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Knackly MCP Server for Pydantic AIGive Pydantic AI instant access to 8 tools to Create Data Record, Get Record Details, List Catalogs, and more

Built by Vinkius GDPR 8 Tools SDK

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

Ask AI about this App Connector for Pydantic AI

The Knackly app connector for Pydantic AI is a standout in the Productivity category — giving your AI agent 8 tools to work with, ready to go from day one.

Vinkius delivers Streamable HTTP and SSE to any MCP client

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 Knackly "
            "(8 tools)."
        ),
    )

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

asyncio.run(main())
Knackly
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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 Knackly MCP Server

Connect your Knackly account to any AI agent and automate document generation through natural conversation.

Pydantic AI validates every Knackly tool response against typed schemas, catching data inconsistencies at build time. Connect 8 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

  • Template Management — Browse templates and their field configurations
  • Document Generation — Create documents from templates with field data
  • App Browsing — List all Knackly apps and their configurations
  • Generation History — Track document generation history and outputs

The Knackly MCP Server exposes 8 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.

All 8 Knackly tools available for Pydantic AI

When Pydantic AI connects to Knackly through Vinkius, your AI agent gets direct access to every tool listed below — spanning document-automation, template-assembly, legal-tech, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.

create_data_record

Add a new record

get_record_details

Get details for a specific record

list_catalogs

List catalogs in a workspace

list_data_models

List models in a catalog

list_data_records

List records for a model

list_generated_documents

List automated documents

list_webhooks

List configured webhooks

list_workspaces

List Knackly workspaces

Connect Knackly to Pydantic AI via MCP

Follow these steps to wire Knackly into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.

01

Install Pydantic AI

Run pip install pydantic-ai
02

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token
03

Run the agent

Save to agent.py and run: python agent.py
04

Explore tools

The agent discovers 8 tools from Knackly with type-safe schemas

Why Use Pydantic AI with the Knackly MCP Server

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

01

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

02

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

03

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

04

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

Knackly + Pydantic AI Use Cases

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

01

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

02

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

03

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

04

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

Example Prompts for Knackly in Pydantic AI

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

01

"Show all apps and templates available for document generation."

02

"Generate an NDA for Acme Corp and show the required fields."

03

"Show document generation history for this month."

Troubleshooting Knackly MCP Server with Pydantic AI

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

01

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Knackly + Pydantic AI FAQ

Common questions about integrating Knackly MCP Server with Pydantic AI.

01

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.
02

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.
03

Can I switch LLM providers without changing MCP code?

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