How do I get a Namely API Token?

You can generate a Personal Access Token in your Namely account under API > Personal Access Tokens. Ensure your user has the appropriate permissions to view the data you want to retrieve.

Can I see salary information through this server?

This depends on the permissions of the user who generated the API token. If your Namely user can see salary data, the `get_profile` and `list_jobs` tools will return that information.

What is the Company Slug?

The slug is the unique part of your Namely URL. For example, if you log in at 'acme.namely.com', your slug is 'acme'.

Namely MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Namely
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Namely through the 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 Namely "
            "(10 tools)."
        ),
    )

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

asyncio.run(main())

Fully ManagedVinkius Servers

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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 Namely MCP Server

Connect your Namely HRIS account to your AI agent and take full control of your organization's employee data and structures through natural conversation.

Pydantic AI validates every Namely tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through the 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 — List all employee profiles and get detailed information including contact info and roles.
Job & Salary Info — Access a complete list of job titles and salary structures defined in your organization.
Org Structure — View all groups, departments, and teams to understand your organizational hierarchy.
HR Timeline — Monitor organization events like birthdays and work anniversaries.
Custom Fields & Reports — List available reports and custom data fields defined for your profiles.
Company Feed — Access recent company-wide announcements from the Namely home feed.

The Namely MCP Server exposes 10 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 Namely to Pydantic AI via MCP

Follow these steps to integrate the Namely 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 10 tools from Namely with type-safe schemas

Why Use Pydantic AI with the Namely MCP Server

Pydantic AI provides unique advantages when paired with Namely 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 Namely integration code

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

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

Namely + Pydantic AI Use Cases

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

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

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

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

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

Namely MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Namely to Pydantic AI via MCP:

get_profile

Get specific employee details

get_team

Get team details

list_announcements

List company announcements

list_events

g., birthdays, work anniversaries) from the organization timeline. List HR events

list_fields

List custom employee fields

list_groups

g., departments, offices) in your organization. List organization groups

list_jobs

List job titles and info

list_profiles

List employee profiles

list_reports

List HR reports

list_teams

List organization teams

Example Prompts for Namely in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Namely immediately.

"List all employees in the 'Engineering' department."

"What company announcements were posted recently?"

"Show me upcoming birthdays in the organization."

Troubleshooting Namely MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Namely + Pydantic AI FAQ

Common questions about integrating Namely 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 Namely MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.