Compatible with every major AI agent and IDE
Add comment on Doodle
Add a comment to a Doodle poll
Add participant on Doodle
Provide a name and preference array (0=no, 1=yes, 2=if-need-be) matching option quantities. Add a participant vote to a Doodle poll programmatically
Close poll on Doodle
Overrides the core settings dictating finally which exact option value string won. Close a Doodle poll and set the final chosen option
Create poll on Doodle
Participants will be invited to vote on their preferred options. Create a new Doodle poll for group scheduling
Delete poll on Doodle
Drops the raw data out of the system returning completely blank state. Permanently delete a Doodle poll and all associated participant votes and comments
Get comments on Doodle
Retrieve all comments on a Doodle poll
Get poll on Doodle
Retrieve detailed information for a specific Doodle poll by ID
List participants on Doodle
List all participants who voted on a Doodle poll
List polls on Doodle
Returns poll titles, states (OPEN/CLOSED), creation dates, number of participants, and chosen final options. List all Doodle polls created by the authenticated user
Remove participant on Doodle
The core system inherently recalculates the total votes autonomously. Remove a participant and their votes from a Doodle poll
How Vinkius protects your data
Can I set different limits for each virtual assistant on my team?
Absolutely. You have full control in our command center. You can create an AI agent that only "reads" data so the support team can answer questions, and another superpowered agent that can "edit" and "create" information exclusively for your operations team. Each AI gets exactly the level of access you allow.
What happens if the underlying API rate limits my agent?
Our edge infrastructure automatically handles backoffs, queueing, and throttling. If an AI agent sends too many erratic requests, Vinkius manages the rate limits gracefully, ensuring your backend doesn't crash.
What if the AI ends up reading customer data or confidential information?
We have a built-in digital "bodyguard" called DLP (Data Loss Prevention). If a tool fetches data and the response contains social security numbers, credit cards, or personal customer info, Vinkius magically blocks and erases that information before it is delivered to the AI. The AI works only with what is strictly necessary, and your sensitive data never leaks.
Can I close a poll and set the final meeting time through the agent?
Absolutely. Use the 'close_poll' tool. Provide the poll ID and the text of the winning option. The agent will change the poll state to CLOSED, locking the participation arrays and officially confirming the chosen time.
Doodle Capabilities for AI Assistants
Securely interface Claude Code, ChatGPT, and Cursor with the Doodle API through semantic routing and standardized natural language triggers.
Intelligent meeting polls Management
The Doodle server exposes documented endpoints for meeting polls. This allows ChatGPT and Cursor to interact with productivity APIs seamlessly.
Connecting group scheduling with Cursor
Use the Doodle MCP to manage group scheduling requests. Models like Claude Code utilize this connection to perform reliable productivity updates.
Doodle. Runs on everything.
From IDE to framework. Every connection governed by Vinkius.
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
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