How to Use the FirstPromoter MCP in Pydantic AI

Q: How do I initialize the FirstPromoter MCP Server in Pydantic AI?

Install the package using pip install "pydantic-ai-slimmcp". Use the MCPToolset class with your Vinkius HTTP URL and register it directly into your Pydantic AI agent's toolsets array.

Q: Can I use Pydantic AI to automate promoter onboarding on FirstPromoter?

Yes, your agent can call createpromoter and listpromoters. The input arguments are strictly validated against Pydantic schemas before the API call is dispatched to Vinkius.

Build type-safe affiliate agents using Pydantic AI to validate every FirstPromoter API response at runtime.

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Connect FirstPromoter MCP to Pydantic AI

Create your Vinkius account to connect FirstPromoter 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 FirstPromoter with Pydantic AI

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Type-Safe Affiliate Operations

The FirstPromoter MCP Server integrates with Pydantic AI to guarantee that all affiliate payload structures match your application's expected models. Every response from `get_lead` or `get_promoter` is validated against strict types before your agent acts on it. If FirstPromoter updates its API or returns unexpected schema variants, your Pydantic AI agent fails loudly. This prevents corrupt data from polluting your internal databases or triggering incorrect payout loops.

Pydantic AI Reward Verification

Issue affiliate rewards safely using `create_reward` and `list_rewards`. Pydantic AI ensures that the parameters passed to the reward creation tool conform exactly to your financial schemas. The agent checks current campaign metrics via `get_campaign_report` to confirm that the affiliate hit their target. If the data passes type validation, the agent executes `create_reward` with exact numerical precision.

Validated Conversion Audits

Audit your referral pipelines using `list_conversions` and `list_leads`. This MCP setup parses raw referral data into typed Pydantic models, allowing your agent to run clean, structured audits. By enforcing schema constraints on `get_promoter_report`, you eliminate hallucinated promoter IDs or incorrect conversion values. Your agent operates only on verified, structured data.

Setup guide

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

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by FirstPromoter. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about FirstPromoter MCP in Pydantic AI

Install the package using `pip install "pydantic-ai-slim[mcp]"`. Use the `MCPToolset` class with your Vinkius HTTP URL and register it directly into your Pydantic AI agent's toolsets array.

Pydantic AI raises a validation error at runtime. This prevents tools like `get_payout_stats` from passing malformed data to your agent, protecting your downstream accounting systems.

Yes, your agent can call `create_promoter` and `list_promoters`. The input arguments are strictly validated against Pydantic schemas before the API call is dispatched to Vinkius.

Yes, Pydantic AI is model-agnostic. You can use the FirstPromoter tools with OpenAI, Anthropic, Gemini, or local models run via Ollama, as long as they support tool calling.

All commission rates and payout values are processed in an ephemeral V8 Isolate Sandbox using this MCP connection. Pydantic AI validates the data structures locally, ensuring no sensitive credentials or financial records are leaked.

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