"Understanding the Concept of LLM Context" matching MCP servers:

Supply chain risk scoring for npm, PyPI, and GitHub repos

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.

Free remote MCP for Core Web Vitals metrics by CMS, CDN, and framework (Chrome field data + multi-site crawl). No auth. Endpoint: https://www.corewebvitals.io/api/state-of-cwv/mcp

Independent latency and uptime data for 46 hosted AI inference APIs, probed every 5 minutes from 4 regions with no gateway, plus a verified model deprecation calendar and Claude Code / Codex speed and accuracy drift. Four read-only tools, no API key; data CC BY 4.0.

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.

Enables agents to check remaining Anthropic and OpenAI rate-limit quotas with reset times, plus Hermes Agent token usage, so bots can throttle or stop before hitting limits.

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 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.

LLM cost optimization and provider usage visibility for MCP-capable agents.

Enables benchmarking of Large Language Model APIs by measuring performance metrics such as generation throughput, prompt throughput, and Time To First Token (TTFT) with configurable concurrency levels and parameters.

An MCP-powered AI platform for satellite mission control, enabling space weather analysis, orbital collision detection, satellite health monitoring, and secure command validation through natural language.

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.

Provides LLMs with network intelligence capabilities including PTR record lookups, ASN analysis, traceroute enrichment, and real-time network monitoring through The Aleph API.

MCP server for Tessera's LLM cost-optimization layer. Anchored spend, drift detection, recommendations as typed tools.

MITM proxy MCP server that intercepts and verifies AI agent tool calls, detecting fabrication, tracking costs, verifying outcomes, and inferring user satisfaction.

An MCP server that functions as an intelligent gateway for multiple LLM backends including OpenAI, Claude, and Ollama. It supports automatic provider fallback, streaming responses via Server-Sent Events, and real-time monitoring for robust AI integration.