How to Use the 15Five MCP in LlamaIndex

Q: How do I load 15Five data into my LlamaIndex vector store?

You use the llama-index-tools-mcp package to initialize the MCP client. From there, the McpToolSpec converts the server tools into indexable data sources for your RAG pipeline.

Q: Can LlamaIndex query historical 15Five check-ins?

Yes, the agent calls listcheckins to retrieve the submissions and indexes them. You can then run semantic searches over past feedback to find specific team updates.

Q: How does the 15Five MCP Server handle department filtering in LlamaIndex?

The listdepartments tool exposes the team structure directly to your query pipeline. Your agent uses these structural details to filter search queries by department.

Q: What tools are available for peer appreciation in LlamaIndex?

The agent can use listhighfives to index public appreciation posts, or invoke sendhighfive to write a new one. This MCP tool lets your agent interact with peer recognition data directly.

Index your 15Five check-ins and OKRs into LlamaIndex to query performance trends with live data.

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Connect 15Five MCP to LlamaIndex

Create your Vinkius account to connect 15Five 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 15Five with LlamaIndex

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Index OKRs for Grounded RAG Applications

The `list_objectives` tool retrieves both company and individual OKRs to feed directly into your LlamaIndex vector store. This prevents your agent from hallucinating goals, grounding its answers in your actual corporate targets. Integrating this feed allows you to query your index about goal progress. You get factual, search-backed answers instead of generic guesses.

Query Team Check-ins via MCP Server Indexing

The `list_checkins` tool extracts recent employee submissions so you can index them for semantic search. LlamaIndex can run queries across months of check-ins to identify recurring roadblocks. Adding `list_users` to the pipeline helps map these indexed check-ins to specific employee profiles. This makes it simple to search for historical feedback from any team member.

Map Company Structure for Smart Retrieval

The `list_departments` tool provides the structural mapping of your organization, which LlamaIndex uses to categorize indexed data. It groups employees and their feedback into distinct vector namespaces. When you query the index about department performance, the agent retrieves context from the exact team namespace. This keeps search results highly relevant.

Setup guide

Set up 15Five 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 15Five 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 15Five tools.",
)
response = await agent.run("List recent 15Five data")

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Common questions about 15Five MCP in LlamaIndex

You use the `llama-index-tools-mcp` package to initialize the MCP client. From there, the `McpToolSpec` converts the server tools into indexable data sources for your RAG pipeline.

Yes, the agent calls `list_checkins` to retrieve the submissions and indexes them. You can then run semantic searches over past feedback to find specific team updates.

The `list_departments` tool exposes the team structure directly to your query pipeline. Your agent uses these structural details to filter search queries by department.

The agent can use `list_high_fives` to index public appreciation posts, or invoke `send_high_five` to write a new one. This MCP tool lets your agent interact with peer recognition data directly.

All employee check-ins and OKR details are processed within Vinkius's secure sandbox. Your active session tokens protect the raw API data, ensuring it remains private and isolated from unauthorized access.

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