How to Use the Kippy MCP in LlamaIndex

Q: Can I filter Kippy tools in LlamaIndex?

Yes, use the allowedtools parameter to restrict access. This keeps your index focused on the performance data you actually need.

Q: How does LlamaIndex handle Kippy audit logs?

You can index listauditlogs as a temporal document. This lets you ask the agent about system changes over specific time windows.

Build a knowledge base from your performance data by indexing Kippy results with LlamaIndex.

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Connect Kippy MCP to LlamaIndex

Create your Vinkius account to connect Kippy 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 Kippy with LlamaIndex

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Perplexity

Semantic indexing for Kippy tools

LlamaIndex turns `list_project_scores` output into searchable vector data. You get answers grounded in your actual team performance history. Stop guessing performance trends. Instead, query your index to find patterns in `list_annual_scores` that simple reports miss.

Ground your agents in live data

Your agent queries the index rather than hallucinating. LlamaIndex pulls the latest `list_feedback` entries to ensure your RAG application is current. This approach gives your agent a memory of every appraisal. You get precise answers based on the most recent `list_appraisals` data available.

Unified knowledge retrieval

Combine `list_competencies` with your internal documents. LlamaIndex creates a single index for all your organizational data. Querying your agent feels like asking a human who knows the whole team. It pulls from Kippy logs and your own private files simultaneously.

Setup guide

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

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Common questions about Kippy MCP in LlamaIndex

Use the BasicMCPClient to connect and pass the tool spec to your FunctionAgent. It automatically converts the tools into a queryable format.

Yes, use the allowed_tools parameter to restrict access. This keeps your index focused on the performance data you actually need.

You control the refresh frequency. Since it's an MCP server, your agent calls the tools in real time to populate the latest index state.

You can index `list_audit_logs` as a temporal document. This lets you ask the agent about system changes over specific time windows.

Your data lives in your own vector store. LlamaIndex processes the records locally, keeping your performance metrics away from public models.

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