How to Use the MediaWiki MCP in Pydantic AI

Q: How do I validate MediaWiki results in Pydantic AI?

The MCPToolset automatically validates responses against Pydantic models. If the server returns something that doesn't match your schema, you get an immediate error.

Q: Is it possible to list recent edits in MediaWiki with Pydantic AI?

Yes, call listrecentchanges through the toolset. You'll receive the data in a validated format that your agent can process right away.

Get type-safe MediaWiki interactions in Pydantic AI with runtime validation for every page request.

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Connect MediaWiki MCP to Pydantic AI

Create your Vinkius account to connect MediaWiki 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 MediaWiki with Pydantic AI

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Type-safe wiki operations in Pydantic AI

Every response from the server is validated against your Pydantic models. If `get_page_info` returns an unexpected field, the agent halts, preventing malformed data from polluting your state. This runtime checking keeps your agent logic predictable. You don't have to worry about silent failures when the wiki API structure changes.

Reliable page editing with MCP Server

Execute `edit_page` with strict schema requirements. The agent validates the input before the call is even sent to the server, ensuring your edits follow your defined rules. Use `get_tokens` to manage your CSRF session securely. The Pydantic AI framework ensures that these tokens are handled correctly, maintaining a clean state for every transaction.

Structured retrieval of wiki data

Query your knowledge base with `search_pages` and parse the output into typed objects. This makes it easy to integrate wiki content into your agent's decision-making process. List pages in categories using `list_category_members` to build a structured map of your site. It’s a clean way to organize your data without manual parsing.

Setup guide

Set up MediaWiki 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": {
        "mediawiki-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about MediaWiki MCP in Pydantic AI

The `MCPToolset` automatically validates responses against Pydantic models. If the server returns something that doesn't match your schema, you get an immediate error.

Yes, call `list_recent_changes` through the toolset. You'll receive the data in a validated format that your agent can process right away.

It only accesses the data you explicitly query. Your API keys and site credentials stay local to your Pydantic AI agent instance.

You can restrict which tools the agent has access to by defining your own toolset wrapper. This lets you limit the agent to read-only operations.

You get a standardized, type-safe interface for wiki actions. It removes the need for custom API wrappers and makes your agent code cleaner.

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