Connect UpLead to ChatGPT: Search Prospects and Manage Lead Lists

If you need to connect UpLead to ChatGPT to automate B2B prospecting, enrich company profiles, and manage targeted lead lists, you need a Model Context Protocol (MCP) server. This server acts as the translation layer between ChatGPT's tool calls and UpLead'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 UpLead to Claude or explore our broader architectural overview on connecting UpLead to AI Agents.

Giving a Large Language Model (LLM) read and write access to a premium B2B data provider like UpLead is a significant engineering challenge. You have to handle credit consumption logic, complex multi-parameter search schemas, and aggressive pagination requirements. Every time an agent hallucinates a search loop, it risks draining your account's credit balance.

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

The Engineering Reality of the UpLead 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 UpLead's specific API quirks is exceptionally painful.

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

Variable Credit Consumption Models

UpLead charges credits dynamically based on the data returned. For example, when searching for contacts, you are only charged a credit if the contact has a valid or accept_all email address. If you give an LLM unconstrained access to up_lead_people_search, a poorly optimized prompt could execute a broad query that returns hundreds of contacts, immediately draining your monthly credit allocation. Your MCP server must expose tools that allow the LLM to verify credit balances (list_all_up_lead_credits) and safely query data volumes using UpLead's count_only parameter before fetching actual records.

Implicit Type Enforcement in Search Endpoints

UpLead's quick_search endpoints enforce implicit query constraints. For instance, up_lead_people_quick_search implicitly sends type=contact in the query, while up_lead_companies_quick_search sends type=company. However, the API does not strictly block a caller-supplied type from overriding these defaults. If your MCP tool schemas are too loose, an LLM might inject an overriding type parameter, causing a contact search to suddenly return company data and breaking downstream parsing logic.

Rate Limits and Header Normalization

UpLead enforces rate limits on API requests to protect their infrastructure. It is critical to understand how this is handled: Truto does not retry, throttle, or apply backoff on rate limit errors. When UpLead 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 spec. The caller (your agent framework or ChatGPT) is entirely responsible for reading these headers and implementing its own retry and backoff logic.

UpLead to ChatGPT Quickstart Guide

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

What you need:

• A Truto account with API access.
• An UpLead API key (available on Professional, Elite, and Enterprise plans).
• A ChatGPT Pro, Plus, Business, Enterprise, or Education seat with Developer mode available.

Step 1: Connect UpLead as an Integrated Account

In the Truto dashboard, navigate to Integrated Accounts -> New Integrated Account, select UpLead, and provide your API credentials. Truto securely vaults the API key, so ChatGPT never handles the raw credential.

Step 2: Get your Integrated Account ID

You will need the integrated_account_id to generate the server. You can copy it from the Truto UI on the account detail page, or fetch it via the API:

curl https://api.truto.one/integrated-account \
  -H "Authorization: Bearer $TRUTO_API_TOKEN"

Step 3: Generate the UpLead MCP Server

You can create the MCP server either through the Truto UI or programmatically via the API.

Method A: Via the Truto UI

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

Method B: Via the Truto API Make a POST request to scope an MCP endpoint specifically to your UpLead account. You can pass filters to constrain exactly what ChatGPT can do.

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": "UpLead for ChatGPT",
    "config": {
      "methods": ["read", "write"],
      "tags": ["contacts", "companies", "lists"]
    }
  }'

The API returns a database record containing a url field (e.g., https://api.truto.one/mcp/<token>). This URL carries routing and authentication - treat it like a password.

Step 4: Connect the MCP Server to ChatGPT

You can register the server in ChatGPT using the UI or via a configuration file for local development.

Method A: Via the ChatGPT UI

1. In ChatGPT, go to Settings -> Apps -> Advanced settings.
2. Ensure Developer mode is enabled.
3. Under MCP servers / Custom connectors, click add.
4. Set the Name to "UpLead Prospects".
5. Paste the Truto MCP URL into the Server URL field and click Add.

Method B: Via Manual Config File (for local agents/Claude Desktop users) If you are testing locally or using an environment that requires a standard MCP JSON configuration, use the Server-Sent Events (SSE) transport adapter:

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

Once connected, ChatGPT will automatically request the tools/list from the server. Truto dynamically maps UpLead's documented resources into JSON-RPC tool schemas on the fly.

Hero Tools for UpLead Automation

When you connect UpLead via Truto, your LLM gains access to a standardized set of proxy APIs derived directly from UpLead's resource schemas. Here are the highest-leverage tools available for ChatGPT.

up_lead_companies_enrich

Looks up a single company profile using a domain name, company name, or UpLead ID. The domain provides the most precise match. It returns deep firmographic data including NAICS codes, revenue size, address, and social URLs. This consumes one credit per record.

"I have a target account list with just website domains. Run up_lead_companies_enrich for 'truto.one' and give me a summary of their industry and estimated revenue size."

up_lead_people_enrich

Retrieves a complete UpLead contact profile based on an email address, ID, or a combination of first name, last name, and domain. It returns job function, management level, email status, phone numbers, and LinkedIn URLs. Costs one credit per contact (if the email is valid).

"We just scanned a badge at a conference for 'jane.doe@example.com'. Use up_lead_people_enrich to find out what her current title and management level are."

up_lead_people_pro_search

Executes an advanced search across the UpLead contact database filtering by domain, job function, titles, location, and company size. This requires a Professional, Elite, or Enterprise plan. Supports pagination up to 100 records per page.

"I need a list of marketing decision-makers. Run up_lead_people_pro_search to find VP or C-level marketing contacts located in California who work at software companies with $10M+ in revenue."

list_all_up_lead_lists

Retrieves all saved contact lists currently stored in your UpLead account workspace. This is essential for agents that need to organize leads before exporting them to a CRM.

"Use list_all_up_lead_lists to show me all our current prospecting lists. I am looking for the ID of the list named 'Q3 Enterprise Targets'."

up_lead_list_contacts_add

Adds one or more enriched UpLead contacts (via their contact_ids) to a specific saved list (list_id). This allows the agent to construct target lists dynamically after conducting research.

"Take the contact IDs we just found from the Pro Search, and use up_lead_list_contacts_add to attach them to list ID 12345."

list_all_up_lead_credits

Retrieves the remaining credit balance for the connected UpLead account. AI agents should be instructed to call this tool before executing large, paginated loops to ensure they do not exceed account limits.

"Before we start building this prospect list, call list_all_up_lead_credits to tell me exactly how many credits we have remaining for the month."

For the complete inventory of available UpLead endpoints, pagination rules, and JSON schemas, visit the UpLead integration page.

Workflows in Action

AI agents excel at orchestrating multi-step workflows. By giving ChatGPT access to the UpLead MCP server, you can chain these tools together to automate tedious prospecting tasks.

Workflow 1: Targeted Account Sourcing and List Building

Sales Development Reps (SDRs) spend hours finding the right contacts at target accounts. You can ask ChatGPT to handle the entire discovery and organization process.

"We are targeting 'acme.com'. First, pull their company profile. Then, find their VP of IT. If you find one, add them to our 'Q4 Tech Targets' list."

1. ChatGPT calls up_lead_companies_enrich passing domain: "acme.com" to verify the company exists and matches the ideal customer profile.
2. ChatGPT calls list_all_up_lead_lists and filters the response to find the list_id for "Q4 Tech Targets".
3. ChatGPT calls up_lead_people_search filtering by domain: "acme.com" and title: "VP IT".
4. ChatGPT extracts the resulting contact ID and calls up_lead_list_contacts_add to append the prospect to the saved list.

sequenceDiagram
    participant User
    participant LLM as ChatGPT
    participant Truto as Truto MCP Server
    participant UpLead as UpLead API

    User->>LLM: "Find the VP of IT at acme.com and save them to the Q4 list."
    LLM->>Truto: Call up_lead_companies_enrich (domain: "acme.com")
    Truto->>UpLead: GET /v2/companies/enrich
    UpLead-->>Truto: Company Data
    Truto-->>LLM: JSON Company Profile
    LLM->>Truto: Call up_lead_people_search (domain: "acme.com", title: "VP IT")
    Truto->>UpLead: GET /v2/contacts
    UpLead-->>Truto: Contact Array
    Truto-->>LLM: JSON Contact Results
    LLM->>Truto: Call up_lead_list_contacts_add (list_id: 987, contact_ids: [123])
    Truto->>UpLead: POST /v2/lists/987/contacts
    UpLead-->>Truto: 200 OK
    Truto-->>LLM: Success confirmation
    LLM-->>User: "Contact found and added to the Q4 list."

Workflow 2: Credit Auditing and Enrichment

Before running large enrichment jobs on an imported CSV, you need to ensure the account has enough bandwidth.

"Check our UpLead credit balance. If we have more than 50 credits, enrich 'john.smith@techcorp.com' and pull the company data at the same time."

1. ChatGPT calls list_all_up_lead_credits and inspects the credits integer in the response.
2. Seeing the balance is sufficient, ChatGPT calls the combined up_lead_people_enrich_with_company tool, passing the email address.
3. ChatGPT returns a structured markdown summary combining the person's LinkedIn data, phone numbers, and the company's NAICS codes.

Security and Access Control

Exposing an expensive B2B data API to an LLM requires strict boundary setting. Truto MCP servers provide native access control mechanisms configured during server creation:

• Method Filtering (methods): Restrict ChatGPT to safe operations. Configure methods: ["read"] to allow up_lead_people_search and list_all_up_lead_lists while strictly blocking create or delete actions on lists.
• Tag Filtering (tags): Limit the API surface area. By passing tags: ["companies"], ChatGPT will only see company enrichment tools and will be blind to individual contact tools.
• Dual-Layer Authentication (require_api_token_auth): For enterprise environments where MCP URLs might be shared across teams, enable this flag to force the client to pass a valid Truto user API token in the Authorization header, preventing anonymous usage.
• Time-to-Live (expires_at): Generate ephemeral servers for temporary AI agent tasks. Once the ISO datetime is reached, the server destroys itself automatically.

Scaling AI Prospecting Workflows

Connecting UpLead to ChatGPT transforms how go-to-market teams build their pipelines. Instead of clicking through a UI to filter lists, export CSVs, and manage credits, your AI agents can orchestrate the entire enrichment lifecycle conversationally.

By leveraging Truto's dynamic MCP generation, you bypass the friction of writing custom JSON-RPC wrappers, managing API documentation drift, and handling secure token exchange. You configure the server, apply your security constraints, and let ChatGPT take over the lead sourcing execution.