How to Use the AdaptiveWork (Clarizen) MCP in LlamaIndex

Index your project knowledge base in LlamaIndex by grounding answers in real-time AdaptiveWork (Clarizen) API data.

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Connect AdaptiveWork (Clarizen) MCP to LlamaIndex

Create your Vinkius account to connect AdaptiveWork (Clarizen) 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 AdaptiveWork (Clarizen) with LlamaIndex

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Vectorize AdaptiveWork (Clarizen) data for LlamaIndex

Convert your project metadata into searchable vector embeddings. By running `list_projects` and indexing the results, your RAG system stops guessing project states and starts querying live records. This turns your static documentation into a dynamic index. Your agent retrieves specific project details from the vector store and references the `get_project_details` output to verify the current progress.

Automate search with LlamaIndex

Give your agent the ability to look up task lists based on natural language requests. When a user asks about a project, the agent uses `list_tasks` to fetch the latest items for the context window. The results are immediately indexed to provide grounded responses. You avoid hallucinations because the agent relies on the retrieved API data to form its final answer.

Query custom project fields in LlamaIndex

Execute complex CZQL commands using `run_query` to extract specific project attributes. The retrieved data is then pushed into your knowledge base for future retrieval. You gain a unified view of your portfolio. By combining standard tool outputs with custom query results, your index reflects the exact state of your organizational data.

Setup guide

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

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Common questions about AdaptiveWork (Clarizen) MCP in LlamaIndex

The server retrieves your project records and feeds them into your vector index. This allows your agent to perform semantic searches over your actual task lists.

You can. Once the project list is indexed, your agent queries the vector store to find relevant projects based on your specific questions.

It does. You can restrict the agent to only use specific tools like `list_projects` or `list_tasks` to control exactly what data enters your index.

You trigger a re-index of the tool output whenever your project data changes. This ensures your RAG system always reflects the latest task status.

We secure the connection through a dedicated Vinkius endpoint. Only the task and project records you explicitly query are accessible to your local index.

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