AdaptiveWork (Clarizen) MCP Server for LlamaIndex 6 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add AdaptiveWork (Clarizen) as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to AdaptiveWork (Clarizen). "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in AdaptiveWork (Clarizen)?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About AdaptiveWork (Clarizen) MCP Server
Connect your AdaptiveWork (formerly Clarizen) account to your AI agent to unlock enterprise-grade project and portfolio management. From tracking high-level project health to creating granular tasks and managing resource availability, your agent handles complex workflows through natural conversation.
LlamaIndex agents combine AdaptiveWork (Clarizen) tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Project Portfolio Management — List and audit active projects, check health statuses, and retrieve executive summaries
- Task Orchestration — Create, assign, and update tasks across your project structure to ensure team alignment
- Resource Insights — List organization users and check assignments to optimize team capacity
- Advanced Querying (CZQL) — Run custom Clarizen Query Language commands to retrieve specific data subsets for reporting
- Portfolio Health — Quickly identify project bottlenecks or overdue milestones directly from your chat interface
The AdaptiveWork (Clarizen) MCP Server exposes 6 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect AdaptiveWork (Clarizen) to LlamaIndex via MCP
Follow these steps to integrate the AdaptiveWork (Clarizen) MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 6 tools from AdaptiveWork (Clarizen)
Why Use LlamaIndex with the AdaptiveWork (Clarizen) MCP Server
LlamaIndex provides unique advantages when paired with AdaptiveWork (Clarizen) through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine AdaptiveWork (Clarizen) tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain AdaptiveWork (Clarizen) tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query AdaptiveWork (Clarizen), a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what AdaptiveWork (Clarizen) tools were called, what data was returned, and how it influenced the final answer
AdaptiveWork (Clarizen) + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the AdaptiveWork (Clarizen) MCP Server delivers measurable value.
Hybrid search: combine AdaptiveWork (Clarizen) real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query AdaptiveWork (Clarizen) to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying AdaptiveWork (Clarizen) for fresh data
Analytical workflows: chain AdaptiveWork (Clarizen) queries with LlamaIndex's data connectors to build multi-source analytical reports
AdaptiveWork (Clarizen) MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect AdaptiveWork (Clarizen) to LlamaIndex via MCP:
create_task
You must provide a task name and its parent ID. Add a new granular task to a project or parent task structure in AdaptiveWork
get_project_details
Requires the Project ID. Retrieve detailed metadata and progress metrics for a specific AdaptiveWork project
list_projects
Can filter by state or status in the tool response natively. Retrieve a list of active projects managed within the AdaptiveWork organization
list_tasks
Requires the Project ID. Retrieve the active task list associated with a specific project container ID
list_users
Retrieve the list of active organization users in AdaptiveWork to check resource assignments
run_query
Requires valid CZQL syntax. Execute advanced Clarizen Query Language (CZQL) commands for custom data retrieval
Example Prompts for AdaptiveWork (Clarizen) in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with AdaptiveWork (Clarizen) immediately.
"List all active projects with a 'Critical' health status."
"Create a new task named 'Review Budget' in 'Project Alpha'."
"Run a CZQL query to find all tasks assigned to 'John Doe'."
Troubleshooting AdaptiveWork (Clarizen) MCP Server with LlamaIndex
Common issues when connecting AdaptiveWork (Clarizen) to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpAdaptiveWork (Clarizen) + LlamaIndex FAQ
Common questions about integrating AdaptiveWork (Clarizen) MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect AdaptiveWork (Clarizen) with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect AdaptiveWork (Clarizen) to LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.