flotiq with LangChain

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Chain Flotiq content updates in LangChain

`create_cms_object` lets your LangChain agent write structured JSON payloads directly to your headless CMS. This MCP tool outputs a confirmation payload that feeds straight into your next LangChain link, letting you pass fresh content IDs to downstream nodes without writing custom glue code. LangSmith traces every step of this Flotiq execution, showing you the exact execution latency and token cost of each CMS mutation. You get clear visibility into your Flotiq payload structure and API response times as your LangChain agent works through complex content generation pipelines.

Track Flotiq tenant limits dynamically

`get_tenant_limits` retrieves your active subscription thresholds directly inside a LangChain routing step. Your LangChain agent checks these Flotiq limits before executing bulk runs, allowing the chain to branch or halt if you are nearing your API quota. This prevents runtime failures during high-volume Flotiq content migrations over MCP. By evaluating these Flotiq limits dynamically, your LangChain decision loops can throttle request rates or alert your team before hitting hard API ceilings.

Inspect Flotiq schema rules in LangChain

`get_content_type_schema` pulls the exact structural rules of your Flotiq models to validate inputs before your LangChain agent attempts a write operation. Your LangChain agent reads this Flotiq schema to format its payloads correctly, eliminating validation errors before they happen. This structural verification keeps your headless Flotiq database clean during LangChain runs. Instead of guessing field types, your LangChain agent adapts to Flotiq schema changes in real-time, matching required data types dynamically during long-running autonomous runs.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Flotiq. 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.

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Common questions about Flotiq MCP in LangChain

When the MCP server returns a validation error, your LangChain agent reads the error payload, compares it with the output from `get_content_type_schema`, and automatically corrects the Flotiq JSON structure in the next chain step.

Yes, every call to Flotiq tools like `list_content_objects` is tracked in LangSmith, giving you exact latency and token counts for every headless CMS query.

Your agent runs `list_media_assets` to verify a Flotiq image exists, then passes the resulting asset ID directly to `patch_cms_object` in a single, continuous LangChain execution sequence.

No, the `wipe_cms_object` tool only deletes specific Flotiq content object instances, meaning your core schemas remain untouched during LangChain executions.

Your Flotiq content objects and media assets stay in Flotiq and are processed in Vinkius's secure, ephemeral V8 sandboxes during LangChain sessions. No data is stored on Vinkius servers, and your API keys are isolated from the LLM context.

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