Scorecard

Scorecard MCP Server

The Scorecard MCP server provides a comprehensive AI testing and evaluation platform that creates fast feedback loops for AI systems, shows how models behave with continuous evaluation, and helps catch problems early while shipping AI products that work.

Overview

The Scorecard MCP server provides a comprehensive AI testing and evaluation platform that creates fast feedback loops for AI systems, shows how models behave with continuous evaluation, and helps catch problems early while shipping AI products that work. This remote Model Context Protocol server offers 22 specialized tools designed for systematic AI testing, evaluation management, and performance optimization.

Server Details

• Server Name: Scorecard
• Remote URL: https://scorecard-mcp.dare-d5b.workers.dev/sse
• Total Tools: 22
• Transport Method: Server-Sent Events (SSE)

Key Capabilities & Value Proposition

Comprehensive Testing Framework

The server enables structured tests that provide clear, actionable insights, allowing teams to be confident in performance before going live. Key capabilities include:

• Project Management: Create and manage evaluation projects with hierarchical organization
• Testset Development: Build comprehensive test datasets with custom schemas and field mappings
• Testcase Management: Create, update, and organize individual test cases within testsets
• Evaluation Runs: Execute systematic evaluations and track performance over time
• System Definitions: Define AI system interfaces with input, output, and configuration schemas
• Configuration Management: Manage different system configurations for comparative testing

Advanced AI Evaluation Features

Scorecard helps make sense of AI performance with tools to test and evaluate AI systems, map out real scenarios, and bring clarity to AI performance while gaining insights, identifying risks early, and shipping with confidence.

• Continuous Monitoring: Real-time performance tracking and evaluation
• Structured Testing: Systematic approach to AI system validation
• Performance Analytics: Detailed insights into system behavior and effectiveness
• Risk Assessment: Early identification of potential issues and failure modes

Primary Use Cases & Target Audience

AI Development Teams

• LLM Application Developers: Test and validate language model integrations
• AI Product Managers: Monitor system performance and user experience metrics
• MLOps Engineers: Implement continuous evaluation pipelines
• Quality Assurance Teams: Ensure AI systems meet performance standards

Enterprise Applications

Organizations can get a pulse on how users interact with AI systems in real time with continuous evaluation, identify issues, monitor failures, and find opportunities to improve.

• Customer Service AI: Evaluate chatbot performance and response quality
• Content Generation Systems: Test AI-generated content for accuracy and relevance
• Recommendation Engines: Assess recommendation quality and user satisfaction
• Automated Decision Systems: Validate decision-making accuracy and fairness

Research & Development

Researchers can leverage systematic evaluation frameworks like MCPBench to conduct experimental evaluations on AI systems' accuracy, time, and token usage.

• Academic Research: Systematic evaluation of AI models and algorithms
• Comparative Analysis: Benchmark different AI systems against standardized metrics
• Performance Optimization: Identify areas for model improvement and tuning

Integration Benefits

ChatGPT Custom Connectors

Transform ChatGPT into a powerful AI evaluation assistant:

• Create and manage test datasets directly from chat
• Run automated evaluations on AI systems
• Generate performance reports and insights
• Monitor system health and performance metrics

Claude Custom Connectors

Enhance Claude's capabilities with structured testing tools:

• Design comprehensive evaluation frameworks
• Execute systematic AI testing workflows
• Analyze performance data and generate recommendations
• Implement continuous monitoring for AI applications

Technical Architecture

The Scorecard MCP server implements a comprehensive evaluation ecosystem with the following tool categories:

1. Project Management Tools (1 tool): list_projects
2. Testset Management Tools (5 tools): Create, update, list, delete, and retrieve testsets
3. Testcase Management Tools (5 tools): Comprehensive testcase lifecycle management
4. Evaluation Run Tools (2 tools): Create and update evaluation runs
5. Record Management Tools (1 tool): Create evaluation records
6. System Definition Tools (5 tools): Define and manage AI system interfaces
7. Configuration Management Tools (3 tools): Manage system configurations

The Scorecard platform creates a fast feedback loop for AI systems, enabling smarter testing, validated metrics, and improved products with continuous evaluation.