How to Use the Alchemer MCP in Pydantic AI

Q: Can I use Alchemer tools to list surveys in Pydantic AI?

Absolutely, just call listsurveys through your toolset. The result is immediately available for your agent to process as a list of typed objects.

Enforce strict schemas on Alchemer data using Pydantic AI to ensure your agent never processes malformed feedback.

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Connect Alchemer MCP to Pydantic AI

Create your Vinkius account to connect Alchemer 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 Alchemer with Pydantic AI

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Type-safe Alchemer data for Pydantic AI

Every call to `list_survey_responses` returns data validated against your Pydantic models. If the schema changes, the agent stops instead of guessing. Use `list_survey_questions` to define your validation models with confidence. You get guaranteed structure for every field in your survey forms.

Campaign management in Pydantic AI

Retrieve campaign statuses with `list_survey_campaigns` and map them to your internal state objects. Your agent maintains perfect parity with your live surveys. `list_contact_lists` returns arrays that Pydantic AI validates instantly. You never have to worry about missing fields or unexpected nulls.

Account monitoring tools

Verify your system state with `get_account_usage` to ensure your agent stays within limits. The tool output is typed and ready for your logic. `get_survey_details` provides the exact metadata needed to populate your validation classes. It keeps your agent code clean and predictable.

Setup guide

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

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

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

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Common questions about Alchemer MCP in Pydantic AI

Define your Pydantic models and the agent will check tool outputs against them. If the Alchemer data format shifts, the agent will throw a validation error.

Use the MCPToolset with your HTTP endpoint. It supports SSE and Streamable HTTP to keep the connection alive while your agent runs.

Absolutely, just call `list_surveys` through your toolset. The result is immediately available for your agent to process as a list of typed objects.

The agent fails loudly. This prevents the silent data corruption common in less rigid frameworks.

Vinkius handles authentication through a single endpoint token in a secure, ephemeral environment. Your raw survey submissions are never exposed to unauthorized processes.

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