"MEGA" matching MCP servers:

Persistent memory for AI coding agents

It gives AI agents one tool to access the Omega-API judge, which evaluates conversational turns and returns a decision and gate action (release, clarify, suppress, or none) to guide the agent's response.

AI agent provenance, trust, and auditability layer. VERITAS multi-gate scoring, Cortex approval gates, S.E.A.L. hash-chain audit ledger, and semantic RAG with cryptographic provenance tracking for every decision an agent makes.

Enables building and iterating on a software company through natural language, returning live preview URLs, without needing a browser.

Enables AI models to dynamically create and execute their own custom tools through a meta-function architecture, supporting JavaScript, Python, and Shell runtimes with sandboxed security and human approval flows.

Transforms complex Pega Platform interactions into intuitive, conversational experiences by exposing Pega DX APIs through the standardized Model Context Protocol, enabling AI applications to interact with Pega through natural language.

A server that transforms a standard Language Model into a dynamic multi-agent system where the model simulates both a Conductor (project manager) and Experts (specialized agents) to tackle complex problems through a collaborative workflow.

Enables AI agents to combine ambient multimodal perception, continuous episodic memory, and recency-decay context briefing for fast deterministic decisions and safe tool routing. It integrates with MCP clients to reduce unnecessary frontier LLM calls while enforcing token budgets and local-first memory retention.

Automates codebase review and documentation generation using specialized AI agents, integrating with MCP for AI workflows and Smithery for deployment.

Enables personal AI agents to combine Meta Ray-Ban audio/vision sensory grounding with long-term episodic life memory graphs and empathetic emotional resonance. Integrates with MCP-compliant clients via JSON-RPC stdio for ambient, context-aware interactions.

Enables personal AI agents to fuse ambient multimodal perception with continuous episodic memory streams, routing routine reflexive actions at low latency while reserving heavier reasoning for ambiguous plans. Memory records apply Ebbinghaus recency decay with contradiction resolution and user-controlled deletion.

Detects financial fraud and AI agent transaction risks using machine learning, behavioral biometrics, network graph analysis, and agent-to-agent protection.

Enables personal AI agents to integrate multimodal Meta Ray-Ban audio and vision grounding with long-term episodic life memory graphs and empathetic emotional resonance. Supports proactive, context-aware assistance through sensory inputs, ambient telemetry, and task goals.

Provides persistent, causal memory for AI agents with semantic recall, causal tracking, and importance-based forgetting through MCP tools.

Enables autonomous agents and multi-agent frameworks to discover and execute modular, class-based tools over SSE with a standardized response contract.