How to Use the Aspire MCP in Pydantic AI

Q: How do I register the Aspire toolset in Pydantic AI?

Install the pydantic-ai-slimmcp package, then instantiate MCPToolset with your Vinkius HTTP URL. Pass this toolset object into your Agent constructor to instantly expose tools like listcampaigns.

Q: Does Pydantic AI validate the data returned by the Aspire MCP Server?

Yes. Every response from tools like getmember is validated against Pydantic models at runtime. If the server returns data that violates your defined schemas, the agent raises a validation error immediately.

Q: Is the MCPServerHTTP class still supported in Pydantic AI?

No, MCPServerHTTP is deprecated in Pydantic AI. You should use the unified MCPToolset class pointing to your Aspire MCP endpoint to handle all tool discovery and execution.

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Setup Aspire with Pydantic AI

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Type-Safe Campaign Auditing with Pydantic AI

Marketing data can be messy, but your Pydantic AI pipeline shouldn't be. When your agent calls `get_project` or `list_projects` through Pydantic AI, every field is validated against strict Python type schemas at runtime. If the API returns unexpected nulls or modified fields, the framework catches it instantly instead of letting corrupt data break your downstream apps. This strict validation ensures that your Aspire marketing reports are built on clean, reliable data returned by this MCP Server.

Runtime Validation for Creator Profiles

Creator profiles often contain varied social handles and engagement metrics. By using `get_member` within Pydantic AI, you ensure that Aspire influencer details are parsed correctly before your agent attempts to evaluate them. If an Aspire profile lacks required fields, Pydantic AI raises a clear validation error. This allows you to handle edge cases gracefully, keeping your automated outreach and evaluation pipelines completely reliable.

Verified Content Metrics

Tracking campaign performance requires precise numbers. When your agent pulls engagement metrics using `get_post` or `list_posts`, Pydantic AI validates the integers and strings returned by the server, ensuring your calculations are based on valid data. This setup eliminates the risk of your agent misinterpreting social metrics. You get clean, validated performance stats that you can safely pass to your financial or reporting modules.

Setup guide

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

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

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

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Common questions about Aspire MCP in Pydantic AI

Install the `pydantic-ai-slim[mcp]` package, then instantiate `MCPToolset` with your Vinkius HTTP URL. Pass this toolset object into your `Agent` constructor to instantly expose tools like `list_campaigns`.

Yes. Every response from tools like `get_member` is validated against Pydantic models at runtime. If the server returns data that violates your defined schemas, the agent raises a validation error immediately.

Pydantic AI supports both Streamable HTTP and SSE transports. For connecting to your hosted Vinkius server, the Streamable HTTP transport is the standard, secure choice.

No, `MCPServerHTTP` is deprecated in Pydantic AI. You should use the unified `MCPToolset` class pointing to your Aspire MCP endpoint to handle all tool discovery and execution.

Your campaign metrics and creator lists are validated purely in-memory within your local or cloud runtime. No campaign data is sent to external validation services, and Vinkius's zero-trust MCP gateway ensures your credentials and payloads remain private.

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