How to Use the Filebase (Web3 Storage) MCP in LangChain

Q: Can LangChain agents monitor bandwidth usage for Filebase (Web3 Storage)?

Yes. Your agent can call platformgetgatewayusage to pull real-time bandwidth metrics. This lets you construct chains that route traffic to different gateways based on current consumption.

Q: How does LangChain handle IPNS name updates using this MCP Server?

Your agent uses platformupdatename inside its execution loop to point an existing IPNS name to a new CID. LangChain records the tool inputs and outputs, giving you a clear audit trail of every IPNS update in LangSmith.

Q: What happens if a pinning operation fails during a chain execution?

The psaaddpin tool returns an error payload that your agent catches. You can configure your chain to retry the operation or fall back to rpcpinadd to handle temporary API bottlenecks.

Chain decentralized storage operations directly into your LangChain workflows to pin, update, and track IPFS assets.

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Connect Filebase (Web3 Storage) MCP to LangChain

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Setup Filebase (Web3 Storage) with LangChain

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Chaining Pinning Workflows in LangChain

This MCP Server exposes `psa_add_pin` to let your LangChain agents pin IPFS content directly during a chain execution. When your agent finishes processing data, it hands off the CID to the pinning service to guarantee persistence across the decentralized network. You can trace the inputs and outputs of `psa_get_pin` inside LangSmith to monitor latency and state. Having this visibility means you know exactly when a file transitions from pending to pinned without guessing.

Dynamic IPNS Publishing and Resolution

The `platform_create_name` tool allows your agent to establish static pointers for dynamic IPFS content. Instead of hardcoding new CIDs every time your chain updates a file, the agent updates the IPNS record to point to the latest version. Your agent uses `platform_update_name` to bind the new CID to the existing name. It then resolves the name with `rpc_name_resolve` to verify the update succeeded before moving to the next step in your pipeline.

Real-Time Usage Tracking and Budget Enforcement

The `platform_get_usage` tool gives your chain instant access to your current storage and bandwidth metrics. This prevents your agents from running up massive bills by checking constraints before executing large uploads. By coupling this tool with `platform_get_bucket_usage`, your agent can make decisions on whether to spin up a new gateway using `platform_create_gateway` or clean up old pins. It keeps your storage footprint lean without manual intervention.

Setup guide

Set up Filebase (Web3 Storage) 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 Filebase (Web3 Storage) 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({
    "filebase-web3-storage-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 Filebase (Web3 Storage) transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Filebase (Web3 Storage) MCP in LangChain

Use the LangChain MCP adapter to expose the pinning tools to your agent. Your agent can run a multi-step chain that first uploads content with `rpc_add` and then immediately pins the resulting CID using `psa_add_pin` in a single run.

Yes. Your agent can call `platform_get_gateway_usage` to pull real-time bandwidth metrics. This lets you construct chains that route traffic to different gateways based on current consumption.

Your agent uses `platform_update_name` inside its execution loop to point an existing IPNS name to a new CID. LangChain records the tool inputs and outputs, giving you a clear audit trail of every IPNS update in LangSmith.

The `psa_add_pin` tool returns an error payload that your agent catches. You can configure your chain to retry the operation or fall back to `rpc_pin_add` to handle temporary API bottlenecks.

The MCP server runs in a zero-trust V8 sandbox, meaning your API tokens and IPFS keys never leak to the LLM. Your agent only interacts with the tool schemas, while the actual execution happens in an ephemeral, isolated environment.

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