How to Use the Asana MCP in LangChain

Q: Can a LangChain agent search across multiple Asana workspaces?

Yes, by chaining listworkspaces with searchtasks. Your LangChain agent runs the workspace list first, then loops through each ID to locate specific tickets based on your prompt.

Q: How do I pass Asana tool outputs directly to the next LangChain step?

You define a LangGraph state that accepts the output of gettask or listprojects. The next node in your LangChain graph reads this state directly to make routing decisions.

Q: Can I restrict which Asana tools my LangChain agent can access?

Yes, you can filter the list returned by client.gettools() before passing them to your LangChain agent. If you only want to allow read operations, pass listtasks and omit updatetask.

Run multi-step project workflows by linking Asana tools directly into your LangChain reasoning loops.

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Connect Asana MCP to LangChain

Create your Vinkius account to connect Asana to LangChain 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 Asana with LangChain

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Claude

Perplexity

Build self-correcting Asana chains in LangChain

The `search_tasks` tool lets your LangChain agent find existing tickets before creating duplicates. If the search returns a match, the agent routes the data to `update_task` instead of spawning a new ticket, keeping your project board clean. You can trace this entire decision tree in LangSmith to see exactly why your chain chose to update an Asana task instead of writing a new one. This keeps your agent's reasoning visible and predictable.

Map workspaces with this MCP Server

Your pipeline uses `list_workspaces` and `list_projects` to dynamically discover where incoming work should live. This MCP Server maps your team's current structure without hardcoded IDs. You can then run a single LangChain runnable that inspects the workspace, finds the right project, and calls `list_sections` to place the task in the correct column.

Automate task audits inside your chains

The `list_stories` tool pulls the entire comment history of any ticket directly into your LLM prompt context. Your chain can analyze these updates to write a summary, then call `update_task` to post the status change. Because LangChain handles tool outputs as raw text or structured documents, you feed these stories straight into your summary templates without manual parsing.

Setup guide

Set up Asana MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Asana tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "asana-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Asana transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Asana MCP in LangChain

Use the LangChain rate limiter wrapper around the MCP client. The server returns standard rate limit headers when you run calls like `list_tasks`, allowing your chain to back off and retry automatically.

Yes, by chaining `list_workspaces` with `search_tasks`. Your LangChain agent runs the workspace list first, then loops through each ID to locate specific tickets based on your prompt.

You define a LangGraph state that accepts the output of `get_task` or `list_projects`. The next node in your LangChain graph reads this state directly to make routing decisions.

Yes, you can filter the list returned by `client.get_tools()` before passing them to your LangChain agent. If you only want to allow read operations, pass `list_tasks` and omit `update_task`.

Your task descriptions, project names, and workspace structures remain inside the Vinkius secure sandbox. The server only communicates with the official API, meaning your private project data never touches external third-party logging servers.

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