Can I use this for both hypothetical and actual testing?

Yes, the framework supports 'Hypothetical Tracing Mode' for reasoning about UI output and 'Actual Tool Calling Mode' for verifying real HTTP endpoint responses.

Does this skill require external tools?

Yes, the framework utilizes Playwright to navigate through the frontend of your application for turn-based evaluation.

How does this framework assess product fit?

It evaluates if the tool-to-widget chain delivers the promised value proposition to a specific persona, comparing the experience against manual or non-AI alternatives.

What is an MCP failure pattern?

These are specific UX issues unique to AI-driven apps, such as 'tool ping-pong' (sequential calls that should be batched) or 'widget mismatch' (displaying data in an inappropriate format).

MCP Evaluation Framework

Name: MCP Evaluation Framework
Author: mberto10

bymberto10

0•

Security & Testing

Evaluates Model Context Protocol (MCP) applications for product fit, value delivery, and user experience using automated persona-based walkthroughs.

The MCP Evaluation Framework is a specialized skill designed to bridge the gap between technical MCP tool implementation and actual user value. It systematically assesses how users interact with MCP-powered applications by simulating target personas and walking through UI flows via Playwright. By identifying specific failure patterns like 'tool ping-pong' or 'widget mismatch,' it provides actionable feedback across tool schemas, outputs, and interface layers, ensuring that AI-driven features deliver a compelling and repeatable product experience.

Key Features

01Turn-by-turn value delivery assessment for tool-to-widget transitions

02Integrated product fit assessment to measure repeat-use motivation and competitive advantage

03Categorized improvement reports for Tool Schema, Output, Widget, and Flow layers

040 GitHub stars

05Persona-driven UI walkthroughs using Playwright automation

06Detection of seven MCP-specific failure patterns including over-clarifying and error opacity

Use Cases

01Auditing an MCP-powered dashboard to ensure widgets accurately reflect underlying tool data

02Validating the product-market fit of new AI agents by simulating end-user success criteria

03Identifying conversational friction and unnecessary tool calls in LLM-driven interfaces

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add mberto10/mberto-compound mcp-evaluation-framework

For use in Claude.ai and ChatGPT

Download Skill