"memory" matching MCP servers:

Enables capturing a tamper-evident, hash-chained local record of every AI agent tool call — both the decision before a tool runs and its outcome — so you can review what the agent actually did in order, detect edits, deletions, or reordering at the exact index, and export a signed evidence pack any third party can verify with no installs.

Enables AI agents to monitor server health and capacity, diagnose outages, inspect Docker and deployment status, and perform safe, bounded recovery actions via MCP without granting unrestricted shell or SSH access.

iOS leak hunting and performance investigation. Reads .memgraph and .trace files, classifies retain cycles against a 34-pattern catalog with Swift fix templates, bridges to source via SourceKit-LSP. 28 MCP tools, 34 catalog resources, 5 investigation prompts.

8 standalone MCP servers for SMBs — Guardrails (prompt injection, PII, secrets detection), FinOps (AI cost tracking and budgets), Observability (agent tracing), Smart Router (17+ models, 9 providers), Trust Score (governance scoring 0-100), Memory (persistent agent memory), ThinkSecure (runtime security + SOC2/GDPR audit), and A2A Lite (agent-to-agent protocol). 37 tools total, TNC credits

Tells an agent what went wrong with its own Ollama calls, in sentences with the fix: lost prompt cache and where the prompt stopped matching, context overflow, a model running partly on CPU, tool schemas crowding the prompt. Also the last turn in numbers and which local models fit in video memory. Local, read-only, nothing leaves the machine.

A lightweight npm MCP server for deep Redis diagnostics, offering tools for memory, slowlog, clients, keyspace, latency, and config analysis with AI-powered recommendations.

A locally-hosted MCP (Model Context Protocol) server that gives Claude real tools to debug Docker containers — fetch logs, inspect memory/CPU, and run diagnostic commands inside a container, all from a chat with Claude Desktop.

Provides read-only access to host system metrics (CPU, memory, disk), Docker container health/logs, and sandboxed log file analysis via MCP tools, enabling AI agents to monitor enterprise infrastructure safely.

MCP server for AI agent deployment health — gateway status, CPU/memory/swap, recent errors from journalctl/dmesg, skill registry integrity, upgrade outcomes, cron + disk usage. Each component gets HEALTHY/DEGRADED/CRITICAL classification with overall rollup.

Enables AI assistants to monitor and manage Linux infrastructure including services, logs, processes, disk, memory, ports, cron, nginx, Docker, and system health checks via the Model Context Protocol.

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

Identifies resource-intensive processes on macOS across CPU, memory, and network usage, with tools for detailed process filtering and system overview.

Live .NET runtime diagnostics for AI assistants. Ask Claude to diagnose memory leaks, GC pressure, LOH fragmentation, and thread starvation in any running .NET process — no code changes required.

A Linux system monitoring MCP server that provides real-time information on CPU, memory, disk, network, processes, Docker, security, and more via MCP tools.

Cryptographic accountability for AI agents. Ed25519-signed receipts for every MCP tool call. Constraints, chains, AI judgment, invoicing, and local dashboard included.

Lets an AI assistant inspect a Linux machine through ten fixed read-only tools covering load, memory, disks, processes, ports, interfaces, systemd units, the journal, and allowlisted log files. All output is size-capped, control-character-stripped and secret-redacted, and nothing in the package can write to the system.

Enables Claude to monitor CPU, memory, disk, processes, network connections, Docker containers, and system logs for system diagnostics and troubleshooting.

Enables MCP-capable LLM clients to perform read-only Linux system observation and OS algorithm experiments by exposing typed tools for memory, filesystem, process, and CPU scheduling data with controlled, safe boundaries.

FastMCP server with read-only tools to monitor Redis — queue depths, Celery queue status, connected clients, server/memory info, and per-database key counts

Self-hosted AI incident triage with checkpointed resume and episodic memory. Exposes investigate_incident, get_investigation, search_incident_memory, and export_postmortem tools over MCP.