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ClaudeChatGPTPerplexityGeminiMicrosoft CopilotRaycastMeta AIGrokZ.aiQwenKimi
DeepSeekMistralCursorVS CodeWindsurfJetBrainsClineLovableVercel AI SDKLangChain

Use Agent Memory Hierarchy Calculator with your AI.

Connect your account once and let the AI you already use work with it, without building another integration. Manage context decay and optimize retrieval performance.

Included with plan

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

Complete set · 3 capabilities

The complete Agent Memory Hierarchy Calculator capability set.

These are the exact actions your AI can choose when you ask it to work with Agent Memory Hierarchy Calculator.

Capability set01 / 01

01-03

3 capabilities in this set.

Part of 3 available through Agent Memory Hierarchy Calculator.

  1. 01

    Calculate memory allocation

    Determines the exact distribution of data across the three memory tiers

  2. 02

    Calculate memory health

    Evaluates the decay, fragmentation, and eviction needs of the current memory state

  3. 03

    Estimate retrieval performance

    Predicts the latency and operational impact of accessing the long-term memory tier

Observed, not estimated

809ms average. Fast in production.

Agent Memory Hierarchy Calculator is checked daily against the live service.

Daily averagePeak 973ms
Aug 20Today
Fastest day
691ms
Slowest day
973ms
14-day trend
Slowing+15%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 3 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Agent Memory Hierarchy Calculator, so you can see the experience inside your AI.

It does not authenticate your account with Agent Memory Hierarchy Calculator. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Agent Memory Hierarchy Calculator Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_cVg8Qwy9sVJEou5PgXqCjl1RjQ9oRvy4FJlE5XDI/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — Agent Memory Hierarchy Calculator capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "agent-memory-hierarchy-calculator-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_cVg8Qwy9sVJEou5PgXqCjl1RjQ9oRvy4FJlE5XDI/mcp"
    }
  }
}
  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Step-by-step instructions for each client are in the guide. How to connect

Who it's for

Built for the work Agent Memory Hierarchy Calculator owners hand off.

This MCP is built for technical roles that manage complex, long-running AI systems. If your agent needs to maintain context over hours or days, you need this level of memory control. It gives you the data points necessary to move beyond simple prompt engineering and into true system architecture.

  • 01

    AI Engineer

    Builds and debugs agents that require deterministic memory management and context tracking.

  • 02

    ML Ops Specialist

    Monitors the operational health of deployed agents, specifically tracking memory decay and fragmentation.

  • 03

    Prompt Engineer

    Optimizes agent prompts by understanding the underlying memory capacity and retrieval limits.

FAQ

Questions Agent Memory Hierarchy Calculator owners ask.

  • 01

    Does this MCP handle different types of memory?

    Yes, it is designed to handle episodic, semantic, and procedural memory types. It provides deterministic logic for both consolidation and eviction across these categories.

  • 02

    What is the difference between STM and LTM?

    Short-Term Memory (STM) holds summarized, recent history. Long-Term Memory (LTM) stores semantic knowledge using vector-based storage, allowing for retrieval of facts over time.

  • 03

    Can I predict how fast my agent will retrieve data?

    You can use the estimate_retrieval_performance capability. This predicts the latency and operational impact of accessing the long-term memory tier before you deploy the agent.

  • 04

    Is this for simple chatbots?

    No. This MCP is for complex agents that need reliable, long-running context. It provides the architectural controls needed when simple prompt engineering isn't enough.

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