CrewAI Platform MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add CrewAI Platform as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
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 CrewAI Platform. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in CrewAI Platform?"
)
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 CrewAI Platform MCP Server
Connect your CrewAI Platform (AMP) account to any AI agent and take full control of your autonomous multi-agent orchestration through natural conversation.
LlamaIndex agents combine CrewAI Platform tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- Crew Management — List all deployed multi-agent workflows and extract pure JSON blueprints mapping the complete agent graph topology
- Autonomous Kickoffs — Activate multi-agent processing immediately by triggering crews with dynamic JSON inputs to start complex workflows
- Live Run Monitoring — Retrieve disconnected physical states of active executions, tracking agents as they complete sequential or parallel tasks
- Agent & Task Auditing — Enumerate isolated role-playing agents and globally registered modular operations to verify backstories and expected outcomes
- Execution Control — Dispatch instant interrupt signals to hard-stop active runs and manage internal LLM context boundaries
- Webhook Oversight — Inspect exact validation criteria for async results and monitor where Crew outcomes post standard JSON boundaries
The CrewAI Platform MCP Server exposes 10 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 CrewAI Platform to LlamaIndex via MCP
Follow these steps to integrate the CrewAI Platform 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 10 tools from CrewAI Platform
Why Use LlamaIndex with the CrewAI Platform MCP Server
LlamaIndex provides unique advantages when paired with CrewAI Platform through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine CrewAI Platform tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain CrewAI Platform tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query CrewAI Platform, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what CrewAI Platform tools were called, what data was returned, and how it influenced the final answer
CrewAI Platform + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the CrewAI Platform MCP Server delivers measurable value.
Hybrid search: combine CrewAI Platform real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query CrewAI Platform 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 CrewAI Platform for fresh data
Analytical workflows: chain CrewAI Platform queries with LlamaIndex's data connectors to build multi-source analytical reports
CrewAI Platform MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect CrewAI Platform to LlamaIndex via MCP:
cancel_run
Inspect deep internal arrays mitigating specific Plan Math
get_agent
Enumerate explicitly attached structured rules exporting active Billing
get_crew
Perform structural extraction of properties driving active Account logic
get_run_status
Retrieve explicit Cloud logging tracing explicit Vault limits
get_task
Identify precise active arrays spanning native Gateway auth
kickoff_crew
Provision a highly-available JSON Payload generating hard Customer bindings
list_agents
Irreversibly vaporize explicit validations extracting rich Churn flags
list_crews
Identify bounded CRM records inside the Headless CrewAI Platform
list_tasks
Dispatch an automated validation check routing explicit Gateway history
list_webhooks
Identify precise active arrays spanning native Hold parsing
Example Prompts for CrewAI Platform in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with CrewAI Platform immediately.
"List all crews in my account"
"Kickoff crew 'crew_abc' with input: {'topic': 'AI Trends 2024'}"
"What is the backstory of agent 'agent_789'?"
Troubleshooting CrewAI Platform MCP Server with LlamaIndex
Common issues when connecting CrewAI Platform to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCrewAI Platform + LlamaIndex FAQ
Common questions about integrating CrewAI Platform 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 CrewAI Platform 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 CrewAI Platform to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.