Connect Slido to ChatGPT: Sync Polls, Q&A, and Interaction Data

If you need to connect Slido to ChatGPT to automate audience engagement analytics, sync Q&A exports, or summarize meeting polls, you need a Model Context Protocol (MCP) server. This server acts as the translation layer between ChatGPT's tool calls and Slido'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 Slido to Claude or explore our broader architectural overview on connecting Slido to AI Agents.

Giving a Large Language Model (LLM) read and write access to a live audience interaction platform like Slido is an engineering challenge. Event data is highly relational, operations are often asynchronous, and payloads can grow massively during large town halls. Every time Slido updates an endpoint or changes an export format, custom connector code requires redeployment. This guide breaks down exactly how to use Truto to generate a secure, managed MCP server for Slido, connect it natively to ChatGPT, and execute complex workflows using natural language.

The Engineering Reality of the Slido 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, the reality of implementing it against Slido's APIs is painful. You are not just building standard CRUD wrappers - you are managing long-running background jobs and deeply nested hierarchical data.

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

The Asynchronous Export Pattern Slido does not allow you to instantly download a list of 10,000 participant questions in a single synchronous API call. Instead, the Slido API relies heavily on an asynchronous export pattern. To retrieve data like Q&A, polls, or participants, you must first issue a POST request to start an export job. Slido returns an HTTP 202 Accepted response with an export_id. You must then build logic to repeatedly poll the GET export endpoint using that ID until the status shifts from PROCESSING to COMPLETED. If you do not explicitly build this polling logic into your MCP server, the LLM will hallucinate the final payload based on the initial 202 response.

Event-Scoped Data Architecture Virtually all interaction data in Slido is strictly scoped to a specific event_id. Questions belong to events. Polls belong to events. Participants belong to events. An AI agent cannot simply ask "get all questions." It must first query the events index, extract the correct event_id, and inject that identifier into subsequent API calls. If your MCP server schemas do not clearly map these dependencies, ChatGPT will consistently fail validation errors when trying to fetch audience data.

Rate Limits and 429 Errors Event platforms experience massive traffic spikes. During a live Q&A, you might hit API limits rapidly. Slido enforces strict rate limits, and Truto does not automatically retry, throttle, or apply backoff on rate limit errors. When the upstream Slido API returns an HTTP 429, 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. Your client framework or the LLM itself is entirely responsible for reading these headers and executing exponential backoff.

How to Create the Slido MCP Server

Instead of writing custom polling logic and JSON schemas, you can use Truto to instantly generate an MCP server. Truto derives the tool definitions directly from the integration's documentation and underlying API schema, acting as a managed proxy between the LLM and Slido.

You can generate the MCP server either through the visual interface or programmatically via the API.

Method 1: Via the Truto UI

For administrators and non-developers, generating a server takes less than a minute.

1. Navigate to the Integrated Accounts page in your Truto dashboard.
2. Select your connected Slido account.
3. Click the MCP Servers tab.
4. Click Create MCP Server.
5. Select your desired configuration (e.g., restrict to read methods only, or filter by specific tags like q-a).
6. Copy the generated MCP server URL (e.g., https://api.truto.one/mcp/a1b2c3d4e5f6...).

Method 2: Via the Truto API

For engineering teams building multi-tenant AI products, you can generate MCP server URLs on the fly for your end-users. The API validates that the Slido connection is active and returns a cryptographically secure token URL.

const response = await fetch(
  'https://api.truto.one/integrated-account/<SLIDO_ACCOUNT_ID>/mcp',
  {
    method: 'POST',
    headers: {
      Authorization: 'Bearer <YOUR_TRUTO_API_TOKEN>',
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      name: 'Slido Townhall MCP',
      config: {
        methods: ['read', 'create'],
        tags: ['exports', 'events']
      }
    }),
  }
);
 
const mcpServer = await response.json();
console.log(mcpServer.url); // Pass this URL to ChatGPT or your agent framework

How to Connect the MCP Server to ChatGPT

Once you have the Truto MCP URL, you need to register it with your LLM client. The MCP URL is fully self-contained - it holds the authentication context for the specific Slido tenant.

Method A: Via the ChatGPT UI (Custom Connectors)

If you are using ChatGPT Plus, Enterprise, or Team tiers, you can connect the server directly through the interface.

1. Open ChatGPT and navigate to Settings -> Apps -> Advanced settings.
2. Toggle Developer mode on (MCP support requires this flag).
3. Under MCP servers / Custom connectors, click Add a new server.
4. Name: Enter a descriptive label (e.g., "Slido Event Data").
5. Server URL: Paste the Truto MCP URL you generated.
6. Click Save. ChatGPT will immediately perform the MCP handshake and load the Slido tools.

Method B: Via Manual Config File (Local SSE Transport)

If you are running a local instance of Claude Desktop, Cursor, or a custom agent framework that utilizes the @modelcontextprotocol/server-sse package, you can mount the server via a JSON configuration file.

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

Restart your agent client, and it will bind the Slido capabilities to the model's context window.

Slido Hero Tools for AI Agents

Truto translates Slido's API into a suite of highly specific, LLM-optimized tools. Here are five high-leverage tools available for Slido, focusing on the core asynchronous export patterns.

create_a_slido_exports_event

This tool initiates an asynchronous export of all events in the Slido workspace. It returns an HTTP 202 job object containing an export_id. This is typically the prerequisite tool an agent must call to discover the target event_id before querying specific Q&A or poll data.

"I need to find the event ID for the Q3 All-Hands townhall. Please trigger an export of the events, check the status until it finishes, and tell me the ID for the Q3 event."

create_a_slido_exports_question

Starts an asynchronous export of Q&A questions submitted during an event. The exported records contain deep telemetry, including score, upvotes, downvotes, and is_anonymous flags. It requires the agent to pass the event_id.

"Start an export of all questions asked during the Q3 All-Hands (event ID 98765). Once the export is ready, download the data and list the top 5 questions sorted by highest upvotes."

create_a_slido_exports_poll

Triggers an async export of polls associated with an event. This tool maps the overarching poll structure, returning IDs and types (e.g., multiple choice, rating, word cloud) that can be used to query individual participant votes later.

"Export the polls for our product feedback session. Let me know how many active polls we ran and what types of questions we asked the audience."

create_a_slido_exports_participant

Starts an asynchronous export of participant data. This exposes who joined the event, when they joined, and their basic identity (if they did not join anonymously). This is critical for matching audience members to specific CRM records.

"Trigger an export of the participants for the marketing webinar. I need to know how many non-anonymous users joined the session and extract their email addresses."

get_single_slido_export_by_id

The linchpin tool for the entire Slido integration. Because the previous tools return a 202 Accepted status with an export_id, the agent must use this tool to poll the API. The tool returns the job status (PROCESSING, COMPLETED, FAILED) and, once complete, provides access to the final data payload.

"Take the export ID (554433) you just generated for the Q&A export. Check its status using the Get Export tool. If it's still processing, wait 5 seconds and check again until you can read the final results."

To see the full inventory of tools, including endpoints for webex integrations, spaces, and individual poll voting values, visit the Slido integration page.

Workflows in Action

Exposing these tools to an LLM unlocks complex, multi-step automation. By chaining the async export tools with the polling tool, ChatGPT can perform deep analysis on massive town halls without writing a single line of Python script.

Here are two concrete workflows showing exactly how ChatGPT routes the execution.

Workflow 1: The Townhall Q&A Post-Mortem

Event organizers need to summarize audience sentiment immediately after a massive all-hands meeting. Doing this manually requires exporting CSVs, formatting them, and reading through hundreds of submissions.

"Analyze the Q&A from our '2026 Strategy Kickoff' event. Start by finding the event ID, then export all the questions. Filter out anything with less than 5 upvotes, and summarize the core themes the audience is most anxious about."

Execution Steps:

1. create_a_slido_exports_event: The agent initiates an event export.
2. get_single_slido_export_by_id: The agent polls the export ID until complete, retrieves the list of events, and extracts the ID for "2026 Strategy Kickoff" (e.g., event_1029).
3. create_a_slido_exports_question: The agent passes event_1029 into the payload and starts the Q&A export.
4. get_single_slido_export_by_id: The agent polls the new export ID until the question data is ready.
5. Data Processing: ChatGPT analyzes the raw JSON payload, filters objects where upvotes < 5, synthesizes the text, and generates a thematic summary of employee anxiety.

sequenceDiagram
    participant LLM as ChatGPT
    participant MCP as Truto MCP Server
    participant Slido as Slido API

    LLM->>MCP: Call create_a_slido_exports_question(event_id: 1029)
    MCP->>Slido: POST /exports/questions
    Slido-->>MCP: 202 Accepted (export_id: 8844)
    MCP-->>LLM: Return export_id: 8844

    note over LLM,Slido: ChatGPT processes the 202 and initiates polling

    LLM->>MCP: Call get_single_slido_export_by_id(id: 8844)
    MCP->>Slido: GET /exports/8844
    Slido-->>MCP: 200 OK (status: COMPLETED, data: [...])
    MCP-->>LLM: Return Q&A payload

Workflow 2: Audience Engagement Scoring

Marketing teams often want to cross-reference who attended an event with how they voted in specific polls to qualify leads.

"I need an engagement report for the 'Product Roadmap' webinar. Export the participants and the poll votes. Match the non-anonymous participants to their specific votes on the 'Which feature do you want most?' poll, and format it as a markdown table."

Execution Steps:

1. create_a_slido_exports_participant: The agent triggers the participant export to get names and participant_ids.
2. create_a_slido_exports_poll_vote_value: The agent triggers the granular vote export to get the raw answers tied to participant_ids.
3. get_single_slido_export_by_id: The agent polls both export IDs sequentially until both data sets are COMPLETED.
4. Data Processing: ChatGPT acts as a relational database in-memory. It joins the participant JSON objects with the poll vote JSON objects on the participant_id key, filtering out is_anonymous: true records, and outputs the requested markdown table.

Security and Access Control

Giving an AI agent access to potentially sensitive internal Q&A data requires strict governance. Truto MCP servers provide multiple layers of access control at the token level, ensuring your agents only access what they strictly need.

• Method Filtering: You can restrict an MCP server to only allow specific operation types. Setting methods: ["read"] ensures the agent can trigger exports and poll statuses, but cannot accidentally execute custom write operations if Slido introduces them.
• Tag Filtering: Slido resources in Truto are categorized by tags. You can configure the MCP server with tags: ["polls"], ensuring the LLM is physically incapable of seeing or calling the Q&A or participant toolsets.
• API Token Authentication: By default, the Truto MCP URL acts as a bearer token. For higher security, you can enable require_api_token_auth: true. This forces the client (like a custom LangGraph framework) to pass a valid Truto API token in the Authorization header, adding a strict secondary identity check.
• Expiration Controls: You can attach an expires_at ISO datetime to the MCP server. Truto uses distributed alarms to permanently destroy the server and its underlying token credentials at the exact millisecond of expiration, preventing lingering backdoor access.

Strategic Wrap-Up

Connecting Slido to ChatGPT transforms audience interaction data from static CSV exports into real-time, queryable intelligence. However, the architectural reality of Slido's asynchronous export API makes building point-to-point connectors a massive headache for engineering teams.

By leveraging Truto to generate a managed MCP server, you eliminate the boilerplate. Truto handles the dynamic tool generation, enforces strict rate limit headers for the client, and translates complex Slido schemas into LLM-native instructions. This allows your developers to focus on building high-value event analytics workflows instead of wrestling with pagination cursors and polling logic.