"The most widely used models" matching MCP servers:

Enables diagnosing long AI-agent traces by identifying the most likely root-cause steps, so users can quickly find where an agent run went wrong.

Enables checking and validating disk space usage via a single tool, reporting total, used, and available space with optional free space thresholds.

Enables MCP clients to query Kiro credit usage, including plan, used and remaining credits, reset date, and percentage used, through a read-only tool with optional cache refresh.

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

Estimates the environmental footprint of your AI use — energy (kWh), miles driven, water used for cooling, and CO₂ — plus a prompt-efficiency score, working with any AI client by measuring token usage.

Audits local Claude Code transcripts to show which connected MCP servers are actually used, idle, failing, or awaiting authorization, and recommends which to keep or disconnect.

Enables AI models to securely query and manage virtual machines and virtualized resources via the libvirt API through the Model Context Protocol.

Enables AI assistants to see which model IDs each provider's API has started listing since the last check, and to list the models a given provider currently serves with optional substring filtering, all without spending tokens.

Once configured, you can use natural language commands like: "Search for failed builds in the last week" "Why last deploy was failed" "Search the most failed builds" "Trigger a build for the main branch" "Show me recent builds for project X" "Pin the latest successful build" "Cancel the running bui

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.

Enables natural-language querying and analysis of AWS spending and model-invocation usage, including detailed breakdowns by service, region, account, day, and instance type.

Enables users to query and analyze cloud spending, EC2 cost breakdowns, service-level spend, and model usage logs through natural-language interactions. It also supports cross-account spend retrieval when configured.

Enables querying AWS spend data via Cost Explorer and Amazon Bedrock usage data from CloudWatch logs using natural language. Supports cross-account access, local and remote deployment, and integration with Claude Desktop and LangGraph agents.

Enables users to analyze AWS spend and Bedrock model usage through natural-language queries, including EC2 costs, service spend reports, and breakdowns by region, model, and user.

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.

Web analytics and AI session replay for agents: visitors, breakdowns, funnels, goals, revenue attribution, and AI-found site issues. Connects to the hosted Flowsery MCP server.

A read-only MCP server that reports BI pipeline readiness, blockers, and the next allowed action for governed Power BI workflows. It never writes files, executes warehouse work, or grants human approvals.

Relays error logs from other MCP servers when an LLM call fails and the returned error message is unclear, helping the model handle errors more intelligently.

A read-only kubectl MCP server for AI assistants. The kubectl verb is hardcoded in each tool (get/describe/logs/events) and user input only fills argument values — no mutation path even with malicious input, and Secret/ConfigMap values are never returned (metadata only). Returns structured JSON, plus one-shot k8s_triage (health scan) and k8s_inventory (cluster snapshot).

Enables agents to fetch app reviews from Apple's App Store and Google Play per country store, newest first, filtered by date or star rating, with each scheduled run returning only reviews not seen before. It also returns a per-app, per-country rating trend row with the current rating, rating count, change since the last run, and the star split of new reviews.