How to Use the Ayrshare MCP in Pydantic AI

Q: How does Pydantic AI use the Ayrshare tools?

You add the MCPToolset with the Ayrshare server URL to your Agent. Pydantic AI then makes tools like createpost available to your LLM, automatically validating the API responses against Pydantic models.

Q: What happens if the Ayrshare API returns unexpected data to my Pydantic AI agent?

Your agent will raise a ValidationError. This is the core benefit of Pydantic AI: it fails loudly instead of letting your agent work with bad or incomplete data from getpostanalytics.

Q: Can I manage my post schedule with this?

Yes. The setautoschedule and listautoschedules tools let your agent define and review posting times for your social platforms. Pydantic AI ensures the schedule data you get back is always correctly formatted.

Build type-safe social media agents. Get validated, structured data from Ayrshare with Pydantic AI.

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Connect Ayrshare MCP to Pydantic AI

Create your Vinkius account to connect Ayrshare 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 Ayrshare with Pydantic AI

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Get Social Analytics That Won't Break Your Code

Pulling data from APIs can be risky. Fields change, types are wrong, and your code breaks. Pydantic AI solves this by validating every response against a strict schema. When your agent calls `get_post_analytics` or `get_social_analytics`, the JSON response is parsed into a Pydantic model. If a field is missing or the wrong type, your agent gets a `ValidationError`, not silent data corruption.

Manage Your Publishing with this MCP Server

This isn't just about reading data; it's about writing it safely. Your agent can create and schedule posts, manage profiles, and update schedules with confidence. Use `create_post` and `schedule_post` knowing the inputs and outputs are checked. Pydantic AI's type-safe approach means you're far less likely to send a bad request or misinterpret a response from the Ayrshare MCP server.

Your Pydantic AI Agent, Your Choice of LLM

Pydantic AI doesn't lock you into a specific model provider. You can build your Ayrshare agent with OpenAI, Anthropic, Gemini, or a local model, and the tools will work the same way. The `MCPToolset` abstracts the connection, so tools like `list_history` and `list_profiles` are available to whichever LLM you've configured. You can swap the underlying model without rewriting your tool logic.

Setup guide

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

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

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

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Common questions about Ayrshare MCP in Pydantic AI

You add the `MCPToolset` with the Ayrshare server URL to your `Agent`. Pydantic AI then makes tools like `create_post` available to your LLM, automatically validating the API responses against Pydantic models.

Your agent will raise a `ValidationError`. This is the core benefit of Pydantic AI: it fails loudly instead of letting your agent work with bad or incomplete data from `get_post_analytics`.

If correctness is your top priority, it's the best choice. The automatic validation ensures your agent is always working with clean, predictable data structures from this MCP, which eliminates a whole class of bugs.

Yes. The `set_auto_schedule` and `list_auto_schedules` tools let your agent define and review posting times for your social platforms. Pydantic AI ensures the schedule data you get back is always correctly formatted.

The server processes your post content and analytics data within a sandboxed, ephemeral environment. With Pydantic AI, there's an extra layer of security: the strict data validation prevents malformed or unexpected data from the API from ever reaching your agent's logic.

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