How to Use the Sunsama MCP in LlamaIndex

Q: How does LlamaIndex help with Sunsama task data?

LlamaIndex takes the output from tools like listtasks and indexes it. This means you can query historical data—the specific details of old tasks—and get an answer that is directly supported by the API records, minimizing hallucinations.

Q: What’s the best way to start with Sunsama and LlamaIndex?

Start by running listtasks for a specific date range. Pass that output into your RAG system's indexer. This immediately grounds your knowledge base in actionable, live data.

Build knowledge-augmented RAG apps using the Sunsama MCP Server and LlamaIndex.

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Connect Sunsama MCP to LlamaIndex

Create your Vinkius account to connect Sunsama to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Sunsama with LlamaIndex

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Ground answers in live task data.

Stop relying on memory. With `list_tasks()`, you can retrieve all current project items, then index that raw text into your vector store. Your AI client can then query past session tasks and get answers grounded in actual Sunsama API data. This makes the MCP Server output part of a searchable knowledge base. You’re building RAG applications where live task status is combined with static documents.

Structure workflows using detailed context retrieval.

Need to know what tools are available? Use `list_channels()` and `list_contexts()` to gather system parameters. LlamaIndex indexes these lists, so you can query things like: 'What channels exist for the marketing team?' This provides semantic search over your API structure, which is way better than just passing tool names.

Maintain historical records of user activity.

You can use `get_user_profile()` to retrieve current user details. By indexing this output, you create a persistent record that answers questions like: 'What is the default profile for Project Alpha?' It’s about turning transient API responses into searchable knowledge assets for your RAG system.

Setup guide

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

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Common questions about Sunsama MCP in LlamaIndex

LlamaIndex takes the output from tools like `list_tasks` and indexes it. This means you can query historical data—the specific details of old tasks—and get an answer that is directly supported by the API records, minimizing hallucinations.

Yes. You combine live API calls with your documents. For example, you can index the output of `get_task_details` alongside internal company policies to give one unified answer.

The server touches task text and planned dates. When building your RAG applications, the user must explicitly define which fields from `get_task_details` are allowed to be indexed into the vector store.

It is. Standard calls just execute; LlamaIndex allows you to treat those executions as knowledge. You get a searchable record of what happened, not just the current status.

Start by running `list_tasks` for a specific date range. Pass that output into your RAG system's indexer. This immediately grounds your knowledge base in actionable, live data.

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