How to Use the Filebase (Web3 Storage) MCP in Pydantic AI

Q: Can I use Pydantic AI to track Filebase (Web3 Storage) bandwidth usage?

Yes. The agent calls platformgetusage and parses the response into typed Python objects for accurate monitoring.

Q: How do I configure the Filebase (Web3 Storage) toolset in Pydantic AI?

You initialize the toolset using the unified MCPToolset class with your server URL. Then, pass it directly to the agent's toolsets list.

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Connect Filebase (Web3 Storage) MCP to Pydantic AI

Create your Vinkius account to connect Filebase (Web3 Storage) to Pydantic AI 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 Filebase (Web3 Storage) with Pydantic AI

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Type-Safe Pinning via Pydantic AI

The `psa_add_pin` tool adds a CID to your pinning queue while enforcing strict schema validation on the response. If the pinning service returns unexpected fields, Pydantic AI raises a validation error immediately. This prevents silent failures in your production pipelines. The agent relies on clean, verified structures when listing active pins with `psa_list_pins` using this MCP Server.

Validated IPNS Name Management

The `platform_get_name` tool retrieves IPNS metadata and validates the schema at runtime. When updating paths, the agent uses `platform_update_name` to ensure the target CID matches the expected format. This guarantees your app never links to malformed addresses. The framework catches formatting issues before they hit the network.

Query IPFS Daemon State Securely

The `rpc_version` tool checks the active IPFS daemon version to confirm compatibility. Running `rpc_pin_ls` lets your agent verify pinned objects against local state. This keeps your storage layer synchronized. The type-safe responses ensure your code handles daemon state changes via this MCP Server without crashing.

Setup guide

Set up Filebase (Web3 Storage) MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "filebase-web3-storage-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Filebase (Web3 Storage) tools.",
)

result = await agent.run("List recent Filebase (Web3 Storage) transactions")
print(result.output)

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Common questions about Filebase (Web3 Storage) MCP in Pydantic AI

The framework validates every response against Pydantic models. If Filebase (Web3 Storage) returns unexpected data, the client raises a validation error instead of passing bad data.

Yes. The agent calls `platform_get_usage` and parses the response into typed Python objects for accurate monitoring.

You initialize the toolset using the unified `MCPToolset` class with your server URL. Then, pass it directly to the agent's toolsets list.

Yes. This MCP Server supports async calls natively, allowing your agent to pin files without blocking other application tasks.

We handle authentication via a single endpoint token. This MCP Server isolates your raw files, bucket metrics, and IPNS keys from external networks.

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