"Understanding Cursor or Cline Context in Long-term Memory Systems" matching MCP servers:

AI coding usage analytics — track spend, model usage, and costs across Cursor and Claude Code sessions.

A FastMCP server for Cursor platform APIs enabling spend guardrails, cloud agent monitoring, and inter-agent inbox messaging.

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

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

Enables real-time monitoring of Cursor Pro usage limits and API quotas across different AI services. Tracks Sonnet 4.5, Gemini, and GPT-5 request usage with alerts when approaching subscription limits.

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

Windows tray process that exposes local ambient context (presence, foreground app, battery, etc.) as MCP tools with privacy classification and opt-in controls.

An MCP server that records agent execution metrics and exposes a Context Window Explorer to visualize exactly what entered the model's context window across sessions, tokens, and tool calls.

Context window usage estimation for AI coding agents via MCP, enabling proactive state preservation before compaction.

Enables assistants to read-only explore and analyze Grafana dashboards and data sources, execute queries against Prometheus and Azure Log Analytics, and run guided investigation workflows.

Provides persistent memory for MCP-compatible agents (like Copilot CLI) to save and recall knowledge across sessions, plus long-running monitoring tools.

Enables AI agents to access real-time Windows PC context including active window, system performance, screen time, productivity analytics, and historical usage through MCP tools.

Enables auditing MCP servers' tools by their estimated context token cost, ranking verbose names, descriptions, and schemas before they consume the agent window. Can run as an MCP tool so agents can budget their own context.

Provides a read-only interface to Kubernetes clusters, enabling LLMs to list pods, get pod status and logs, fetch deployment manifests, and perform pod health analysis with resource trend tracking.

Provides unified project context and monitoring capabilities including project health metrics, build diagnostics, Git integration, and infrastructure validation to initialize development sessions with comprehensive project information.

Enables Git repository operations and real-time monitoring via MCP tools, with support for WebSocket events, authentication, and observability.

Enables AI-powered debugging through structured logging with pattern recognition and intelligent analysis. Allows applications to write structured logs and receive actionable debugging insights based on error patterns and frequency analysis.

Enables AI assistants to query and analyze AI agent sessions from observability providers like Shepherd (AIOBS) and Langfuse, allowing users to debug agent runs, compare sessions, track performance, and analyze LLM usage patterns.

Reads local Codex rollout logs to expose context-window usage snapshots, remaining-token estimates, and log-event freshness through a read-only MCP tool.

Enhanced Home Assistant MCP server with 22 context efficient tools for smart home control, automation trace debugging, entity registry management, CEL expression queries, and long-term statistics.