How do I authenticate?

You need a Lattice API Key configured via Admin settings, passed into the plugin's configuration.

Can I update OKRs from this server?

Currently the server is heavily optimized for reading OKRs, directories, and feedback.

Is employee personal data secure?

Yes, queries route directly from your Vurb client strictly and securely into the Lattice native endpoints. Nothing is stored intermediately.

Lattice MCP Server for LlamaIndex 9 tools — connect in under 2 minutes

Name: Lattice
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 9 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Lattice as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Lattice. "
            "You have 9 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Lattice?"
    )
    print(response)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Lattice MCP Server

Connect your AI agent directly to Lattice HR. With this server, your LLM can fetch detailed employee profiles, active OKRs, tasks, and search continuous feedback loops directly tied to the Lattice platform.

LlamaIndex agents combine Lattice tool responses with indexed documents for comprehensive, grounded answers. Connect 9 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

Employee Directory: Fetch user metadata directly from your HRIS via Lattice.
Goal Tracking: Query active company or individual OKRs and assess progress.
Feedback & Praise: Monitor continuous feedback loops and recognition events.
Review Cycles: Check past and current performance review structural data.

The Lattice MCP Server exposes 9 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Lattice to LlamaIndex via MCP

Follow these steps to integrate the Lattice MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 9 tools from Lattice

Why Use LlamaIndex with the Lattice MCP Server

LlamaIndex provides unique advantages when paired with Lattice through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Lattice tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Lattice tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Lattice, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Lattice tools were called, what data was returned, and how it influenced the final answer

Lattice + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Lattice MCP Server delivers measurable value.

Hybrid search: combine Lattice real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Lattice to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Lattice for fresh data

Analytical workflows: chain Lattice queries with LlamaIndex's data connectors to build multi-source analytical reports

Lattice MCP Tools for LlamaIndex (9)

These 9 tools become available when you connect Lattice to LlamaIndex via MCP:

get_feedback

Get details about a specific feedback entry

get_goal

Get targeted details for a specific goal

get_review

Get details regarding a specific review cycle

get_user

Get details for a specific Lattice employee

list_feedback

Retrieve a list of feedback and praise instances

list_goals

Retrieve a list of all OKRs & Goals

list_reviews

Retrieve a list of performance review cycles

list_tasks

Retrieve pending tasks

list_users

Retrieve a list of employees/users from Lattice

Example Prompts for Lattice in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Lattice immediately.

"List all the current engineering OKRs mapped within Lattice."

"Retrieve the full team employee directory for the Marketing division."

"Who received recent public praise and continuous feedback this week?"

Troubleshooting Lattice MCP Server with LlamaIndex

Common issues when connecting Lattice to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Lattice + LlamaIndex FAQ

Common questions about integrating Lattice MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Lattice tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.