How to Use the Medium Alternative MCP in Pydantic AI

Use the Medium Alternative MCP Server with Pydantic AI to enforce strict data validation on every story you publish.

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Connect Medium Alternative MCP to Pydantic AI

Create your Vinkius account to connect Medium Alternative 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 Medium Alternative with Pydantic AI

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Perplexity

Type-safe publishing with Pydantic AI

Every response from `create_post` and `create_publication_post` is validated against your Pydantic models. If the API returns junk, the agent stops immediately. You catch data errors before they hit your feed. It turns an unpredictable API into a reliable component of your publishing pipeline.

Query publications and contributors

Use `list_publications` to get a structured list of your writing spaces. Pydantic AI verifies the response fields, so your code never crashes on missing keys. Follow up with `list_contributors` to audit your team. The agent gets a typed object back, making it easy to filter or process the contributor data.

Verify user identity via get_me

The `get_me` tool returns your profile info as a validated object. Your agent knows exactly who it is acting as, with no room for ambiguity. It ensures your agent's state is always in sync with your actual Medium account. You get a clear, typed view of your user profile.

Setup guide

Set up Medium Alternative 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": {
        "medium-alternative-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Medium Alternative MCP in Pydantic AI

Pydantic AI provides runtime validation that catches API changes or unexpected responses immediately. It prevents silent failures by forcing the agent to handle only data that matches your defined models.

Yes, the MCP server is model-agnostic. Pydantic AI handles the validation of the tool outputs regardless of which model is driving the agent's logic.

You should wrap your tool calls in standard Python try-except blocks. Pydantic AI will raise a validation or execution error if the server is unreachable or returns malformed data.

Yes, you can configure the MCPToolset to use SSE transports. This is the preferred method for maintaining a persistent, reliable connection between your agent and the server.

Your token is never cached by the server. It is passed securely to the API endpoint during the request, and the server process remains isolated, ensuring your private key is never exposed.

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