How to Use the Basecamp MCP in LlamaIndex

Q: How does LlamaIndex know which project to query?

The agent first runs listprojects via the MCP Server to map your natural language query to the correct project ID. Once identified, it queries the specific tools for that project.

Q: Can I build a Q&A bot with LlamaIndex?

You can use getproject and listmessages to feed context into a conversational agent, allowing team members to ask questions about project status in plain English.

Index your Basecamp messages, projects, and to-do lists directly into your LlamaIndex knowledge base for real-time RAG.

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Connect Basecamp MCP to LlamaIndex

Create your Vinkius account to connect Basecamp 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 Basecamp with LlamaIndex

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Index Basecamp Message Boards for LlamaIndex RAG

The `list_messages` tool retrieves all conversations from your project boards so your agent can index them. LlamaIndex takes this text, chunks it, and stores it in your vector database for semantic search. Instead of searching Basecamp manually, you query your LlamaIndex agent. It uses `get_message` to pull full threads and ground its answers in actual team discussions, eliminating hallucinations.

Query Basecamp To-dos Using LlamaIndex MCP Server

The `list_todos` tool fetches all active tasks, which LlamaIndex indexes alongside your document libraries. This lets you ask questions like 'What tasks are blocking the release?' and get answers mapped to actual pending items. If the agent needs to verify a specific task's status, it calls `get_todo` to pull the latest comments and due dates. Your RAG pipeline stays updated with real-time project states.

Semantic Search Over Team Profiles and Projects

The `list_people` tool extracts your team roster, allowing LlamaIndex to build a searchable directory of expertise. The agent queries this index to find who is working on what before assigning new tasks. When creating a task, the agent uses `get_person` to verify availability. It then calls `create_todo` to place the task in the correct project list, keeping your workspace organized.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Basecamp. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Basecamp MCP in LlamaIndex

Use the MCP Server to fetch data. Your LlamaIndex pipeline calls `list_messages` or `list_todos`, converts the JSON responses into Document objects, and loads them into your vector store.

Yes. If your LlamaIndex agent finds a resolved issue in your local files, it can call `complete_todo` to mark the corresponding task as done in Basecamp.

The agent first runs `list_projects` via the MCP Server to map your natural language query to the correct project ID. Once identified, it queries the specific tools for that project.

You can use `get_project` and `list_messages` to feed context into a conversational agent, allowing team members to ask questions about project status in plain English.

Your comments, messages, and task descriptions are transmitted over secure, encrypted channels. Vinkius hosts the MCP Server in an ephemeral sandbox, meaning your sensitive team communications are never stored on our platform.

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