"Integrating Claude AI with Microsoft Outlook" matching MCP servers:

Enables search, analytics, and visualization of Claude Code sessions with MCP tools for session management, recovery, and insights.

A Model Context Protocol server that provides AI assistants access to AWS CloudWatch Logs, enabling browsing, searching, summarizing, and correlating logs across multiple AWS services.

Enables Claude Code sessions to check account rate-limit headroom, local token spend, and live session status, with read-only MCP tools for limits, summaries, token usage, and session listing.

MCP server that provides read-only analysis of Claude Code session transcripts, enabling queries about project activity, session details, and tool usage.

Read-only AMD GPU monitoring for Claude on Linux — VRAM, temperature, clocks, power, and running ML processes via rocm-smi.

An instrumented MCP server for debugging lost tool calls in Claude Desktop, using ping, echo, sized_echo, and slow_echo tools to record and analyze the full protocol flow.

Provides structured access to Claude Code agent log files, allowing agents to query assistant lines and tool calls without reading raw JSONL data.

Maintenance, recovery, and observability for Claude Code. CLI + MCP server + dashboard.

Provides comprehensive telemetry and usage analytics for Claude Code sessions, including token usage tracking, cost monitoring, and tool usage patterns. Enables users to monitor their Claude usage with detailed metrics, warnings, and trend analysis.

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.

Read-only systemd inspection for Claude on Linux — list units, status, journal, failed units, unit files.

Echo tool MCP server that extracts and logs W3C Trace Context from requests to verify propagation from Claude Code.

Enables read-only Kubernetes incident investigation through MCP tools for listing pods, describing resources, fetching logs, and searching runbooks.

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

Enables an LLM agent to investigate a Kong Gateway through fourteen read-only, schema-validated tools and a deterministic diagnostic engine, so it can trace routes, services, plugins and upstream health and explain faults such as why authentication is failing on a path. Every fact comes from the gateway itself, and the model only selects tools and writes the evidence-backed explanation.

Model Context Protocol server for Verlon AI that exposes gates, logs, recommendations, and experiments as MCP tools, enabling coding agents to inspect and manage AI infrastructure natively.

Enables AI agents to manage prompts and sessions via the ai-agent-logs API, with tools for health checks, login, listing/creating prompts, and retrieving sessions.

Enables LLMs to query telemetry data via the Spyglass AI agent, providing intelligent insights about application performance, errors, and bottlenecks.

A MCP server for tracking AI usage metrics and structured logs across applications. Monitor model calls, analyze usage patterns, track costs, and debug AI interactions.

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