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 CrewAI 9 tools — connect in under 2 minutes

Name: Lattice
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 9 Tools Framework

Connect your CrewAI agents to Lattice through Vinkius, pass the Edge URL in the `mcps` parameter and every Lattice tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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

from crewai import Agent, Task, Crew

agent = Agent(
    role="Lattice Specialist",
    goal="Help users interact with Lattice effectively",
    backstory=(
        "You are an expert at leveraging Lattice tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Lattice "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 9 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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.

When paired with CrewAI, Lattice becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Lattice tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

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

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 9 tools from Lattice

Why Use CrewAI with the Lattice MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Lattice through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Lattice + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Lattice for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Lattice, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Lattice tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Lattice against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Lattice MCP Tools for CrewAI (9)

These 9 tools become available when you connect Lattice to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Lattice + CrewAI FAQ

Common questions about integrating Lattice MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.