Check

Check MCP Server

The Check MCP Server exposes the Check Payroll API to AI tools and agents via the Model Context Protocol. It is offered as both a hosted (remote) endpoint requiring no infrastructure and a self-hosted package for full control of the runtime environment. The server maps Check API operations to MCP tools, allowing natural-language access to companies, employees, payrolls, tax filings, payments, and reports for AI-assisted operator workflows.

Check MCP Server is one of 4 APIs that Check publishes on the APIs.io network.

Tagged areas include MCP, Model Context Protocol, AI, and Agents. The published artifact set on APIs.io includes API documentation.

GitHub

Documentation

📖
Documentation
https://docs.checkhq.com/docs/overview-1

Other Resources

🔗
HostedMCP
https://docs.checkhq.com/docs/hosted-remote
🔗
SelfHostedMCP
https://docs.checkhq.com/docs/self-hosted

API entry from apis.yml

apis.yml Raw ↑ 
aid: check-hq:mcp-server
name: Check MCP Server
description: The Check MCP Server exposes the Check Payroll API to AI tools and agents via the Model Context
  Protocol. It is offered as both a hosted (remote) endpoint requiring no infrastructure and a self-hosted
  package for full control of the runtime environment. The server maps Check API operations to MCP tools,
  allowing natural-language access to companies, employees, payrolls, tax filings, payments, and reports
  for AI-assisted operator workflows.
humanURL: https://docs.checkhq.com/docs/overview-1
tags:
- MCP
- Model Context Protocol
- AI
- Agents
properties:
- type: Documentation
  url: https://docs.checkhq.com/docs/overview-1
- type: HostedMCP
  url: https://docs.checkhq.com/docs/hosted-remote
- type: SelfHostedMCP
  url: https://docs.checkhq.com/docs/self-hosted
features:
- name: Hosted Remote MCP
  description: Connect AI tools to Check without managing any infrastructure.
- name: Self-Hosted MCP
  description: Run the MCP server locally with full environment control.
- name: Natural Language Payroll Operations
  description: Query and operate payroll resources using LLM-driven tool calls.