"Tools to Enhance Cursor AI Performance" matching MCP servers:

A Python-based MCP server that analyzes PostgreSQL query plans, suggests indexes, and checks pagination safety to help optimize database performance.

MCP server that wraps Cursor Enterprise API to enable AI agents to answer questions about team spending, usage, and model adoption.

Um servidor MCP completo para integração com Sentry no Cursor, oferecendo 27 ferramentas para monitoramento de erros, performance e saúde de aplicações.

A comprehensive, AI-powered performance analysis and monitoring platform for OpenShift/Kubernetes clusters. This project provides Model Context Protocol (MCP) servers for analyzing etcd, network, and OVN-Kubernetes components with deep performance insights, automated root cause analysis, and actionable recommendations.

Query Grafana logs, metrics, and dashboards from Cursor. Enables the AI to call your Grafana instance via tools without leaving the editor.

Enables observability and governance for local LLMs via Ollama, including auditing model usage, scanning prompts for secrets/PII, and enforcing allow/deny policies.

Presents a plausible, tool-rich workspace surface — file reading, credential listing, command execution, database queries, and configuration retrieval — that returns only synthetic results while logging every connection, tool call, and argument received. It enables researchers to measure what MCP clients and agents actually do when handed dangerous-sounding tools, including whether they follow injected instructions or complete a retrieve-then-use credential chain.

MCP server that diagnoses Apache Spark job failures and optimizes performance using stack-trace analysis and LLM providers, supporting EMR and local sources.

Provides read-only, governance-audited operations for OLVM and oVirt 4.5 engines, enabling inventory, health, capacity, and diagnosis queries over MCP and CLI with audit logging and budget guards.

Governed AI-ops for Ceph, providing root-cause health analysis and guarded destructive operations via a built-in governance harness with risk tiers, audit, and undo recording.

Collects page performance metrics using Playwright, CDP, and Lighthouse, and returns a normalized PerformanceReport.

An MCP server for managing and monitoring Docker, Docker Compose, and Kubernetes environments alongside Azure Application Insights. It enables advanced log filtering, container lifecycle management, and querying of cloud application traces and metrics.

Connects AI-powered tools to the Confident AI platform for evaluating, observing, and iterating on AI quality, enabling cloud evaluations, dataset management, prompt versioning, production tracing, and human annotations directly from editors like Cursor and Claude Code.

Connect AI agents like Claude, Cursor, and Gemini to historical Windows performance and privacy data gathered by the AppControl Windows app. This server enables natural language queries into CPU, GPU, and temperature history alongside system security events for deep local auditing.

MCP server that exposes Symfony profiler runtime data (requests, queries, logs) to AI agents like Claude Code for debugging and optimization.

An MCP server that analyzes SQL schemas, generates realistic test data, benchmarks queries, and provides optimization recommendations to improve database performance.

Enables AI assistants to query system health, metrics, service reconnaissance, run multi-node patrols, and send incident alerts across distributed infrastructure through MCP tools.

Provides AI agents with tools to retrieve live system statistics (CPU, memory) and safely evaluate basic arithmetic expressions.

Enables smart branch kiosks to run policyholder lookups, actuarial surrender-vs-loan comparisons, VIP queue token issuance, and BigQuery APM analytics through modular MCP tools.

Read-only MCP server for querying AI usage metrics (exact tokens, cost, latency, errors, productivity) from a local SQLite database, enabling charts and analysis in Claude Code and Cursor.