MCPBench is a framework for evaluating the performance of MCP servers. It supports the evaluation of two types of tasks: web search and database query, is compatible with local and remote servers, and mainly evaluates accuracy, latency, and token consumption.

An AI-based NPM package analysis MCP server that provides real-time security scanning, dependency analysis, performance evaluation, and other functions. It integrates Claude and Anthropic AI technologies to optimize the management of the npm ecosystem.

NPM Sentinel MCP is an AI-based NPM package analysis server that provides real-time security scanning, dependency analysis, performance evaluation, etc. It supports integration with Claude and Anthropic AI to optimize NPM ecosystem management.

Nano Agent is an experimental small engineering proxy MCP server that supports multi-provider LLM models, used to test and compare the proxy capabilities of cloud and local LLMs in terms of performance, speed, and cost. The project includes a multi-model evaluation system, a nested agent architecture, and a unified tool interface, supporting providers such as OpenAI, Anthropic, and Ollama.

The Ollama-MCP-server is a protocol server that connects local Ollama LLM instances with MCP-compatible applications, providing functions such as task decomposition, result evaluation, and model management, and supporting standardized communication and performance optimization.

The Mandoline MCP Server is an AI assistant evaluation framework that provides tools for creating custom evaluation metrics, batch scoring, and performance analysis for AI assistants such as Claude and Cursor through the Model Context Protocol, helping AI continuously improve its own performance.

The PageSpeed MCP Server is a bridge that connects AI assistants with the Google PageSpeed Insights API, providing website performance analysis functions for AI models, including core performance indicators, SEO evaluation, accessibility audits, and resource optimization suggestions.

Ollama-MCP-server is a middleware server that connects the local Ollama large language model. It provides task decomposition, result evaluation, and model management functions through the Model Context Protocol, supporting standardized communication and performance optimization.

Performance Review Data MCP Server

AWorld is a multi - agent system framework aiming to bridge the gap between theoretical MAS capabilities and practical applications, providing a full - set solution from single - agent to multi - agent collaboration/competition. The project supports scenarios such as browser/mobile operations and GAIA benchmark testing, adopts a client - server architecture, integrates a rich toolchain, and includes performance evaluation and training functions.