How to Use the Cloze MCP in LlamaIndex

Q: Can I build a RAG pipeline using Cloze data in LlamaIndex?

Yes. You can use listpeople and listprojects to pull CRM data, index it into a vector store, and query your Cloze records using LlamaIndex's standard query engines.

Q: How does LlamaIndex handle custom CRM fields from Cloze?

The listcustomfields tool retrieves your custom metadata schemas. LlamaIndex uses this data to tag your indexed documents, enabling precise metadata filtering during vector searches.

Q: How do I prevent hallucinations when querying Cloze projects?

By using listprojects directly within your LlamaIndex tool spec, the agent retrieves live deal data instead of guessing, ensuring every response is grounded in actual CRM facts.

Index live Cloze CRM contacts and deals directly into your LlamaIndex vector store using this MCP server for grounded RAG.

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Connect Cloze MCP to LlamaIndex

Create your Vinkius account to connect Cloze 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 Cloze with LlamaIndex

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Indexing CRM Contacts into LlamaIndex

The `list_people` tool extracts your entire contact database from Cloze to build a local vector index in LlamaIndex. Your agent queries this index to find relationships between contacts without hammering the live API. By feeding this output into a document store, you search through company roles and contact histories using semantic queries rather than rigid SQL filters.

Grounded Deal Queries via Cloze MCP Server

The `list_projects` tool pulls current real estate properties and business deals from Cloze into your LlamaIndex RAG pipeline. This gives your agent real-time context on open sales pipelines before answering user questions. The framework indexes these project descriptions, allowing you to ask natural language questions about your active pipeline and get answers backed by actual CRM records.

Semantic Tag Search Integration

The `search_people_by_tag` tool fetches specific contact segments from Cloze to populate your LlamaIndex query engines using this MCP tool. You filter by tag first, then apply semantic search over that specific subset of leads. This hybrid approach prevents the LLM from searching irrelevant contacts, saving token costs and speeding up response times during client lookups.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Cloze. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Cloze MCP in LlamaIndex

Yes. You can use `list_people` and `list_projects` to pull CRM data, index it into a vector store, and query your Cloze records using LlamaIndex's standard query engines.

The `list_custom_fields` tool retrieves your custom metadata schemas. LlamaIndex uses this data to tag your indexed documents, enabling precise metadata filtering during vector searches.

Yes. After searching your vector index, your LlamaIndex agent can use `update_person` to modify the live CRM record with new information found during the RAG process.

By using `list_projects` directly within your LlamaIndex tool spec, the agent retrieves live deal data instead of guessing, ensuring every response is grounded in actual CRM facts.

Your CRM contact details, project specs, and emails are processed in memory to build the LlamaIndex vector nodes. Vinkius runs this MCP server in a zero-trust sandbox, ensuring your customer data never leaks to external logs.

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