Collect feedback on prompt enhancement techniques to improve future responses by tracking helpfulness of applied methods.
MIT
134,708 tools. Last updated 2026-05-22 23:13
"MCP feedback enhancement" matching MCP tools:
- Refine a perspective using natural-language feedback. Adaptive AI understands your input, updates the perspective, and captures structured data to trigger appropriate actions, replacing rigid forms.MIT
- Record your speech to get a transcript and English feedback on pronunciation, grammar, and fluency. Use for voice conversations with coaching.MIT
- Retrieve feedback submission time-series data with summary totals and per-bucket counts for trend analysis.MIT
- Retrieve top-performing announcements from a LaunchNotes project ranked by engagement, open rates, click rates, feedback volume, or sentiment scores to analyze content performance.MIT
- Update an existing feedback record by ID, changing only its value, weight, and metadata while keeping trace linkage immutable.MIT
Matching MCP Servers
- AlicenseAqualityCmaintenanceMCP server that synthesizes user feedback from multiple sources into actionable insights. Aggregates reviews, support tickets, and survey responses to identify patterns and priorities.Last updated4MIT
- FlicenseAqualityCmaintenanceEnables AI assistants to access TestFlight beta tester feedback, including screenshots, crash logs, and text comments from App Store Connect. It works across any platform without requiring Xcode, using official API keys and optional browser automation for full text feedback.Last updated8
Matching MCP Connectors
Use your own Word templates to convert Markdown → DOCX/PDF/HTML from any MCP-compatible AI.
Independent SBB/CFF/FFS MCP — schedules, prices, tickets. By SwissTrip; not the official SBB MCP.
- Submit feedback on code review issues to mark them as fixed, dismissed, or false positives, helping improve future reviews.MIT
- Submit feedback for a trace with a rating value (e.g., thumbs up/down or 1-5 scale). Optionally add weight and metadata for categorization.MIT
- Retrieve detailed feedback information including customer data, reporter details, sentiment analysis, and associated items to understand user input context.MIT
- Retrieve feedback time-series data grouped by model to compare feedback volume and satisfaction across models over a specified period.MIT
- Retrieve raw feedback-score distribution time-series data with per-score buckets to analyze sentiment mix over time. Enterprise-gated.MIT
- Retrieve weighted feedback-score time-series data for calibrated quality metrics. Filters by time range, tokens, cost, and other parameters. Enterprise-gated.MIT
- Manage feature toggles for a LaunchNotes project, including feedback collection, roadmap display, ideas submission, RSS feed, voting, and SEO indexing.MIT
- Search and filter customer feedback in LaunchNotes projects by content, sentiment, importance, and status to analyze user input.MIT
- Report issues or share input on AgentLux. Choose category (bug, friction, confusion, suggestion, praise) and write detailed feedback to help improve the platform.MIT
- Retrieve the source code of an enhancement implementation by providing the enhancement spot and implementation name.MIT
- Retrieve available attributes for filtering MCP servers in the Glama MCP directory, enabling precise search and exploration through the Glama MCP API.MIT
- Find a Warhammer 40,000 enhancement by name. Get its points cost, detachment, and effect.MIT
- Creates an asynchronous video enhancement task. Supports uploading videos via URL or local file path, with configurable target resolution.MIT
- Create word cloud charts to visualize word frequency or importance through text size variation. Analyze common terms in social media, reviews, or feedback data.TypeScriptMIT