"Methods to Store and Learn Dynamic Context Memories" matching MCP servers:

Minimal agent telemetry store with OTLP/HTTP ingestion to Postgres and MCP streamable-http tools for querying logs, traces, and metrics.

An MCP server that connects to the Google Play Developer API to deploy apps, manage releases, respond to reviews, and monitor app health through an AI assistant.

A comprehensive MCP server for IT, DBA, and developer tasks, enabling error resolution, task automation, performance monitoring, automated documentation, and configuration management.

Provides read-only access to App Store Connect analytics, sales, finance, and customer review data, with tools to fetch and parse reports from Apple's Analytics Reports API and other report endpoints.

Enables management of App Store Connect resources including app reviews, TestFlight crashes, analytics reports, and Xcode Cloud workflows through natural language or AI agents.

Enables analysis and management of iOS/macOS apps via the App Store Connect API, including app management, reviews, sales reports, analytics, performance metrics, and TestFlight.

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.

Read-only MCP server that exposes governed operational records with provenance and validity windows so clients can check which evidence is still current before answering. It lets agents reconcile freshness, schedule state, and evidence specificity to determine whether scheduled automations are actually healthy, rather than trusting an expired dashboard summary.

Enables interaction with Google Cloud services including billing cost analysis, log querying, and metrics monitoring through natural language commands. Provides comprehensive tools for managing GCP resources, analyzing costs, detecting anomalies, and retrieving operational insights.

Enables inference engines to match longest common prompt token prefixes via a radix trie, so cached KV-cache blocks are reused instead of re-prefilled. This eliminates redundant prefill computation and lowers time-to-first-token, alongside paged attention allocation, speculative decoding verification, INT8 quantization, and latency telemetry.

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.

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

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 AI assistants to access App Store Connect data including financial metrics, subscription analytics, app performance data, and revenue insights. Provides real-time iOS app metrics through secure API integration with rate limiting and comprehensive reporting capabilities.

Exposes a synthetic issue tracker and pipeline warehouse as callable tools so an agent can answer operational questions about tickets, pipeline health, runs, incidents, and governed metrics with every claim cited to the exact tool call it came from. All writes are proposal-only, requiring human approval through a gated apply path that logs each step for audit.

Enables named, authenticated callers to run read-only introspection of a Google Cloud project and to read and write per-tenant notes that stay isolated between callers. Every request is scope-checked against a reviewed policy, rate-limited per caller, and audited without ever logging argument values.