How do I find my AlisQI Bearer Token?

Log in to AlisQI, navigate to **Menu** > **Management** > **Integration Hub**. You can generate and manage your API tokens there. Each token inherits the permissions of the associated user account.

Is the data model the same for everyone?

No, AlisQI uses a dynamic, user-defined data model. This means field names and analysis sets are specific to your company's configuration. Use the `list_analysis_sets` tool to discover your unique structure.

Can I attach files to quality results via the agent?

Currently, the agent can retrieve metadata for existing attachments using the `get_result_attachments` tool. For uploading new files, we recommend using the AlisQI web interface or specific integration endpoints.

AlisQI MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: AlisQI
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect AlisQI through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to AlisQI "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in AlisQI?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About AlisQI MCP Server

Connect your AlisQI instance to your AI agent to unlock professional quality management (QMS) orchestration. From auditing quality results and managing analysis sets to retrieving technical metadata for fields and monitoring workflow webhooks, your agent handles your quality operations through natural conversation.

Pydantic AI validates every AlisQI tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Results Orchestration — List, retrieve, and store quality results for any of your custom analysis sets
Schema Discovery — List and audit analysis sets and their field definitions to understand your dynamic data model
Document Oversight — Retrieve technical metadata for result attachments and monitor your quality documentation
Workflow Monitoring — List active webhooks to ensure your quality event triggers (like non-conformities) are operational
QMS Insights — Quickly identify quality trends or audit recent analysis entries directly from your chat interface

The AlisQI MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect AlisQI to Pydantic AI via MCP

Follow these steps to integrate the AlisQI MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from AlisQI with type-safe schemas

Why Use Pydantic AI with the AlisQI MCP Server

Pydantic AI provides unique advantages when paired with AlisQI through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your AlisQI integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your AlisQI connection logic from agent behavior for testable, maintainable code

AlisQI + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the AlisQI MCP Server delivers measurable value.

Type-safe data pipelines: query AlisQI with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple AlisQI tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query AlisQI and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock AlisQI responses and write comprehensive agent tests

AlisQI MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect AlisQI to Pydantic AI via MCP:

get_analysis_set_details

Get set metadata

get_api_info

Check API status

get_result_attachments

List document attachments

get_result_details

Get specific result

list_active_webhooks

List active triggers

list_analysis_sets

List analysis sets

list_choice_lists

List selection menus

list_fields

List dynamic fields

list_results

Supports filtering. List quality results

store_results

Create or update results

Example Prompts for AlisQI in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with AlisQI immediately.

"List all analysis sets available in my AlisQI instance."

"Show the last 5 quality results for 'Raw Material Inspection'."

"Check if there are any active webhooks for non-conformities."

Troubleshooting AlisQI MCP Server with Pydantic AI

Common issues when connecting AlisQI to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

AlisQI + Pydantic AI FAQ

Common questions about integrating AlisQI MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your AlisQI MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.