How to Use the Internet Archive MCP in Pydantic AI

Q: Can I search the Internet Archive using Pydantic AI tools?

You can. The search tool integrates seamlessly, allowing your agent to query the archive and validate the resulting item list.

Q: Does my Pydantic AI agent support Wayback Machine checks?

It does. You can call waybackavailability and have the agent verify the snapshot status using your custom logic.

Use Pydantic AI to validate every Internet Archive response against strict schemas for high-integrity research agents.

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Connect Internet Archive MCP to Pydantic AI

Create your Vinkius account to connect Internet Archive 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 Internet Archive with Pydantic AI

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Type-safe metadata retrieval

Every response from `get_item_metadata` is validated against your Pydantic models. If the archive returns malformed data, your agent catches it immediately. This prevents silent failures during data ingestion. You get the exact fields you define, or the agent errors out safely.

Strict search result parsing

The `search` tool returns a structured list of identifiers. Your agent maps these to your internal types for further processing. By enforcing types on the search results, you ensure that every subsequent call to `get_item_files` uses a valid identifier.

Analyze historical snapshots

Use `wayback_availability` to retrieve snapshot metadata. Your agent validates the timestamp and URL format before committing to a link. This protects your agent from hallucinated URLs or broken snapshots. You get a verified, typed response every time.

Setup guide

Set up Internet Archive 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": {
        "internet-archive-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Internet Archive transactions")
print(result.output)

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Common questions about Internet Archive MCP in Pydantic AI

The framework validates the JSON response against your defined Pydantic models. This ensures the data is clean before your agent uses it.

You can. The `search` tool integrates seamlessly, allowing your agent to query the archive and validate the resulting item list.

It does. You can call `wayback_availability` and have the agent verify the snapshot status using your custom logic.

Pydantic AI will raise a validation error. This prevents your agent from processing corrupted data and stops the flow immediately.

Yes. This MCP Server does not store any state or user information. It only processes public requests for archive metadata.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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