How to Use the Cobot MCP in LlamaIndex

Q: Can I filter which Cobot tools LlamaIndex can access?

Yes, you can use the allowedtools filter during setup to restrict the agent. For instance, you can expose resource listings while hiding billing tools.

Index your Cobot space data directly into LlamaIndex to query bookings and memberships with semantic search.

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Connect Cobot MCP to LlamaIndex

Create your Vinkius account to connect Cobot 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 Cobot with LlamaIndex

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Turn live booking data into a searchable index

By connecting this MCP Server to LlamaIndex, your agent indexes the raw output of `list_space_bookings` and `list_space_resources` directly into your vector store. Users can ask when the conference room is free using natural language. The agent searches the indexed tool outputs to give an accurate, hallucination-free answer based on real-time space availability.

Build RAG pipelines for membership inquiries

This MCP Server reads active accounts via `list_cobot_memberships` and details from `get_membership_details` to help LlamaIndex answer tricky billing questions. Instead of hardcoding API queries, your agent uses semantic search over the retrieved membership data to find the exact plan details. This keeps your responses grounded in your actual Cobot records.

Analyze space utilization with LlamaIndex and MCP

Your LlamaIndex agent pulls historical data using `list_space_bookings` and maps it against `list_membership_plans` to analyze space utilization. Because LlamaIndex excels at synthesizing structured data, you get clear summaries of which resources are bottlenecked. It helps you make data-driven decisions about your coworking layout.

Setup guide

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

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Common questions about Cobot MCP in LlamaIndex

Install the llama-index-tools-mcp package and initialize the client with your Vinkius endpoint. Convert the tools using McpToolSpec and pass them to your agent to start querying space information.

Yes, the agent can call the invoice listing tool to pull billing histories and index them. You can then query the index to find outstanding balances or payment trends.

It grounds its answers directly in the structured JSON returned by tools like membership details. The agent only uses the retrieved context, ensuring it doesn't invent fake memberships.

Yes, you can use the allowed_tools filter during setup to restrict the agent. For instance, you can expose resource listings while hiding billing tools.

Your coworking plans and resource schedules are fetched via secure, token-authorized requests through the MCP Server. LlamaIndex processes this data locally or within your private vector database, ensuring no raw credentials leak.

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