"Multi-Agent Systems with Shared Memory Storage" matching MCP servers:

AI-powered VMware storage management for vSphere. Manage datastores, iSCSI targets, and vSAN via natural language with 11 MCP tools.

Read-only Azure storage intelligence MCP server enabling scoped investigations, risk analytics, cost savings simulation, data health checks, and admin tenant-wide discovery across a synthetic storage estate.

Enables managing multiple Microsoft Clarity projects by routing requests per client name using a token map, proxying the official Clarity MCP server with tools to list and switch clients.

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.

An MCP server that pulls PageSpeed Insights and Google Search Console data across multiple owned sites, enabling cross-site performance reports and synthesis via Claude.

Registers a set of inert, tempting decoy tools with any MCP-capable client so that any call from a hijacked or prompt-injected agent instantly raises an alert with full attack context and a per-call trace token. Also plants worthless canary tokens in honeypot files that trigger zero-false-positive leak alerts when they surface in agent output, files, or outbound requests.

MCP server for AI agent observability, providing trace and span logging, search, latency/tokens/cost metrics, and anomaly detection using an in-memory buffer.

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

MCP server exposing AI agent observability tools: list, inspect, and deterministically replay recorded agent execution traces for debugging failures.

Enables AI agents to integrate with IBM Storage Insights for seamless observability and diagnosis of registered storage assets. It provides tools to retrieve alerts, performance metrics, and system configurations via the IBM Storage Insights External APIs.

Exposes rate-limit and usage data for AI coding agent CLIs as MCP tools, enabling querying of usage snapshots and reset times.

An MCP server that provides append-only, tamper-evident local receipts for AI agent actions, capturing command executions, outputs, and handoff evidence.

Local-first observability for AI agent stacks. Provides MCP tools for agents to check health, shadow configs, and burnout metrics proactively.

MCP-based production monitoring agent for manufacturing OEE, providing real-time monitoring, downtime root-cause analysis, and automated alerts through integration with SQL Server, MES APIs, and incident management systems.

Exposes network-monitoring tools (query metrics, analyze windows, compare, logs, status, runbooks, speed tests) as an MCP server for agentic workflows. Designed with evaluation suites, cost-aware model routing, and semantic tool retrieval.

Provides cost analysis of AI agent usage by previewing scrubbed metadata before upload and analyzing spend, cache-hit rates, and waste recovery through a local or remote server.

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.

Tracks AI agent token usage and spending in real time, with budget alerts, per-task cost breakdown, and a visual dashboard.

Enables debugging of distributed transactions by continuously ingesting Docker container logs, indexing them by trace/request ID, and exposing MCP tools to search, tail, and correlate logs across services.

Unified MCP safety server that detects prompt injection (75 patterns), scans LLM outputs for leaked secrets/PII, enforces API cost budgets, and creates signed audit trails. Zero ML dependencies, pure Python.