The agent provides a verifiable report on how frequently specific brands are mentioned in AI coding agent scenarios, including statistical confidence intervals and strict reading rules to prevent overstating rankings.
The agent uses the ask10ais MCP server to query public rankings and scenario mention rates. It identifies the relevant scenario (e.g., 'Most Popular AI Coding Agents') and retrieves mention rates with 95% confidence intervals. Crucially, the agent must adhere to the returned 'reading_rules', such as not claiming one brand is ahead if confidence intervals overlap, and reporting absent brands as 'not mentioned' rather than 'unknown'. The output includes a citation template for accurate sourcing.
- Category: Research and knowledge
- Effect: Read-only
- MCP servers: ask10ais MCP (https://koskamo.com/explore/ask10ais-mcp)
- Source: MCP: query ask10ais measurements from your agent · ask10ais (https://ask10ais.com/en/agents)
- Source quote: "Every result comes with its own reading rules, so your agent won't overstate it."
- Playbook page: https://koskamo.com/explore/use-cases/retrieve-ai-coding-agent-brand-mention-rates
Prompt: Query the mention rates for GitHub Copilot and Cursor in the 'Most Popular AI Coding Agents' scenario. Summarize the results using the provided reading rules, ensuring you do not claim a lead if confidence intervals overlap. Include the citation template in your response.