Connect ClayHR to ChatGPT: Manage Employee Lifecycle & Time

If you need to connect ClayHR to ChatGPT to automate employee onboarding workflows, manage project allocations, or orchestrate timesheets, you need a Model Context Protocol (MCP) server. This server acts as the translation layer between ChatGPT's function calls and ClayHR's REST APIs. You can either build and maintain this infrastructure yourself, or use a managed integration platform like Truto to dynamically generate a secure, authenticated MCP server URL.

If your team uses Claude, check out our guide on connecting ClayHR to Claude or explore our broader architectural overview on connecting ClayHR to AI Agents.

Giving a Large Language Model (LLM) read and write access to a comprehensive Human Capital Management (HCM) system like ClayHR is a complex engineering task. You have to handle relational data payloads, map dynamic custom fields to MCP tool definitions, and manage strict timesheet and allocation hierarchies. Every time a developer adds a new custom field or updates an approval workflow in ClayHR, your custom server code must be updated, redeployed, and tested.

This guide breaks down exactly how to use Truto to generate a secure, managed MCP server for ClayHR, connect it natively to ChatGPT, and execute complex HR workflows using natural language.

The Engineering Reality of the ClayHR API

A custom MCP server is a self-hosted integration layer. While the open MCP standard provides a predictable way for models to discover tools, implementing it against ClayHR's API introduces highly specific challenges.

If you decide to build a custom MCP server for ClayHR, you own the entire API lifecycle. Here are the specific integration challenges that break standard CRUD assumptions when working with ClayHR:

Timesheet and Project Allocation Coupling

Unlike simple time-tracking tools where a timesheet is a standalone record, ClayHR enforces strict referential integrity between users, projects, allocations, and timecards. An LLM cannot simply "create a timesheet." It must first retrieve the target user's ID, fetch the active project allocations (clay_hr_timesheets_list_my_allocations), and supply the exact projectId and elapsedTime values in the required formats. Your MCP server must expose these discrete steps as separate tools, with explicit schema descriptions instructing the LLM to pass the IDs sequentially. If you skip this, the LLM will hallucinate project IDs and fail the foreign key constraints.

Custom Field Polymorphism

ClayHR relies heavily on custom fields to track organization-specific employee data. Saving these fields via the API requires interacting with clay_hr_users_save_custom_field_values. The challenge is that these endpoints often expect internal numeric IDs for custom field definitions rather than human-readable labels. Your MCP server must either handle the translation layer or provide the LLM with a dedicated tool (list_all_clay_hr_customfields) to look up the schema definitions before attempting a write operation.

Raw Upstream Rate Limiting

When an LLM hits the ClayHR API with excessive parallel tool calls (like bulk updating 50 employee profiles), it will inevitably trigger rate limits. Truto does not retry, throttle, or apply backoff on rate limit errors. When ClayHR returns an HTTP 429, Truto passes that exact error back to the caller.

However, Truto normalizes the upstream rate limit information into standardized IETF headers (ratelimit-limit, ratelimit-remaining, ratelimit-reset).

HTTP/1.1 429 Too Many Requests
ratelimit-limit: 100
ratelimit-remaining: 0
ratelimit-reset: 1698765432
Content-Type: application/json
 
{
  "error": "Rate limit exceeded. Check headers for reset time."
}

The LLM client (or your orchestration layer) is entirely responsible for reading these headers and implementing its own retry or backoff logic. Do not expect the MCP server to absorb these errors silently.

Quickstart: Generate and Connect a ClayHR MCP Server

If you want the fastest path from a fresh Truto account to ChatGPT calling the ClayHR API, follow these steps.

Step 1: Connect ClayHR as an Integrated Account

In the Truto dashboard, navigate to Integrated Accounts -> New Integrated Account, select ClayHR, and input the required API credentials (typically an API key or OAuth flow depending on the tenant setup). Truto securely stores the credentials and handles the auth injection for all future requests.

Step 2: Generate the MCP Server

Truto dynamically generates MCP tools based on the resources available in the connected ClayHR instance. You can create the MCP server via the UI or the API.

Method A: Via the Truto UI

1. Navigate to the integrated account page for your ClayHR connection.
2. Click the MCP Servers tab.
3. Click Create MCP Server.
4. Select the desired configuration (e.g., allow read and write methods, filter by timesheets tags).
5. Copy the generated MCP server URL.

Method B: Via the API Make a single POST request to scope an MCP endpoint to that specific account. You can filter by methods and tags to strictly constrain what ChatGPT can access.

curl -X POST https://api.truto.one/integrated-account/$INTEGRATED_ACCOUNT_ID/mcp \
  -H "Authorization: Bearer $TRUTO_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "ClayHR HRIS for ChatGPT",
    "config": {
      "methods": ["read", "write", "custom"],
      "tags": ["users", "timesheets", "projects"]
    }
  }'

The response contains a url field (e.g., https://api.truto.one/mcp/a1b2c3d4e5f6...). This URL contains a cryptographic token that routes and authenticates the request. Treat it like a secret.

Step 3: Connect the MCP Server to ChatGPT

Now, feed this URL to ChatGPT. You have two ways to do this.

Method A: Via the ChatGPT UI (Custom Connectors)

1. Open ChatGPT and navigate to Settings -> Apps -> Advanced settings.
2. Enable Developer mode.
3. Under MCP servers / Custom connectors, click Add new.
4. Give it a name (e.g., "ClayHR via Truto").
5. Paste the Truto MCP URL into the Server URL field and save.

ChatGPT will immediately connect, perform the JSON-RPC handshake, and load the ClayHR tools.

Method B: Via Manual Config File (SSE Bridge) If you are running a local proxy, an orchestration framework, or a desktop client that requires a standard configuration file, use the @modelcontextprotocol/server-sse package to bridge the connection.

Create or update your MCP configuration JSON file:

{
  "mcpServers": {
    "clayhr-truto": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-sse",
        "--url",
        "https://api.truto.one/mcp/YOUR_SECURE_TOKEN"
      ]
    }
  }
}

Restart your client, and the tools will be dynamically injected.

High-Leverage ClayHR Tools for ChatGPT

Truto automatically generates schemas for dozens of ClayHR endpoints. Rather than exposing raw REST paths, the MCP server provides descriptively named tools with strict JSON schemas. Here are the most powerful tools to expose to ChatGPT.

clay_hr_users_upsert

Creates a new user, or updates an existing user if an email match is found. This is the ultimate tool for employee onboarding because a single call can instantiate the user, set compensation, and map custom field values.

Required arguments: email, firstName.

"We just hired John Smith as a Senior Engineer. His email is john.smith@company.com. Create his profile in ClayHR and set his title."

create_a_clay_hr_project_allocation

Assigns a user to a specific project. This is a critical prerequisite for timesheet entry. The LLM must pass the exact projectId and userId.

Required arguments: projectId, userId.

"Look up the project ID for the 'Website Redesign' initiative, then allocate Sarah Connor to that project so she can log hours against it."

clay_hr_timesheets_save

Creates or updates a timesheet entry. Because the Truto MCP server injects explicit descriptions into the schema, ChatGPT knows to supply the date, projectId, and elapsedTime precisely as the ClayHR API expects them.

Required arguments: date, projectId, elapsedTime.

"Log 6.5 hours for me on the Website Redesign project for yesterday."

create_a_clay_hr_pto_balance

Adds a Paid Time Off (PTO) balance to a user's account. This is typically restricted to HR admins, and it requires knowing the internal ptoPolicyId.

Required arguments: userId, ptoPolicyId, balance.

"Find the user ID for Marcus Cole, check the policy ID for 'Standard Vacation', and add 40 hours to his PTO balance."

create_a_clay_hr_performance_review

Launches a performance review for a specific user based on a template. This takes the manual clicking out of quarterly review cycles.

Required arguments: userId, templateId.

"Trigger the Q3 Engineering Performance Review template for all users reporting to the VP of Engineering."

clay_hr_expense_reports_submit

Transitions an expense report from draft to submitted status. This is the final step in the expense lifecycle after adding individual expense items.

Required arguments: expenseReportId, status.

"Find my draft expense report for the Q3 Offsite, verify the total matches my receipts, and submit it for manager approval."

To view the complete inventory of available tools, resource schemas, and webhooks for this integration, visit the ClayHR integration page.

Workflows in Action

When ChatGPT is equipped with Truto's ClayHR MCP tools, it acts as an autonomous agent. It can chain multiple API calls together to achieve complex, multi-step outcomes.

Scenario 1: Autonomous Employee Onboarding

Persona: HR Administrator

"Onboard Jane Doe (jane.doe@acme.com) as a UX Designer. Once her profile is created, allocate her to the 'Mobile App V2' project and give her a starting PTO balance of 80 hours under the 'New Hire PTO' policy."

Step-by-step execution:

1. clay_hr_users_upsert: ChatGPT calls this tool with {"firstName": "Jane", "lastName": "Doe", "email": "jane.doe@acme.com"}. The API returns the new user's ID (e.g., 4591).
2. list_all_clay_hr_projects: ChatGPT searches for "Mobile App V2" to resolve the project ID (e.g., 88).
3. create_a_clay_hr_project_allocation: ChatGPT uses userId: 4591 and projectId: 88 to assign Jane to the project.
4. list_all_clay_hr_pto_policies: ChatGPT searches for "New Hire PTO" to find the policy ID (e.g., 12).
5. create_a_clay_hr_pto_balance: ChatGPT calls this tool with userId: 4591, ptoPolicyId: 12, and balance: 80.

Outcome: ChatGPT replies, "Jane Doe has been successfully onboarded. Her user ID is 4591. She is now allocated to the Mobile App V2 project and has been credited with 80 hours of PTO."

sequenceDiagram
    participant User as User
    participant ChatGPT as ChatGPT
    participant MCPServer as MCP Server
    participant ClayHR as ClayHR API
    
    User->>ChatGPT: "Onboard Jane Doe and allocate her to Mobile App V2..."
    ChatGPT->>MCPServer: tools/call clay_hr_users_upsert
    MCPServer->>ClayHR: POST /api/users
    ClayHR-->>MCPServer: 200 OK (userId: 4591)
    MCPServer-->>ChatGPT: tool_result (userId: 4591)
    
    ChatGPT->>MCPServer: tools/call list_all_clay_hr_projects
    MCPServer->>ClayHR: GET /api/projects
    ClayHR-->>MCPServer: 200 OK (projectId: 88)
    MCPServer-->>ChatGPT: tool_result (projectId: 88)
    
    ChatGPT->>MCPServer: tools/call create_a_clay_hr_project_allocation
    MCPServer->>ClayHR: POST /api/allocations
    ClayHR-->>MCPServer: 200 OK
    MCPServer-->>ChatGPT: tool_result (Success)
    
    ChatGPT-->>User: "Jane Doe is onboarded and allocated."

Scenario 2: End-to-End Expense Logging

Persona: Field Consultant

"I spent $45.50 on a client lunch today for the 'Acme Corp Implementation' project. Create an expense report for this week, add the lunch expense, and submit the report."

Step-by-step execution:

1. create_a_clay_hr_expense_report: ChatGPT creates a new report named "Week of [Current Date] Expenses" and retrieves the expenseReportId.
2. list_all_clay_hr_expense_types: ChatGPT looks up the internal ID for the "Meals/Entertainment" expense category.
3. create_a_clay_hr_expense_item: ChatGPT logs the $45.50 charge, tagging it with the correct category ID and linking it to the expenseReportId.
4. clay_hr_expense_reports_submit: ChatGPT submits the report using the expenseReportId and setting the status to submitted.

Outcome: ChatGPT replies, "I've created your expense report for this week, added the $45.50 client lunch, and submitted the report for approval."

Security and Access Control

Exposing an HRIS to an AI agent requires strict governance. Truto provides four layers of security for MCP servers:

• Method Filtering: Constrain the MCP server to specific HTTP verbs. You can set methods: ["read"] to allow ChatGPT to query user directories and timesheets without the ability to mutate data.
• Tag Filtering: Restrict tools to specific API domains. By setting tags: ["timesheets", "projects"], you prevent the LLM from accidentally accessing payroll, performance reviews, or PTO policies.
• Require API Token Auth: By setting require_api_token_auth: true, possession of the MCP URL is no longer enough. The client must also pass a valid Truto API token in the Authorization header, adding a strict secondary layer of access control.
• Ephemeral Servers: Set an expires_at ISO datetime when creating the server. Truto will automatically destroy the MCP token at that exact time - perfect for granting a contractor's AI agent temporary access to log timesheets for a single sprint.

Architecting AI-Native HR

Connecting ClayHR to ChatGPT unlocks massive operational efficiency, but the integration must be treated as a production engineering effort. Relying on custom-built MCP servers means constantly fighting schema drift, mapping undocumented endpoints, and building bespoke security layers to prevent an LLM from deleting critical employee records.

By routing ChatGPT's tool calls through Truto's managed MCP infrastructure, you get standardized rate limit headers, automatic schema discovery, granular method filtering, and zero-downtime maintenance.

Stop wrestling with API maintenance. Connect your AI agents directly to the business logic that matters.