How to Use the Lattice MCP in LlamaIndex

Q: Can I search Lattice feedback using LlamaIndex?

Yes. Once you ingest the data from listfeedback into your index, you can perform semantic searches across all entries.

Q: Is it possible to filter Lattice tools in LlamaIndex?

You can include or exclude specific tools like getuser during the setup phase. This gives you control over what the agent accesses.

Q: How does the Lattice MCP server handle privacy?

The server only reads the specific listusers and listfeedback data you request. Your data remains ephemeral and is never stored by the server itself.

Index your Lattice HR data into searchable knowledge bases using LlamaIndex for grounded AI answers.

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Connect Lattice MCP to LlamaIndex

Create your Vinkius account to connect Lattice 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 Lattice with LlamaIndex

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Claude

Perplexity

Ground AI answers in Lattice data

Transform raw HR records into searchable vectors. Your LlamaIndex application calls `list_users` and `list_goals` to build a semantic index of your company's performance landscape. Querying this index produces grounded answers. The agent no longer guesses because it references the actual data retrieved from your Lattice instance.

Query historical feedback with LlamaIndex

Make past praise and feedback searchable. By running `list_feedback` through your ingestion pipeline, you turn static entries into a knowledge base for your agents. Retrieval becomes a semantic task. You ask questions about team sentiment, and the system finds the relevant feedback entries using vector search.

Sync review cycles into your knowledge graph

Keep your LlamaIndex knowledge base current with active review cycles. The agent uses `list_reviews` to identify updates and fetches details with `get_review`. This keeps your RAG application relevant. You avoid stale information by indexing the most recent records directly from the HR platform.

Setup guide

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

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Common questions about Lattice MCP in LlamaIndex

Use the tool specification to call the server functions. Convert the output of `list_tasks` or `list_goals` into documents for your vector store.

No. Your credentials exist only in the environment configuration. The client uses your token for each request to the server.

Yes. Once you ingest the data from `list_feedback` into your index, you can perform semantic searches across all entries.

You can include or exclude specific tools like `get_user` during the setup phase. This gives you control over what the agent accesses.

The server only reads the specific `list_users` and `list_feedback` data you request. Your data remains ephemeral and is never stored by the server itself.

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