How to Use the GetFeedback MCP in Pydantic AI

Q: How do I initialize GetFeedback with Pydantic AI?

Install pydantic-ai-slimmcp. Create an MCPToolset pointing to your MCP Server HTTP endpoint and pass it to the toolsets parameter of your Agent.

Q: Will Pydantic AI catch missing GetFeedback data?

Yes. If a call to getresponsedetails returns a missing required field, the validation error stops the agent immediately. You define exactly which fields are optional.

Q: Why use the unified toolset approach?

The older HTTP server method is deprecated. MCPToolset provides a cleaner, type-safe way to bind the external tools to your agent.

Q: Does this MCP Server expose my Pydantic AI survey metadata?

Auth tokens required for getmyidentity operate strictly behind a zero-trust boundary. The V8 Isolate Sandbox ensures that your proprietary survey structures never leak to unauthorized network requests.

Enforce strict runtime validation on all GetFeedback API responses by connecting your Pydantic AI agents to this MCP integration.

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Connect GetFeedback MCP to Pydantic AI

Create your Vinkius account to connect GetFeedback 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 GetFeedback with Pydantic AI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Type-Safe MCP Server Operations

Every payload from `list_completed_feedback` gets validated against your custom Pydantic models the second it hits this GetFeedback MCP Server. If the API changes a field name or returns an unexpected null, the agent fails loudly. You never get silent data corruption in your database. The framework catches hallucinated parameters before they execute. If the LLM tries to pass a string instead of an integer to `list_recent_feedback`, Pydantic AI rejects the tool call immediately.

Reliable Survey Triggers

Automating `send_survey_invites` requires absolute precision when targeting your GetFeedback users. Your agent uses strict schemas to guarantee that every target email address is formatted correctly before hitting the endpoint. Network drops won't cause silent errors. The agent runs `verify_api_connection` and validates the status code against an expected literal type before proceeding.

Metric Extraction Pipelines

Agents pulling GetFeedback data via `get_survey_stats` cast the raw JSON into exact float and integer types. You know exactly what shape the response count and average scores will take. When iterating through `list_feedback_page`, the system guarantees that every paginated response matches your predefined schema. It works identically whether you use an Anthropic, OpenAI, or local model.

Setup guide

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

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

result = await agent.run("List recent GetFeedback 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 GetFeedback. 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 GetFeedback MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]`. Create an `MCPToolset` pointing to your MCP Server HTTP endpoint and pass it to the `toolsets` parameter of your Agent.

Yes. If a call to `get_response_details` returns a missing required field, the validation error stops the agent immediately. You define exactly which fields are optional.

The framework is completely model-agnostic. You can pipe the output of `list_surveys` into a local Llama 3 instance just as easily as GPT-4.

The older HTTP server method is deprecated. `MCPToolset` provides a cleaner, type-safe way to bind the external tools to your agent.

Auth tokens required for `get_my_identity` operate strictly behind a zero-trust boundary. The V8 Isolate Sandbox ensures that your proprietary survey structures never leak to unauthorized network requests.

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