How to Use the Asana MCP in LlamaIndex

Q: How does LlamaIndex index the output of Asana tools?

The framework wraps tools like gettask in an McpToolSpec. When called, the text output is parsed into a Document object, which your indexer embeds and stores in your vector database.

Q: Can I use Asana tag data to filter LlamaIndex queries?

Yes, you pull tags using listtags and apply them as metadata filters in your LlamaIndex vector store. This lets you restrict your agent's search to tasks marked with specific labels.

Q: How do I keep my LlamaIndex vector store updated with Asana changes?

You run a periodic indexing pipeline that calls listtasks to fetch recent updates. The pipeline compares the task IDs against your index and updates modified documents using gettask.

Index your Asana workspaces into LlamaIndex vector stores to ground your agent's decisions in live project data.

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Connect Asana MCP to LlamaIndex

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Setup Asana with LlamaIndex

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Ground your RAG pipelines in live Asana tasks

The `list_tasks` tool allows LlamaIndex to pull task payloads and convert them into document nodes for your vector index. This lets your agent query past project work to answer questions about current timelines. By combining this live data with your static documentation, the agent avoids hallucinating details about active deliverables.

Build semantic search indexes for this MCP Server

Use `search_tasks` inside a LlamaIndex query engine to locate tickets matching natural language queries. The engine converts the search results into indexable text, making historical project decisions searchable. This setup turns your active ticket backlog into a structured knowledge base that your agent can reference during multi-document reasoning steps.

Map project structures automatically with this MCP Server

The `list_projects` and `list_sections` tools feed the structural layout of your workspaces directly into LlamaIndex index metadata. This MCP Server ensures your agent reads this hierarchy to understand how tasks relate to specific milestones. This structural context ensures that when the agent calls `create_task`, it places the new ticket in the correct section based on semantic relevance.

Setup guide

Set up Asana 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 Asana 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 Asana tools.",
)
response = await agent.run("List recent Asana data")

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Common questions about Asana MCP in LlamaIndex

The framework wraps tools like `get_task` in an `McpToolSpec`. When called, the text output is parsed into a Document object, which your indexer embeds and stores in your vector database.

Yes, you pull tags using `list_tags` and apply them as metadata filters in your LlamaIndex vector store. This lets you restrict your agent's search to tasks marked with specific labels.

You run a periodic indexing pipeline that calls `list_tasks` to fetch recent updates. The pipeline compares the task IDs against your index and updates modified documents using `get_task`.

Absolutely. If a query reveals a missing step in your documentation, the agent uses `create_task` to log a new ticket directly in the relevant project.

Yes, all task titles, project names, and section structures are processed locally or within your private cloud environment. The V8 sandbox ensures that your credentials and API payloads are never exposed to external networks.

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