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 Pydantic AI 9 tools — connect in under 2 minutes

Name: Lattice
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 9 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Lattice through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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 pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Lattice "
            "(9 tools)."
        ),
    )

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

asyncio.run(main())

Fully ManagedVinkius Servers

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DLPData protection

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Ed25519Audit chain

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

Pydantic AI validates every Lattice tool response against typed schemas, catching data inconsistencies at build time. Connect 9 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the Lattice MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

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 with type-safe schemas

Why Use Pydantic AI with the Lattice MCP Server

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

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Lattice integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Lattice connection logic from agent behavior for testable, maintainable code

Lattice + Pydantic AI Use Cases

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

Type-safe data pipelines: query Lattice with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Lattice tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Lattice and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Lattice responses and write comprehensive agent tests

Lattice MCP Tools for Pydantic AI (9)

These 9 tools become available when you connect Lattice to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Lattice + Pydantic AI FAQ

Common questions about integrating Lattice MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Lattice MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.