Connect Slido to Claude: Analyze Participant Feedback and Surveys

If your team uses ChatGPT, check out our guide on connecting Slido to ChatGPT and connecting Slido to AI Agents.

Slido powers audience interaction for thousands of enterprise town halls, webinars, and all-hands meetings. But while capturing Q&A upvotes, poll responses, and survey texts is easy, analyzing that raw data after the event is a manual, labor-intensive process. Event managers and HR teams spend hours exporting CSVs, categorizing anonymous questions, and trying to gauge audience sentiment.

Giving a Large Language Model (LLM) direct access to your Slido data solves this problem. By connecting Slido to Claude, your AI agent can automatically retrieve event data, analyze Q&A trends, cross-reference survey responses, and generate structured reports. To do this securely, you need a Model Context Protocol (MCP) server. This server acts as the translation layer between Claude's natural language tool calls and Slido's underlying REST APIs.

You can either spend weeks building, hosting, and maintaining this infrastructure yourself, or use a managed integration platform like Truto to dynamically generate a secure, authenticated MCP server URL in seconds. This guide breaks down exactly how to use Truto to generate a managed MCP server for Slido, connect it natively to Claude, and execute complex analytics 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 vendor APIs is painful. If you decide to build a custom MCP server for Slido, you are responsible for the entire API lifecycle.

Here are the specific architectural challenges you will face when integrating with Slido:

The Asynchronous Export Pattern Unlike many standard REST APIs that return data arrays synchronously or via cursor pagination, Slido relies heavily on asynchronous export jobs for its core data. When you request a list of participants, questions, or poll votes, the API does not return the data immediately. Instead, it returns an HTTP 202 Accepted response containing an export_id. You must then continuously poll a separate GET endpoint using that ID until the job status changes to completed, at which point you receive a download URL for the data.

Exposing this directly to an LLM without clear schema definitions is a disaster. The model expects data immediately and will hallucinate answers if it receives a 202 response object instead. A well-designed MCP server must provide precise tool descriptions that teach the LLM the correct state machine: trigger the export, capture the ID, wait, and poll for the result.

Deeply Nested Hierarchical Architectures Slido's data model is strictly hierarchical. To retrieve the actual answer a user selected on a poll (the "Vote Value"), you cannot simply query a flat table. You must traverse the hierarchy: Organizations have Users, Users own Events, Events have Rooms, Rooms contain Surveys and Polls, and Polls contain Votes. Providing raw API access to an LLM often results in the model confusing event_id with room_id, or failing to pass the required parent IDs to nested endpoints. The MCP tool generation layer must perfectly map these dependencies in its JSON schema.

Strict Rate Limiting and Backoff Requirements Because extracting Slido data requires active polling, it is incredibly easy for an automated AI agent in a loop to trigger rate limits. When a caller exceeds the allowed quota, Slido returns an HTTP 429 Too Many Requests error.

A factual note on rate limits: Truto does not automatically retry, throttle, or apply exponential backoff on your behalf when hitting rate limit errors. When the upstream Slido API returns an HTTP 429, Truto strictly passes that error through to the caller. However, Truto normalizes the upstream rate limit information into standardized HTTP headers (ratelimit-limit, ratelimit-remaining, ratelimit-reset) following the IETF specification. The caller (in this case, your AI agent orchestration layer or Claude) is entirely responsible for reading the ratelimit-reset header, pausing execution, and applying the correct retry backoff logic.

Instead of building this polling, schema mapping, and rate-limit header parsing logic from scratch, you can use Truto to generate dynamic MCP tools that handle the boilerplate.

How to Generate a Slido MCP Server with Truto

Truto creates MCP servers dynamically based on your connected integrations. When you authenticate a Slido account through Truto, the platform reads the Slido API documentation, extracts the required query and body parameters, and generates a precise JSON Schema for every available endpoint.

These tools are exposed via a single, secure JSON-RPC 2.0 endpoint that any MCP client can consume. You can create this server using either the Truto UI or the REST API.

Method 1: Via the Truto UI

For teams moving quickly, the Truto dashboard provides a point-and-click interface for generating MCP servers.

1. Log in to your Truto dashboard and connect your Slido account.
2. Navigate to the Integrated Accounts page and select your active Slido connection.
3. Click on the MCP Servers tab.
4. Click Create MCP Server.
5. Select your desired configuration (e.g., restrict tools to only read operations, or filter by specific tags like "surveys").
6. Click Generate and copy the resulting secure URL (e.g., https://api.truto.one/mcp/a1b2c3d4...).

Method 2: Via the Truto API

For platform engineers building multi-tenant AI products, MCP servers can be provisioned programmatically. You can trigger a POST request to the Truto API to generate a server scoped to a specific customer's Slido account.

curl -X POST https://api.truto.one/integrated-account/{integrated_account_id}/mcp \
  -H "Authorization: Bearer YOUR_TRUTO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Slido Analytics Agent Server",
    "config": {
      "methods": ["read", "create", "custom"]
    }
  }'

The Truto API will validate the request, ensure the integration is AI-ready, and return a robust JSON object containing the secure url. This URL encodes the specific tenant context, meaning no additional OAuth tokens need to be passed by the client.

{
  "id": "mcp-7f8a9b2c",
  "name": "Slido Analytics Agent Server",
  "config": { "methods": ["read", "create", "custom"] },
  "expires_at": null,
  "url": "https://api.truto.one/mcp/a1b2c3d4e5f67890abcdef"
}

Connecting the MCP Server to Claude

Once you have your Truto MCP server URL, connecting it to Claude requires zero custom code. The protocol ensures that Claude automatically fetches the tool schemas, descriptions, and required parameters upon initialization.

Method A: Via the Claude UI (Claude Desktop or Web)

Anthropic has made adding external tools incredibly simple via their user interface.

1. Copy the MCP server URL you generated from Truto.
2. Open Claude and navigate to Settings > Integrations > Add MCP Server (if you are adapting this for ChatGPT, go to Settings > Apps > Advanced settings > Custom connectors).
3. Give your connector a recognizable name (e.g., "Slido Analytics").
4. Paste the Truto URL into the Server URL field and click Add.
5. Claude will perform a handshake with the Truto server, retrieve the Slido tools, and instantly make them available in your chat context.

Method B: Via Manual Config File (Claude Desktop)

If you prefer to configure your environment via code, you can update Claude Desktop's manual configuration file to use an SSE (Server-Sent Events) transport layer.

Locate your claude_desktop_config.json file (typically found in ~/Library/Application Support/Claude/ on macOS or %APPDATA%\Claude\ on Windows) and add the following JSON block:

{
  "mcpServers": {
    "slido_analytics": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-sse",
        "--url",
        "https://api.truto.one/mcp/a1b2c3d4e5f67890abcdef"
      ]
    }
  }
}

Restart Claude Desktop. The application will initialize the SSE connection to Truto, pull down the Slido tool definitions, and bind them to the local model instance.

Slido MCP Hero Tools

Truto automatically generates comprehensive tools for the Slido API. Because Slido relies heavily on asynchronous exports, the most critical tools involve starting export jobs and polling for their completion. Here are the highest-leverage tools available to Claude.

Start Event Export

create_a_slido_exports_event

This tool triggers an asynchronous export of events in Slido. Because the model must poll for completion, it returns an HTTP 202 export job object with an export_id. The exported records will include critical metadata like event_id, owner_user_id, start and end dates, and timezone information.

"Trigger an export of all our Slido events created in the last 30 days. Capture the export ID and let me know when the job is initiated."

Start Q&A Question Export

create_a_slido_exports_question

This tool starts an asynchronous export of Q&A questions for a specific event or room. It is essential for audience sentiment analysis. The resulting payload includes question_id, upvotes, downvotes, score, whether the question was anonymous, and highlight status.

"Start an export for all Q&A questions from event ID 'evt_88392'. I need the data to find the questions with the highest upvote score."

Start Poll Vote Value Export

create_a_slido_exports_poll_vote_value

To analyze specific multiple-choice answers or ranking responses, you need the individual poll vote values. This tool triggers an export of detailed vote records, returning the poll_vote_value_id, rating score, multiple choice option IDs, and participant identifiers.

"Trigger a poll vote value export for the Q3 All-Hands event. Once the data is ready, I want to map the multiple choice option IDs to the actual text answers."

Start Survey and Quiz Export

create_a_slido_exports_surveys_quizz

This tool initiates an export of surveys and quizzes within a given room or event. It returns an export job object for polling, and the final data will include survey_quiz_id, type, and the total count of polls contained within the survey.

"Export all surveys and quizzes associated with the Employee Onboarding room, so we can analyze completion rates across the different survey types."

Get Export Status

get_single_slido_export_by_id

This is the critical polling tool. The AI agent uses this tool to check the status of any export job started by the tools above. It requires the id (the export ID) and returns the current status (e.g., pending, processing, or completed). Once completed, it provides the download link or payload containing the actual data.

"Check the status of export ID 'exp_9921'. If it is completed, retrieve the data and summarize the top three most upvoted questions."

List All Exports

list_all_slido_exports

This tool retrieves a list of all historical Slido exports on the account. It is useful for auditing past analytics jobs or finding a recently completed export without needing the specific ID. You can pass an optional type parameter to filter by export type (e.g., questions vs events).

"List all recent Slido exports filtered by the 'questions' type, and grab the results from the most recent completed job."

To see the complete inventory of available Slido tools, including detailed JSON schema requirements for every property, visit the Slido integration page.

Workflows in Action

Connecting Slido to Claude transforms raw, hierarchical API endpoints into a conversational analytics engine. Here is how specific personas use this setup to automate complex workflows.

Scenario 1: Event Manager Analyzing Post-Event Q&A

Event managers spend hours after large town halls sifting through hundreds of Q&A submissions to identify core themes, address unanswered questions, and report back to executives.

"Find the most upvoted Q&A questions from our 'Q4 Product Roadmap' event yesterday. Wait for the export to finish, retrieve the questions, and write a one-page summary highlighting the top three themes the audience was most concerned about, ignoring any questions with a negative upvote score."

Step-by-Step Execution:

1. Claude calls create_a_slido_exports_question, passing the specific event_id in the body payload.
2. Truto translates this into a POST request to Slido, returning an HTTP 202 with an export_id (e.g., 8472).
3. Claude reads the instructions and calls get_single_slido_export_by_id with ID 8472 to check the status.
4. If the status is pending, Claude waits and polls get_single_slido_export_by_id again.
5. Once completed, Claude ingests the JSON array of questions, filters out negative scores, identifies the themes based on the text context, and generates the final markdown summary for the user.

sequenceDiagram
    participant Claude as Claude Desktop
    participant Truto as Truto MCP Server
    participant Slido as Slido API

    Claude->>Truto: Call create_a_slido_exports_question
    Truto->>Slido: POST /v1/exports/questions
    Slido-->>Truto: 202 Accepted (export_id: 8472)
    Truto-->>Claude: Return export_id: 8472
    Claude->>Truto: Call get_single_slido_export_by_id (8472)
    Truto->>Slido: GET /v1/exports/8472
    Slido-->>Truto: Status: pending
    Truto-->>Claude: Return pending
    Note over Claude, Slido: Claude waits and retries
    Claude->>Truto: Call get_single_slido_export_by_id (8472)
    Truto->>Slido: GET /v1/exports/8472
    Slido-->>Truto: Status: completed, data payload
    Truto-->>Claude: Return completed Q&A data

Scenario 2: HR Analyst Processing Engagement Surveys

HR teams use Slido surveys to gauge employee sentiment. Manually mapping raw vote values to the actual survey questions across different departments is tedious and error-prone.

"Export the results from the 'Annual Team Engagement' survey. Cross-reference the poll vote values with the actual survey questions. I need a breakdown showing what percentage of participants answered 'Strongly Agree' to the remote work policy question."

Step-by-Step Execution:

1. Claude calls create_a_slido_exports_surveys_quizz to get the master list of surveys to locate the specific ID for the Annual Team Engagement survey.
2. Claude polls get_single_slido_export_by_id until the survey list is retrieved.
3. Claude extracts the survey_quiz_id and calls create_a_slido_exports_poll_vote_value to export the actual participant answers.
4. Claude polls get_single_slido_export_by_id to retrieve the vote data.
5. Claude processes the raw data, maps the multiple_choice_option_id to the string "Strongly Agree", calculates the percentages, and outputs a formatted report.

Security and Access Control

Giving an AI agent read and write access to enterprise event data requires strict security guardrails. Truto provides several configuration layers to ensure your MCP servers operate with least-privilege access:

• Method Filtering: When creating the server via the API or UI, you can pass methods: ["read"] in the configuration. This ensures the MCP server only exposes GET and LIST tools, physically preventing the LLM from executing POST, PUT, or DELETE operations.
• Tag Filtering: You can restrict the server to specific operational domains by passing tags: ["surveys"] or tags: ["questions"]. The server will dynamically drop any tools that do not match these integration tags, reducing the AI's attack surface and context window footprint.
• Extra Authentication Layer: By enabling require_api_token_auth: true, possession of the MCP URL is no longer sufficient to access the tools. The connecting client must also pass a valid Truto user session or API token in the authorization header.
• Automatic Expiration: You can provision temporary AI access by setting an expires_at ISO datetime. Once this timestamp is reached, Truto automatically schedules a secure cleanup, deleting the token and revoking access without requiring manual intervention.

Architecting the Future of Event Analytics

Manually exporting CSVs and running pivot tables on Slido data is an outdated workflow. By bridging the gap between Slido's asynchronous REST endpoints and Claude's tool-calling capabilities, you empower your teams to query engagement metrics, analyze sentiment, and summarize town halls using natural language.

Building this bridge from scratch requires managing complex state machines, strict rate limits, and deep hierarchical data structures. Truto abstracts this away entirely, turning Slido's raw API into a curated, LLM-ready MCP server in minutes.