Connect Seamless.AI to ChatGPT: Prospect, Research, and Sync Leads

If you need to connect Seamless.AI to ChatGPT to automate account prospecting, enrich lead data, or orchestrate complex B2B research workflows, you need a Model Context Protocol (MCP) server. This server acts as the critical translation layer between ChatGPT's function calling capabilities and Seamless.AI's specific REST API requirements. You can either build, host, and maintain this stateful infrastructure yourself, or use a managed integration platform like Truto to dynamically generate a secure, authenticated MCP server URL in seconds.

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

Giving a Large Language Model (LLM) read and write access to a B2B intelligence platform like Seamless.AI is a significant engineering challenge. You are not just dealing with simple CRUD operations. You have to handle credit consumption logic, asynchronous research queues, strict temporal constraints, and complex bracket-based query parameter formatting. Every time you want to expose a new filter or search capability to your AI agent, a custom-built MCP server requires updated schema definitions, redeployment, and testing.

This guide breaks down exactly how to use Truto to generate a secure, managed MCP server for Seamless.AI, connect it natively to ChatGPT, and execute multi-step lead generation workflows using natural language.

The Engineering Reality of the Seamless.AI API

A custom MCP server is essentially a self-hosted API integration layer. While the open MCP standard provides a predictable JSON-RPC 2.0 interface for models to discover tools, implementing it against Seamless.AI's specific API surface is exceptionally difficult.

If you decide to build a custom MCP server for Seamless.AI, you own the entire API lifecycle and all of its edge cases. Here are the specific integration challenges you must solve to prevent your AI agents from failing or hallucinating:

The Asynchronous Research Polling Lifecycle

Unlike standard APIs where a GET request immediately returns data, Seamless.AI uses an asynchronous queue system for deep data enrichment. When you want to research a contact or company, you submit identifiers to a POST endpoint (e.g., /research/contacts). The API does not return the enriched data. Instead, it returns an array of requestIds.

Your client must then poll a separate GET endpoint (/research/contacts/list) using those requestIds to check the status. The status will cycle through states like queued, researching, and eventually done (or error, missing, duplicate). If you are building a custom MCP server, you must either build complex stateful polling logic into the server itself, or design your MCP tool schemas to explicitly instruct the LLM on how to perform this polling loop autonomously. Truto handles this by exposing the raw endpoints as distinct tools with highly descriptive schemas that teach the LLM exactly how to manage the polling lifecycle.

Strict Temporal Constraints and 400 Errors

Seamless.AI enforces strict validation on date ranges for its listing endpoints. For example, when fetching saved contacts or companies, the API requires startDate and endDate parameters. However, Seamless.AI flatly rejects any date range longer than 30 days, returning an HTTP 400 error (Dates times are invalid. Please provide a time range that is no more than 30 days.).

LLMs are notoriously bad at arbitrary temporal math. If a user prompts ChatGPT to "get all saved contacts from this year," the LLM will likely construct a 12-month date range and the API call will crash. Your MCP server schemas must explicitly warn the LLM about this 30-day constraint to prevent continuous execution failures.

Bracket Arrays and Dynamic Credit Pagination

Seamless.AI's search filters rely heavily on bracketed array syntax in query parameters (e.g., companyName []=Salesforce&industry []=Software). An LLM naturally outputs flat JSON structures. Your MCP server must intercept the flat JSON-RPC arguments payload and serialize it correctly into the specific bracket notation the vendor expects.

Furthermore, searching costs credits (typically 1 credit per 10 results, rounded up). If you request a limit of 500 results, but the organization only has 15 credits remaining, Seamless.AI does not reject the request. It dynamically shortens the pagination to what the balance can pay for (150 results). Your MCP server must properly map the response cursors and pass this reality back to the LLM so it understands why it received fewer records than requested.

Rate Limits and 429 Handling

It is critical to note how rate limits operate in this context. Truto does not automatically retry, throttle, or apply backoff logic on rate limit errors. When the Seamless.AI upstream API returns an HTTP 429 Too Many Requests error, Truto passes that error directly back to the calling client (ChatGPT).

Truto normalizes the upstream rate limit information into standardized IETF headers (ratelimit-limit, ratelimit-remaining, ratelimit-reset). The caller - or the LLM framework executing the tool - is entirely responsible for reading these headers and executing exponential backoff. Do not assume the integration layer will absorb rate limit errors for you.

Generating a Seamless.AI MCP Server

Truto dynamically generates MCP tools based on the active API documentation and resource schemas defined for an integration. Tools are derived from existing definitions - meaning there is no manual schema coding required. Each MCP server is scoped to a single authenticated Seamless.AI account.

To begin, you must connect a Seamless.AI account to your Truto environment. Navigate to the Truto dashboard, select Integrated Accounts, create a new connection for Seamless.AI, and complete the OAuth or API key authorization flow. Once authorized, you will receive an integrated_account_id.

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

Method 1: Via the Truto UI

1. Navigate to the Integrated Accounts page in your Truto dashboard and click on your connected Seamless.AI account.
2. Click the MCP Servers tab.
3. Click Create MCP Server.
4. Configure the server. You can specify a human-readable name, set an expiration date, or apply method and tag filters (e.g., restricting the server to only read operations).
5. Click Save and copy the generated MCP server URL. It will look like https://api.truto.one/mcp/a1b2c3d4e5f6....

Method 2: Via the Truto API

You can dynamically provision MCP servers programmatically. This is ideal if you are building an application that needs to spin up isolated ChatGPT access for your end-users.

Make a POST request to the Truto API, passing your API token and the target integrated_account_id:

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": "Seamless.AI Prospecting Server",
    "config": {
      "methods": ["read", "write", "custom"]
    }
  }'

The API response will include the secure url. This URL contains a cryptographically hashed token that routes directly to the specific Seamless.AI tenant. Treat this URL as a sensitive credential.

Connecting the MCP Server to ChatGPT

Once you have your Truto MCP server URL, you must register it with ChatGPT. You can do this via the desktop application UI, or by running a local proxy script if you are building custom agents.

Method A: Via the ChatGPT UI

If you are using the ChatGPT Desktop application (requires a Pro, Plus, Business, Enterprise, or Education account):

1. Open ChatGPT and navigate to Settings -> Apps -> Advanced settings.
2. Toggle on Developer mode to reveal MCP capabilities.
3. Under the MCP servers or Custom connectors section, click Add a new server.
4. Enter a descriptive name (e.g., "Seamless.AI Integration").
5. Paste the Truto MCP server URL you generated in the previous step.
6. Click Save.

ChatGPT will immediately ping the /initialize endpoint, perform a handshake, and ingest the dynamically generated Seamless.AI tool schemas.

Method B: Via Local CLI / Config File

If you are orchestrating agents locally, testing with a framework, or using an environment that requires a standard configuration file, you can utilize the official MCP Server SSE package to proxy the connection.

Create a mcp.json file (or append to your existing configuration) to define the server connection using Server-Sent Events (SSE):

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

When your agent runtime boots, it will execute this command, establish a secure tunnel to Truto's JSON-RPC endpoint, and list the available Seamless.AI tools.

Seamless.AI Hero Tools

Truto automatically generates descriptive, snake_case tool names based on the integration's resources. Here are the highest-leverage tools available for your AI agents when working with Seamless.AI.

list_all_seamless_ai_search_companies

This tool allows the LLM to search the Seamless.AI database for target accounts using deep firmographic filters. The LLM can filter by company name, domain, location, industry, size, revenue, and technologies. Truto maps the LLM's flat JSON arguments into the bracketed query arrays required by the upstream API.

Note: This operation consumes credits (1 credit per 10 results). If the credit balance is too low, the page length is dynamically shortened.

"Search Seamless.AI for enterprise software companies located in California with over $50M in annual revenue. Return the first 20 matches."

list_all_seamless_ai_search_contacts

Once target companies are identified, this tool searches for specific personas within those organizations. The LLM can filter by companyName, jobTitle, seniority, department, and industry. It returns detailed contact cards including their title, seniority, and LinkedIn URLs.

Note: This tool also consumes 1 credit per 10 results. The maximum allowed limit per request is 500.

"Find the VP of Engineering and Chief Technology Officer contacts at the companies you just identified. Limit the search to 50 results."

create_a_seamless_ai_contacts_research

This is the critical entry point for the asynchronous enrichment queue. When the LLM finds a contact via the search tool, it only has basic metadata. To get validated email addresses and direct phone numbers, the LLM must submit the contact's searchResultId to this tool. It returns an array of requestId strings that represent the pending jobs in the queue.

Note: Submitting a record to research costs 1 credit per record.

"Submit the 5 VP of Engineering contacts we found into the Seamless.AI research queue to uncover their validated email addresses."

list_all_seamless_ai_contacts_researches

This tool completes the async loop. The LLM uses this tool to poll the status of the requestIds generated by the research creation tool. It returns the current state (queued, researching, done) and, once complete, the fully enriched contact payload containing confidence-scored emails and phone numbers.

"Check the research status for the 5 contacts we submitted. If they are marked 'done', extract their primary validated email addresses and summarize them."

list_all_seamless_ai_contacts

This tool retrieves contacts that have already been saved and enriched in your Seamless.AI organization. It is free to use (no credits consumed). It requires startDate and endDate parameters.

Crucial constraint: The LLM must not request a date range longer than 30 days, or the API will return a 400 error.

"Retrieve all contacts saved in our Seamless.AI account between October 1st and October 25th. Format the output as a clean table with names, titles, and email addresses."

To view the complete inventory of available Seamless.AI endpoints, query structures, and response schemas, refer to the Seamless.AI integration page.

Workflows in Action

Connecting Seamless.AI to ChatGPT unlocks powerful, multi-step agentic workflows. By giving the LLM access to the search, research, and polling tools, it can act as a fully autonomous SDR. Here are real-world examples of how personas utilize these workflows.

The Autonomous SDR Pipeline

A Sales Development Representative needs to build a highly targeted list of decision-makers in a specific niche and extract their contact information.

"Find 10 cybersecurity companies based in New York. Then, find the C-Level engineering contacts at those companies. Submit those contacts for deep research, wait for the research to complete, and give me a final list of their validated email addresses."

Step-by-step execution:

1. Company Search: ChatGPT calls list_all_seamless_ai_search_companies with the industry set to Cybersecurity, location set to New York, and a limit of 10.
2. Contact Search: ChatGPT iterates through the returned company names and calls list_all_seamless_ai_search_contacts, filtering by seniority []=C-Level and department []=Engineering.
3. Initiate Research: ChatGPT extracts the searchResultId for each matched contact and calls create_a_seamless_ai_contacts_research to push them into the enrichment queue, receiving a list of requestId strings back.
4. Polling Loop: ChatGPT calls list_all_seamless_ai_contacts_researches passing the requestIds. If the status is researching, the agent waits and repeats the call.
5. Final Output: Once the status is done, ChatGPT parses the payload and presents the enriched emails to the user.

sequenceDiagram
    participant Client as ChatGPT
    participant Truto as Truto MCP
    participant Seamless as Seamless.AI
    
    Client->>Truto: Call create_a_seamless_ai_contacts_research
    Truto->>Seamless: POST /research/contacts
    Seamless-->>Truto: 202 Accepted (requestIds)
    Truto-->>Client: Return requestIds
    
    Client->>Truto: Call list_all_seamless_ai_contacts_researches
    Truto->>Seamless: GET /research/contacts
    Seamless-->>Truto: 200 OK (status: researching)
    Truto-->>Client: Return status: researching
    
    Note over Client: Agent waits and retries
    
    Client->>Truto: Call list_all_seamless_ai_contacts_researches
    Truto->>Seamless: GET /research/contacts
    Seamless-->>Truto: 200 OK (status: done, enriched data)
    Truto-->>Client: Return enriched contact record

The RevOps CRM Sync

A Revenue Operations manager wants to audit recently saved leads in Seamless.AI and prepare them for a CRM sync, ensuring no data from the past week has been missed.

"Get all the saved contacts from our Seamless.AI account for the last 7 days. Cross-reference their job change alerts and give me a list of anyone who has recently changed jobs so we can update their CRM records."

Step-by-step execution:

1. Date Calculation: ChatGPT calculates the date 7 days ago and today's date, ensuring the range is well under the strict 30-day API limit.
2. Fetch Saved Data: ChatGPT calls list_all_seamless_ai_contacts using the calculated startDate and endDate.
3. Analyze & Filter: ChatGPT parses the returned JSON arrays, explicitly looking at the jobChangeAlert property on each contact record.
4. Final Output: The LLM generates a concise report of executives who have moved companies, ready for the RevOps manager to action.

Security and Access Control

Exposing an enterprise intelligence tool like Seamless.AI to an LLM requires strict governance, especially considering that certain API calls cost real money via credit consumption. Truto MCP servers provide several layers of access control:

• Method Filtering: When generating the server via the API (/integrated-account/:id/mcp), you can pass a methods array. Setting methods: ["read"] ensures the LLM can only search or list saved data, preventing it from initiating new research queries that consume credits.
• Tag Filtering: You can restrict the MCP server to only expose tools related to specific resources. By passing tags: ["companies"], the LLM will only see company-related tools, completely hiding the contact endpoints.
• API Token Authentication: By default, the cryptographically hashed MCP URL is the only authentication needed. For stricter environments, setting require_api_token_auth: true forces the client to also pass a valid Truto API Bearer token in the headers, adding a second layer of defense.
• Time-to-Live Expiration: You can set an expires_at ISO datetime when creating the server. Once the timestamp passes, the server automatically self-destructs, instantly revoking the LLM's access to the Seamless.AI instance.

Accelerate Your AI Agent Integrations

Connecting Seamless.AI to ChatGPT natively transforms how sales and revenue teams interact with B2B data. Instead of manually clicking through search interfaces and exporting CSVs, operators can instruct agents to execute complex, multi-step prospecting and enrichment loops autonomously.

Building this integration infrastructure from scratch requires dealing with async polling loops, bracketed array parsing, and managing complex rate limit backoffs. Truto abstracts these integration complexities away, automatically generating secure, documentation-driven MCP tools that give your LLMs immediate, structured access to Seamless.AI and hundreds of other SaaS platforms.

Stop managing OAuth tokens, parsing vendor-specific errors, and writing boilerplate integration code.