How do I find the Object Key for a specific table?

Use the `list_objects` tool. It will return all data objects in your application along with their names and unique keys (e.g., `object_1`). Use these keys for all subsequent record operations.

Can I search records using multiple conditions?

Yes. Use the `search_records` tool and provide a JSON array of filter objects. For example: `[{"field":"field_1", "operator":"is", "value":"active"}, {"field":"field_2", "operator":"contains", "value":"premium"}]`.

How do I know which fields to use when creating a record?

Use the `list_object_fields` tool with the target `object_key`. It will list all fields, their types, and their keys (e.g., `field_1`), which you must use as property names in your data JSON.

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

Name: Knack
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 Knack 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 Knack "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Knack?"
    )
    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 Knack MCP Server

Connect your Knack application to any AI agent and take full control of your no-code database through natural conversation.

Pydantic AI validates every Knack 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

Database Schema Discovery — List all objects and fields to understand your data structure without leaving the chat
Record Management — Create, retrieve, update, and delete records in any object securely
Advanced Querying — Search for specific records using complex filters based on any field criteria
Data Auditing — Get detailed summaries of specific records to verify information or track changes
Bulk Operations — Effortlessly manage multiple records by providing structured data to your agent

The Knack 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 Knack to Pydantic AI via MCP

Follow these steps to integrate the Knack 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 Knack with type-safe schemas

Why Use Pydantic AI with the Knack MCP Server

Pydantic AI provides unique advantages when paired with Knack 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 Knack integration code

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

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

Knack + Pydantic AI Use Cases

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

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

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

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

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

Knack MCP Tools for Pydantic AI (10)

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

create_record

You must provide the data as a JSON string where keys are the field keys (e.g., field_1). Ensure you have checked the object schema first to know which fields are required. Create a new record in a Knack object

delete_record

Use with caution as this action cannot be undone. Delete a record from a Knack object

get_object_schema

Returns metadata including the object name, key, and high-level structure. Use this to verify you are working with the correct database table. Get the schema of a specific Knack object

get_record

Requires both the object_key and the record_id. Use this for detailed auditing of a specific entry. Get a specific record by ID

list_account_applications

Use this to verify access or discover application IDs. List all applications in the account

list_object_fields

This is crucial for understanding the data types and identifying the field keys (field_1, field_2, etc.) needed for creating or updating records. List all fields for a specific Knack object

list_objects

This is the first step to understand the database structure and find the "Object Key" needed for record operations. List all objects in the Knack application

list_records

You must provide the object_key. Use this to browse the actual data stored in your database. List records for a specific Knack object

search_records

The filters must be provided as a JSON string following the Knack Filter format (e.g., "[{\"field\":\"field_1\", \"operator\":\"is\", \"value\":\"test\"}]"). Search for records using filters

update_record

Provide only the fields you wish to change in the JSON string data. This is a partial update. Update an existing record in a Knack object

Example Prompts for Knack in Pydantic AI

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

"List all database objects in my Knack app"

"Find all premium customers in 'object_1'"

"Create a new customer in 'object_1' with name 'Sarah' and email 'sarah@example.com'"

Troubleshooting Knack MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Knack + Pydantic AI FAQ

Common questions about integrating Knack 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 Knack MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.