How to Use the Skalin MCP in LlamaIndex

Q: How does LlamaIndex use Skalin for account history?

You take the raw JSON output from listaccountinteractions and index it. When a user asks a question, your agent searches that indexed data to provide a grounded answer.

Q: Can LlamaIndex query Skalin's active alerts?

Yes. You can ingest the output of listcsalerts into a vector store. This lets you ask complex questions like, 'List all high-severity alerts related to finance.' The answer comes directly from the indexed data.

Q: What if I want to check account health with LlamaIndex?

You call getaccounthealth, take that result (the status object), and index it. This allows users to later query, 'What is the historical trend of Account XYZ's health?'

Q: How does LlamaIndex handle calling success tasks?

You run listcstasks, take the list of tasks, and add them to your knowledge base. Your agent can then answer questions about specific task deadlines or owners based on that indexed information.

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Setup Skalin with LlamaIndex

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Creating a Knowledge Base from Account Metrics

Don't just read usage data; index it. Run `get_account_metrics` for several accounts, then feed those structured results into your knowledge base. Your RAG app can then answer questions like, 'Which account is showing high API usage?' based on real numbers. This means you query the actual Skalin data, not just a summary of it.

Searching Account History and Alerts

Index the raw output from `list_account_interactions` and `list_cs_alerts`. Now, instead of reading through 50 pages of logs, you ask your agent: 'Why did Acme Corp get an alert last month?' The search engine finds the exact log entry. This turns historical API calls into immediate, queryable knowledge.

Documenting and Querying Account Setup

Use `create_cs_account` to make a new account record. Index the resulting metadata immediately. Later, you can ask your agent questions about that specific setup—like 'What were the initial metrics for this account?'—and it pulls the answer from the index. It's documentation and data combined into one searchable resource.

Setup guide

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

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Common questions about Skalin MCP in LlamaIndex

You take the raw JSON output from `list_account_interactions` and index it. When a user asks a question, your agent searches that indexed data to provide a grounded answer.

Yes. You can ingest the output of `list_cs_alerts` into a vector store. This lets you ask complex questions like, 'List all high-severity alerts related to finance.' The answer comes directly from the indexed data.

You call `get_account_health`, take that result (the status object), and index it. This allows users to later query, 'What is the historical trend of Account XYZ's health?'

The server exposes account details like usage metrics and contacts. When you index this, your RAG app keeps the answers grounded in that source data.

You run `list_cs_tasks`, take the list of tasks, and add them to your knowledge base. Your agent can then answer questions about specific task deadlines or owners based on that indexed information.

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