How to Use the AskNicely MCP in Pydantic AI

Build type-safe agents with Pydantic AI using this MCP Server to validate AskNicely NPS responses at runtime.

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Connect AskNicely MCP to Pydantic AI

Create your Vinkius account to connect AskNicely 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 AskNicely with Pydantic AI

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Validate AskNicely data with Pydantic AI

Pulling feedback with `list_responses` forces the output through your defined models. If the API returns a string instead of an integer for a score, the agent fails loudly. You get exact numbers from `get_statistics` that never hallucinate. Your type-safe framework guarantees the sentiment metrics match the expected schema before the logic acts on them.

Map contacts to strict schemas

The `list_contacts` tool fetches your audience roster directly into Pydantic models. Missing email fields trigger immediate validation errors rather than corrupting your downstream processes. Correctness matters more than speed when dealing with customer data. This integration forces your application to only process valid user records.

Fire surveys via a typed MCP Server

Initiating an outreach campaign requires the `trigger_survey` tool. Your agent validates the payload structure before the HTTP request ever leaves the server. Checking the connection status with `get_account_check` follows the same strict rules. The framework confirms the authentication state matches your expected boolean output.

Setup guide

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

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

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

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Common questions about AskNicely MCP in Pydantic AI

Install pydantic-ai-slim via pip. Use the unified MCPToolset pointing to the Vinkius MCP endpoint, and pass it to your Agent toolsets list.

Yes. The framework enforces strict schemas on every response. Unexpected payloads trigger validation errors immediately.

The framework is completely model-agnostic. You route survey data through OpenAI, Anthropic, or local open-weights models without changing the tool setup.

Streamable HTTP works best for external MCP endpoints. The deprecated MCPServerHTTP class is no longer recommended.

NPS scores and customer names flow through a secure, ephemeral V8 isolate. Vinkius manages the authentication token centrally, meaning your raw credentials stay out of the agent memory.

Use it with your favorite AI tools

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