"Understanding how memory is stored or memory storage methods" matching MCP servers:

A zero-dependency Node.js playground for debugging how MCP clients authenticate to an MCP server over HTTP.

Persistent encrypted memory for AI agents. E2E encrypted private vaults, shared knowledge commons, topic channels, and agent-to-agent DMs. 23 MCP tools, free, no API key needed.

Enables AI assistants to keep and retrieve private context locally, with verifiable receipts and optional Solana anchoring for public proofs.

A tool for detecting and cleaning Java memory shells via local or SSH remote execution. It enables AI agents to scan Java processes, analyze suspicious class code, and safely remove memory shells after user confirmation.

Enables MCP-compatible AI agents to save encrypted, DID-signed session checkpoints and resume the latest state across sessions, while keeping secrets and credentials in a local vault.

MCP server for end-to-end encrypted secret storage and retrieval, enabling LLM agents to fetch secrets by name via one-time URLs while keeping plaintext out of model context and logs.

AI-powered memory dump analysis using Volatility 3 for digital forensics investigations. Enables process analysis, malware detection, network forensics, timeline generation, and anomaly detection with support for Claude, Llama, and other LLMs.

An MCP server that reads a file's embedded C2PA Content Credential to report whether it declares AI generation or a real-world capture, and checks whether that declaration's signature still verifies against the file's current bytes.

Guardrails for AI coding agents that enforces rules via compliance receipts, blocking destructive actions and syncing rules across all AI tools.

A demonstration server that reveals security risks by accessing sensitive environment variables, illustrating how MCP tools can potentially leak user data without explicit consent.

Enables MCP clients to triage SOC alerts while enforcing a trust firewall across retrieval, memory, and privileged actions. It exposes tools for alerts, logs, memory, and alert actions, with defenses that refuse risky operations in the presence of attacker-controllable content.

Enables AI agents to query and record SOC analyst reasoning via a knowledge graph, allowing access to institutional memory from Splunk.

Enables secure, human-approved AI memory from Gmail messages for Claude, with provenance, risk inspection, expiry, and revocation controls.

Pay-per-call API that verifies whether a domain belongs to a real business. Returns a verdict (real/likely_real/uncertain/likely_fake/fake), a 0-100 score, and signals (WHOIS via RDAP, SSL via Certificate Transparency, homepage LLM judgment, contacts, social) — for KYB, vendor screening, fraud checks, and lead qualification.

Aziel Runtime is a node-meshed MCP software orchestration suite for digital forensics, auditing, provenance, research, intelligence-support, and secure agent workflows, with a single gated execution path, compartmentalized engines, verifiable receipts, temporal integrity, and explicit capability refusal. It is not merely an API aggregator or generic tool router.

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

An MCP front-end for AnB. The tool surface is "use-don't-reveal": no tool returns a plaintext secret. Reveal paths require a TTY the server doesn't have, so the no-reveal guarantee is structural, not prompt-based — it holds even against a prompt-injected agent. Runs as a dedicated, narrowly-scoped AnB identity; exec is default-deny (only allowlist rules tagged scope=mcp run); command output

qURL is the portal to the invisible internet -- expiring, scope-limited URLs that let AI agents reach services no one else can see.

Securedact MCP is a local-first privacy layer for MCP-compatible AI workflows. It detects, reviews, redacts, and audits sensitive information before approved sanitized content is used downstream.

Non-custodial memory MCP server with client-side encryption, enabling secure remember, recall, forget, and share operations with post-quantum security.