How to Use the Kylas MCP in LlamaIndex

Q: How do I set up the Kylas MCP Server inside LlamaIndex?

Install the MCP tool package via pip install llama-index-tools-mcp. Then, initialize BasicMCPClient with your Vinkius URL and convert the tools using McpToolSpec before passing them to your agent.

Q: How does LlamaIndex handle rate limits when listing Kylas contacts?

The framework reads data in chunks when calling listcontacts to avoid hitting API rate limits. You configure the ingestion pipeline to index these records during off-peak hours to keep your system fast.

Q: Does this tool spec support resource loading for RAG?

Yes, you can enable includeresources=True on your MCP client to let LlamaIndex read raw CRM data as documents. This makes it easier to feed your sales pipelines directly into your vector indexes.

Build knowledge-augmented sales tools in LlamaIndex that index your Kylas CRM leads and deals for semantic search via MCP.

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Connect Kylas MCP to LlamaIndex

Create your Vinkius account to connect Kylas 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 Kylas with LlamaIndex

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Index Kylas lead data for LlamaIndex RAG

The `list_leads` tool feeds raw sales records directly into your vector database. This MCP Server lets your LlamaIndex pipeline pull live CRM data using `list_leads` and index it directly into your vector database. You get a searchable knowledge base grounded in actual customer interactions.

Search pipeline tasks and deals semantically

The `list_deals` tool extracts active pipeline stages for semantic indexing. Your framework reads your active deals using `list_deals` and stores them as document nodes. This lets sales reps query past deals using plain English to find patterns in successful sales.

Sync contacts directly into your vector store

The `list_contacts` tool gets your entire customer directory for vector embeddings. Your agent calls `list_contacts` to get your entire directory, converting the raw JSON into vector embeddings for instant retrieval. If a new lead comes in, the agent writes back to the CRM using `create_contact` to keep the database updated.

Setup guide

Set up Kylas 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 Kylas 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 Kylas tools.",
)
response = await agent.run("List recent Kylas 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 Kylas. 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 Kylas MCP in LlamaIndex

Install the MCP tool package via `pip install llama-index-tools-mcp`. Then, initialize `BasicMCPClient` with your Vinkius URL and convert the tools using `McpToolSpec` before passing them to your agent.

Yes, your agent searches your indexed vector store for context and then calls `create_lead` to add a new record. This lets you build interactive workflows that write back to your CRM based on search results.

The framework reads data in chunks when calling `list_contacts` to avoid hitting API rate limits. You configure the ingestion pipeline to index these records during off-peak hours to keep your system fast.

Yes, you can enable `include_resources=True` on your MCP client to let LlamaIndex read raw CRM data as documents. This makes it easier to feed your sales pipelines directly into your vector indexes.

Your sensitive lead names, phone numbers, and deal values are processed in an isolated sandbox that never retains your data. The API tokens are managed securely on Vinkius, meaning your vector store only receives the data you explicitly choose to index.

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