How to Use the KanbanTool MCP in LlamaIndex

Q: How can I use LlamaIndex to search my KanbanTool tasks?

You wrap the MCP client in a McpToolSpec and use it to build a data loader. LlamaIndex will call tools like listboardtasks to ingest the data. Once indexed, your agent can query your project history with natural language.

Q: How does LlamaIndex use the KanbanTool MCP Server tools?

The McpToolSpec exposes the KanbanTool tools, like getboarddetails, to your LlamaIndex application. Your agent can then call these tools to fetch fresh data for indexing or to answer a direct query.

Turn your KanbanTool history into a searchable knowledge base with LlamaIndex. Ask questions about any task, board, or update.

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Create your Vinkius account to connect KanbanTool 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 KanbanTool with LlamaIndex

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Index Your Entire Project History

This tool pack lets you build a real knowledge base from your work. Use the `McpToolSpec` to give LlamaIndex access to the KanbanTool MCP Server. Your app can then periodically run `list_boards`, `list_board_tasks`, and `get_task_details` to ingest all your project data into a vector index. Once indexed, you can ask your RAG application complex questions like, "What was the blocker on the payment gateway feature last quarter?" It will find the answer by searching the indexed output of `list_task_activities`.

Find Related Tasks Semantically

Keyword search is obsolete. After indexing all your tasks using `list_board_tasks` and their content via `get_task_details`, you get true semantic search. Your agent understands context, not just text matching. Ask it, "Show me work related to our authentication refactor," and it will find cards that mention 'OAuth', 'SSO', or 'user login'—even if your query didn't include those words. This helps you spot duplicate work or find subject matter experts instantly.

Query Board Configurations with LlamaIndex

Documenting your workflow setup is a pain. Instead, just index it. Have your LlamaIndex app ingest the output of `get_board_details` for all your boards. This captures every column name, WIP limit, and description in your vector database. Now your team can ask the agent, "Which boards have a 'Code Review' column?" or "What's the WIP limit on the 'Mobile App' board?" You get instant, accurate answers grounded in your live configuration.

Setup guide

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

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Common questions about KanbanTool MCP in LlamaIndex

You wrap the MCP client in a `McpToolSpec` and use it to build a data loader. LlamaIndex will call tools like `list_board_tasks` to ingest the data. Once indexed, your agent can query your project history with natural language.

Yes, that's what it's built for. After you index the data from `get_task_details`, LlamaIndex finds tasks based on semantic meaning. It connects concepts, so you don't need to remember specific keywords.

You control the updates. You can set up an ingestion pipeline to periodically run tools like `list_task_activities` and `get_board_details`, keeping your LlamaIndex knowledge base in sync with your live KanbanTool boards.

The `McpToolSpec` exposes the KanbanTool tools, like `get_board_details`, to your LlamaIndex application. Your agent can then call these tools to fetch fresh data for indexing or to answer a direct query.

The MCP Server itself stores nothing. Your LlamaIndex application pulls data—like task descriptions and board metadata—and stores it in your own vector database. You have full control over where that indexed content is stored and how it's secured.

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