How to Use the Lattice MCP in Mastra AI

Q: How do I register the Lattice MCP Server inside Mastra AI?

Instantiate MCPClient with your Vinkius HTTP URL. Then, list the tools and spread them into your Mastra agent configuration so it can immediately call listusers or getuser.

Q: Can Mastra AI agents automatically update goals in Lattice?

No. This server provides read-only access to tools like listgoals and getgoal, meaning your agents can monitor and analyze performance but cannot write changes back to your HR system.

Q: Does Mastra AI support human-in-the-loop approvals for Lattice data?

Yes. You can configure Mastra's requireToolApproval gate to intercept calls to sensitive tools like getfeedback or getreview, forcing a manual admin sign-off before the agent accesses the data.

Q: How does the Lattice MCP Server handle workflow failures in Mastra AI?

If a tool like listtasks fails due to network issues, Mastra's workflow engine automatically retries the operation using your configured backoff strategy before raising an error.

Q: Where is my Lattice review data stored when running this MCP Server?

Performance evaluation details fetched via getreview are never stored. The server runs inside a zero-trust execution environment, processing the HR data in-memory and returning it directly to your Mastra AI agent without caching.

Build reliable performance review workflows with Mastra AI and the Lattice MCP Server.

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Connect Lattice MCP to Mastra AI

Create your Vinkius account to connect Lattice to Mastra AI 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 Mastra AI

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Build conditional OKR workflows with Mastra AI

When goals fall behind in Lattice, Mastra AI lets you build agents that query `list_goals` and trigger custom Slack notifications or email nudges based on target progress. You need more than a static dashboard when performance targets are slipping. If the Mastra AI agent detects a blocked OKR, it calls `get_goal` to extract details and automatically assign follow-up tasks to the owner. It runs entirely on your schedule without manual oversight.

Require approval before Mastra AI retrieves Lattice reviews

Because performance data is highly sensitive, this MCP Server integrates with Mastra AI's approval gates so your agent must get explicit confirmation before calling tools like `list_reviews` or `get_review`. Once approved, the Mastra AI agent compiles the review cycles and prepares digests using `get_user` details. This ensures your HR administrators maintain strict control over who accesses performance evaluations.

Handle Lattice API limits inside Mastra AI workflows

Mastra AI uses built-in exponential backoff to retry calls to `list_tasks` or `list_users` if the Lattice API returns a temporary rate-limit error. HR data syncs often hit rate limits during peak review seasons. Your Mastra AI workflows won't break halfway through a performance sync, keeping your internal tracking systems perfectly updated. The agent waits out the rate limit and completes the sync autonomously.

Setup guide

Set up Lattice MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Lattice tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "lattice-mcp-client",
  servers: {
    "lattice-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Lattice Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Lattice tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Lattice transactions"
);
console.log(result.text);

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Lattice. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Lattice MCP in Mastra AI

Instantiate `MCPClient` with your Vinkius HTTP URL. Then, list the tools and spread them into your Mastra agent configuration so it can immediately call `list_users` or `get_user`.

No. This server provides read-only access to tools like `list_goals` and `get_goal`, meaning your agents can monitor and analyze performance but cannot write changes back to your HR system.

Yes. You can configure Mastra's `requireToolApproval` gate to intercept calls to sensitive tools like `get_feedback` or `get_review`, forcing a manual admin sign-off before the agent accesses the data.

If a tool like `list_tasks` fails due to network issues, Mastra's workflow engine automatically retries the operation using your configured backoff strategy before raising an error.

Performance evaluation details fetched via `get_review` are never stored. The server runs inside a zero-trust execution environment, processing the HR data in-memory and returning it directly to your Mastra AI agent without caching.

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