How to Use the Doodle MCP in LlamaIndex

Q: How does LlamaIndex handle Doodle poll results?

It treats the output of listpolls as a data source. You index these results so your agent can query them for scheduling patterns.

Q: Is deletepoll safe for my LlamaIndex index?

You should clear the index after calling deletepoll. Removing the poll from the server also means you should prune the stale data from your vector store.

Index your Doodle scheduling data into your LlamaIndex knowledge base for smarter, grounded meeting decisions.

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Connect Doodle MCP to LlamaIndex

Create your Vinkius account to connect Doodle to LlamaIndex 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 Doodle with LlamaIndex

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Index poll data with LlamaIndex

Convert `list_polls` output into your vector store. Your agent now references past scheduling history to inform future meeting preferences. It turns raw API responses into searchable context. You stop guessing which time slots worked previously and let the index provide the answer.

Query participant comments

Ingest `get_comments` data to understand participant feedback trends. LlamaIndex maps these threads to your existing documents for a complete view of the planning phase. Your agent answers questions based on what people actually said in the polls. It pulls this context automatically whenever a new scheduling request arrives.

Ground scheduling in actual data

Verify poll status via `get_poll` before making any final commitment. By grounding your agent in live API results, you prevent hallucinations about meeting availability. It checks the current state of every poll against your indexed records. If the data shows a conflict, the agent knows immediately.

Setup guide

Set up Doodle MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Doodle MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Doodle tools.",
)
response = await agent.run("List recent Doodle data")

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Common questions about Doodle MCP in LlamaIndex

It treats the output of `list_polls` as a data source. You index these results so your agent can query them for scheduling patterns.

Absolutely. Once the server returns the poll data, LlamaIndex vectorizes it. You can then ask about previous meeting outcomes or common availability issues.

It does. You merge the API output with your local documents. This creates a unified knowledge base for your scheduling agent.

You should clear the index after calling `delete_poll`. Removing the poll from the server also means you should prune the stale data from your vector store.

Data is retrieved via a single endpoint token. Once it enters your LlamaIndex pipeline, it remains within your local environment and is never shared.

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