MCP Performance Analysis Server

MCP Performance Analysis Server is an MCP server that inspects mobile application performance monitoring metrics to identify critical performance anomalies. It connects development environments such as Cursor to lightweight Python-based analytical tools that process performance data feeds, such as memory log CSV files. Built for mobile app developers and performance engineering teams, the server enables automated diagnostics to pinpoint severe regressions without forcing engineers to comb through voluminous monitoring dumps. By filtering raw profiling telemetry against predetermined threshold criteria, it highlights only pressing issues that require immediate intervention. The tool focuses specifically on serious operational defects, evaluating metrics like excessive physical memory usage and abnormal view component allocations. Teams can run the server locally within their editor workspace or host it remotely to share performance diagnostic capabilities across multiple developers. Findings are delivered back to the client interface in concise alerts, helping engineers quickly spot memory leaks and view bloat during continuous testing and code iteration.

Category: Developer Tools & Code Intelligence

Tags: code analysis, optimization, performance, profiling

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How to install and configure MCP Performance Analysis Server

1. Clone the repository and enter the directory: bash git clone git@github.com:DaSheng1994/mcp_analyze_quality.git cd mcp_analyze_quality 2. Create and activate a Python virtual environment, then install dependencies: bash python3 -m venv .venv source .venv/bin/activate pip install -r requirements.txt 3. Configure the MCP client by editing ~/.cursor/mcp.json: json { "mcpServers": { "performance-analyzer": { "command": "/path/to/your/project/.venv/bin/python", "args": ["/path/to/your/project/main.py"], "cwd": "/path/to/your/project" } } } 4. Restart Cursor completely to start using the server.

What you can do with MCP Performance Analysis Server

• Flag physical memory consumption anomalies whenever process VmRSS metrics exceed the 1.3GB threshold. - Detect view hierarchy leaks when active UI views expand by more than 700 instances. - Ingest and parse remote CSV memory telemetry files directly through developer chat commands. - Enforce custom mobile performance quality criteria by adapting the server's rule configuration markdown files.

Key facts

• https://github.com/DaSheng1994/mcp_analyze_quality
• Developer Tools & Code Intelligence, Monitoring & Observability
• code analysis, optimization, performance, profiling

Part of MCP Servers

What is MCP Performance Analysis Server?

MCP Performance Analysis Server is a specialized Model Context Protocol tool designed to monitor and analyze mobile application performance logs. It flags severe performance regressions and memory bottlenecks, presenting concise diagnostic alerts directly to the user.

How do I install MCP Performance Analysis Server?

Clone the git repository, set up a Python virtual environment, and install the dependencies from requirements.txt. Then, register the server path and executable inside your Cursor configuration file at ~/.cursor/mcp.json and restart the editor.

Which MCP clients work with MCP Performance Analysis Server?

The server documentation specifically details configuration and integration for Cursor, but because it utilizes standard Model Context Protocol conventions, it can also function with any MCP-compliant developer client.

What critical issues does MCP Performance Analysis Server detect?

By default, the server detects severe physical memory threshold breaches when VmRSS exceeds 1.3GB, and flags excessive UI view growth when component counts increase by more than 700 items.

Can I customize the performance analysis rules?

Yes. Analysis logic and threshold settings can be customized by modifying the rules defined within the .cursor/rules/quality-rules.mdc file in the project repository.