Connect Huntr to ChatGPT: Manage Job Seekers and Track Progress

If you need to connect Huntr to ChatGPT to automate talent portals, manage job seekers, and track career placement progress, you need a Model Context Protocol (MCP) server. This server acts as the secure translation layer between ChatGPT's tool calling capabilities and Huntr's REST API.

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

Giving a Large Language Model (LLM) read and write access to a job search CRM like Huntr is a significant engineering challenge. You must handle complex, nested talent profiles, polymorphic activity feeds, and asynchronous job board deployments. Every time Huntr adds a new feature or you create a custom member field, your custom integration code has to be updated, tested, and redeployed.

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

The Engineering Reality of the Huntr API

Building a custom MCP server means owning the entire API integration lifecycle. While the open MCP standard provides a predictable way for ChatGPT to discover tools, implementing it against Huntr's specific data model requires handling several platform-specific quirks.

If you decide to build a custom MCP server for Huntr, here are the specific integration challenges you will face:

The Lightweight List vs. Heavy Get Pattern

Huntr's API heavily relies on a lightweight listing pattern to preserve bandwidth. When an LLM calls an endpoint like /candidates to list job seekers, it only receives superficial data (ID, name, email, memberId). If ChatGPT needs to evaluate a candidate based on their skillNames, totalWorkExperienceYears, or industryPreferencesNames, it cannot do so from the list endpoint. Your MCP server must be designed to instruct the LLM to paginate through the list, extract the specific candidate IDs, and subsequently call the GET /candidates/:id endpoint for the deep profile data. Without strict schema instructions, the LLM will hallucinate skills that weren't returned in the initial list.

Polymorphic Activity Feeds

The Huntr API provides an Actions resource to track every job lifecycle event (applications, interviews, offers). This endpoint returns a polymorphic payload. A single action record might contain references to jobs, employers, activityCategory, note, or contact objects depending on the actionType. Providing this raw, highly variable JSON schema to ChatGPT often leads to context confusion. Your MCP server must define dynamic, predictable tool schemas that help the LLM parse these varying event types correctly without dropping critical context.

Rate Limits and Error Handling

Huntr enforces API rate limits to protect its infrastructure. A common mistake when building AI agent integrations is assuming the integration middleware will magically absorb these limits. Truto does not retry, throttle, or apply backoff on rate limit errors. When the upstream Huntr API returns an HTTP 429 (Too Many Requests), Truto passes that error directly to the caller. Truto normalizes the upstream rate limit information into standardized headers (ratelimit-limit, ratelimit-remaining, ratelimit-reset) per the IETF specification. The caller (in this case, your ChatGPT client or orchestration framework) is strictly responsible for interpreting these headers and executing its own retry/backoff logic.

Huntr to ChatGPT Quickstart Guide

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

What you need:

• A Truto account with API access.
• Huntr organization admin credentials.
• A ChatGPT Pro, Plus, Business, Enterprise, or Education seat with Developer mode enabled.

Step 1: Connect the Huntr Account

First, establish the OAuth or API key connection to Huntr.

1. In the Truto dashboard, navigate to Integrated Accounts -> New Integrated Account.
2. Select Huntr and follow the authentication flow.
3. Note your integrated_account_id.

Step 2: Generate the MCP Server URL

You can create the MCP server endpoint using either the Truto UI or the API.

Method A: Via the Truto UI

1. Navigate to the integrated account page for your Huntr connection.
2. Click the MCP Servers tab.
3. Click Create MCP Server.
4. Name your server (e.g., "Huntr ChatGPT Integration"), configure allowed methods or tags (optional), and click Save.
5. Copy the generated MCP Server URL (it will look like https://api.truto.one/mcp/<secure-token>).

Method B: Via the API Make a POST request to scope an MCP endpoint strictly to this Huntr account:

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": "Huntr Talent CRM for ChatGPT",
    "config": {
      "methods": ["read", "write"],
      "tags": ["candidates", "jobs", "members"]
    }
  }'

The response contains a url field. This single URL handles all routing, authentication, and dynamic tool generation.

Step 3: Connect to ChatGPT

Now, provide this URL to your ChatGPT environment.

Method A: Via the ChatGPT UI

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

Method B: Via Manual Config File (for headless or custom desktop clients) If you are using a client that requires a standard MCP config file, you can bridge the SSE endpoint using the official remote transport package:

{
  "mcpServers": {
    "huntr-truto": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-sse",
        "https://api.truto.one/mcp/<your-secure-token>"
      ]
    }
  }
}

ChatGPT will immediately ping the endpoint, execute an initialization handshake, and list the available Huntr tools derived directly from the API's documentation.

Hero Tools for Huntr

Truto automatically generates MCP tools based on Huntr's API documentation. Because tools are documentation-driven, you only expose curated, explicitly defined capabilities to ChatGPT. Here are the highest-leverage tools available for Huntr.

list_all_huntr_candidates

Retrieves the lightweight list of candidates in your organization's talent portal. Because this endpoint only returns high-level data (name, email, memberId), it is best used as a discovery tool before diving into specific profiles.

"List all the candidates currently tracked in our Huntr portal. Give me their names and IDs."

get_single_huntr_candidate_by_id

Retrieves the comprehensive talent portal profile for a specific candidate. This returns massive analytical value, exposing total work experience, skills, industry preferences, current location, and visa sponsorship requirements.

"Fetch the full profile for candidate ID 'cand_8392'. Summarize their total years of experience, core skills, and whether they require visa sponsorship in the US."

create_a_huntr_job_post

Publishes a new job post to your organization's internal job portal. Setting the status to 'Open' makes it visible immediately. This allows ChatGPT to draft and publish job descriptions in a single motion.

"Create a new Remote Senior DevOps Engineer job post for employer 'Stripe' with a salary of $160,000. Set the status to Open and include this HTML description I just drafted."

huntr_job_posts_send_to_members

Distributes an existing job post directly to specific job seekers' Huntr boards as new Job cards. Note that this triggers an asynchronous background process in Huntr, meaning the tool returns a 200 OK acknowledgment immediately while delivery queues in the background.

"Send the Senior DevOps job post (ID: 'post_9921') to members with IDs 'mem_111' and 'mem_222'."

create_a_huntr_organization_invitation

Generates and emails a Huntr organization invitation to a prospective job seeker. If the user already has a pending invitation, the API cleanly returns an error without duplicating the invite.

"Send an organization invitation to alex.chen@example.com to join our Huntr board. Assign them to the 'Software Engineering' default board template."

create_a_huntr_member_note

Creates an internal administrative note on a member's profile. This note is only visible to organization staff on the admin dashboard, making it perfect for ChatGPT to log automated interview summaries or screening insights.

"Add an internal note to member ID 'mem_555' stating that they passed the technical phone screen with flying colors and should be fast-tracked to the hiring manager."

For a complete list of all available Huntr tools and their specific JSON schemas, visit the Huntr integration page.

Workflows in Action

Providing an LLM with individual tools is useful, but the real power of MCP lies in giving ChatGPT the ability to chain operations together autonomously. Here are two real-world workflows.

Scenario 1: Talent Advisor Distributing a Targeted Role

The Goal: A talent advisor wants to find candidates with a specific background and push a newly created job opportunity directly to their personal Huntr boards.

"We just opened a new job post for a Frontend React Developer (ID: post_102). Check our candidate list, find anyone who lists 'React' in their skills, and send this job post to their board."

Execution Steps:

1. list_all_huntr_candidates: ChatGPT fetches the directory of available candidates to get their IDs.
2. get_single_huntr_candidate_by_id: The LLM loops through the candidate IDs, pulling full profiles to inspect the skillNames array for "React".
3. huntr_job_posts_send_to_members: Having identified three matching candidates, ChatGPT constructs the memberEntries payload and triggers the bulk send tool.

Result: The LLM responds confirming the job has been queued for distribution to the three qualified candidates, successfully automating a manual sourcing task.

Scenario 2: Sourcing and Internal Profiling

The Goal: A career coach wants ChatGPT to process interview feedback, log an internal note on a candidate's profile, and prepare them for a specific application.

"I just finished interviewing Sarah Connor (find her in the candidate list). Add an internal note to her profile that she has exceptional Python skills. Then tell me what her current industry preferences are."

sequenceDiagram
    participant User
    participant ChatGPT
    participant TrutoMCP as Truto MCP Server
    participant Upstream as Huntr API

    User->>ChatGPT: "Find Sarah Connor, add note..."
    ChatGPT->>TrutoMCP: Call list_all_huntr_candidates
    TrutoMCP->>Upstream: GET /candidates
    Upstream-->>TrutoMCP: Returns candidate list
    TrutoMCP-->>ChatGPT: List includes Sarah Connor (mem_999)
    
    ChatGPT->>TrutoMCP: Call get_single_huntr_candidate_by_id (cand_id)
    TrutoMCP->>Upstream: GET /candidates/{id}
    Upstream-->>TrutoMCP: Returns full profile (industry preferences)
    TrutoMCP-->>ChatGPT: Profile data returned
    
    ChatGPT->>TrutoMCP: Call create_a_huntr_member_note (mem_999, text)
    TrutoMCP->>Upstream: POST /members/{id}/notes
    Upstream-->>TrutoMCP: 201 Note Created
    TrutoMCP-->>ChatGPT: Success confirmation
    
    ChatGPT-->>User: "Note added. Sarah prefers the FinTech industry."

Security and Access Control

When connecting ChatGPT to your talent CRM, security is paramount. Truto’s MCP servers are designed with strict, declarative access controls at the infrastructure level.

• Method Filtering: Limit the MCP server to specific HTTP methods. Passing methods: ["read"] during server creation guarantees ChatGPT can only fetch data (like listing candidates) and physically cannot execute POST, PUT, or DELETE requests.
• Tag Filtering: Restrict tools by functional domain. Setting tags: ["jobs"] ensures the server only exposes endpoints related to job posts, hiding sensitive areas like organization billing or API token generation.
• Mandatory API Token Auth: By enabling require_api_token_auth: true, possession of the MCP URL is no longer enough to execute a tool. The client must also pass a valid Truto API token via a Bearer header, adding a required secondary layer of authentication for enterprise deployments.
• Ephemeral Servers: Set an expires_at timestamp when generating the server. Once the timestamp passes, the distributed key-value store immediately drops the token, and background alarms sweep the database, permanently severing ChatGPT's access to the Huntr account.

Moving Beyond Manual Talent Pipelines

Building a custom MCP server for Huntr means writing schema parsers, managing OAuth tokens, normalizing dynamic custom fields, and manually defining dozens of JSON-RPC tools. It is an infrastructure burden that distracts from building actual AI features.

By leveraging Truto's dynamic, documentation-driven tool generation, you can expose Huntr to ChatGPT instantly. The LLM gets predictable, highly curated access to your talent portal, and you get strict filtering and security controls without writing a line of integration boilerplate.