How to Use the Skylink MCP in LlamaIndex

Q: What kind of data does Skylink expose when paired with LlamaIndex?

It provides structured CRM data, including deal records (listdeals), lead timelines (getleadtimeline), and activity logs. This forms the basis of your knowledge base.

Build searchable knowledge bases using Skylink's API with LlamaIndex.

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Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers — Included with Plan

Connect Skylink MCP to LlamaIndex

Create your Vinkius account to connect Skylink to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Skylink with LlamaIndex

Ask AI about this MCP

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Claude

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Gemini

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Querying Past Lead Interactions

Instead of just listing leads, you can index them. Run `get_lead` data and store it in a vector store. Your agent then queries this knowledge base to answer complex questions about historical lead status. The output isn't just raw API data; it's answers grounded directly in Skylink's record-keeping.

Retrieving Account and Contact History

When you run `get_account` or `list_contacts`, the results become part of your index. This lets your agent answer, 'What were the key details from the account that generated this deal?' without writing complex filters. LlamaIndex combines live data with documents for a unified view.

Semantic Search for Tasks and Deals

You can't just filter tasks by name. By indexing the output of `list_tasks` or `get_deal`, your agent understands context. You ask, 'What was the deal that needed follow-up last month?' and it finds the right record. This moves beyond simple attribute matching.

Setup guide

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

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Why Choose Vinkius

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Common questions about Skylink MCP in LlamaIndex

It indexes API tool outputs, allowing your agent to perform semantic searches. You can ask complex questions and get answers grounded in the actual deal or contact records.

Absolutely. You use LlamaIndex to build RAG applications where internal documents (like playbooks) are combined with real-time data from tools like `get_call`.

It provides structured CRM data, including deal records (`list_deals`), lead timelines (`get_lead_timeline`), and activity logs. This forms the basis of your knowledge base.

Yes. It allows you to define a set of allowed tools, enabling the agent to call `list_contacts` and then use that indexed result in subsequent queries for deeper context.

The server touches contact details, lead information (including status), deal records, and activity logs. All of this can be indexed into your knowledge base.

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