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AI Agents Overview

!!! info "TL;DR" Canviq's MCP server lets AI agents programmatically create surveys, query responses, analyze sentiment, and trigger workflows. Built for Claude Desktop and other MCP-compatible AI systems.

What is the MCP Server?

The Model Context Protocol (MCP) server is an HTTP + SSE endpoint that exposes Canviq's survey and feedback capabilities as tools AI agents can call. This enables agentic workflows like:

  • Automated survey creation — AI creates targeted surveys based on product milestones or user behavior
  • Real-time sentiment analysis — Claude Haiku analyzes open-text responses for sentiment trends
  • Workflow orchestration — Trigger automated actions when survey responses meet conditions
  • Feedback triage — Automatically categorize, tag, and route submissions to the right team

Use Cases

Product Team Automation

An AI agent monitors your product analytics. When it detects a drop in retention, it:

  1. Creates a 3-question survey targeting churned users
  2. Distributes via email to the last 100 churned accounts
  3. Analyzes responses for common themes
  4. Creates GitHub issues for the top 3 pain points
  5. Posts a summary to Slack

All without human intervention.

Support Deflection

When a user submits feedback about a known issue, the AI agent:

  1. Detects similarity to existing submissions using embeddings
  2. Links the new submission to the existing one
  3. Auto-responds with a status update and ETA
  4. Adds the user to a notification list for that issue

Roadmap Planning

Before quarterly planning, the AI agent:

  1. Queries all submissions with 20+ votes
  2. Runs sentiment analysis on comments
  3. Groups by category and theme
  4. Generates a prioritized list with justification
  5. Creates draft roadmap cards in your project management tool

How It Works

The MCP server exposes a tool registry. Each tool is a discrete operation like create_survey or get_analytics. AI agents authenticate with API keys, call tools via JSON-RPC over HTTP, and receive structured responses.

Tools use natural language parsing powered by Claude Sonnet. You can say:

"Create a 5-question NPS survey for users who signed up in the last 30 days"

The AI parses this into structured tool parameters:

{
  "tool": "create_survey",
  "params": {
    "title": "User Onboarding Experience",
    "questions": [
      { "type": "nps", "text": "How likely are you to recommend us?" }
      // ... 4 more questions
    ],
    "targeting": {
      "user_segment": "signup_date > now() - interval '30 days'"
    }
  }
}

The server validates scopes, applies rate limits, executes the tool, logs the action, and returns results.

Key Features

  • Natural language input — Describe what you want in plain English
  • Scope-based permissions — Fine-grained access control per agent
  • Audit logging — Every action logged for compliance (SOC 2, ISO 27001)
  • Rate limiting — Distributed rate limits via Upstash Redis
  • Durable execution — Workflow engine powered by Inngest
  • Real-time updates — SSE streams for long-running operations

What's Next