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.

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.

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.

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.

How does the memory decay work?

Relevance decays exponentially. You can use calculate_memory_health to determine when a memory's relevance score falls below the 0.1 threshold, triggering eviction from short-term to long-term storage.

What is consolidation?

Consolidation is triggered when short-term memory utilization exceeds 80%. The system summarizes the contents and migrates them to long-term memory to prevent overflow.

Can I predict retrieval latency?

Yes, use estimate_retrieval_performance to calculate the expected latency based on the number of items requested and the vector search time.

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Connect Agent Memory Hierarchy Calculator to your AI

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Manage context decay and optimize retrieval performance.

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.

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 3 capabilities

We ship only the actions that matter.

Capability set01 / 01

01-03

3 capabilities in this set.

Part of 3 available through Agent Memory Hierarchy Calculator.

01
Calculate memory allocation
Determines the exact distribution of data across the three memory tiers
02
Calculate memory health
Evaluates the decay, fragmentation, and eviction needs of the current memory state
03
Estimate retrieval performance
Predicts the latency and operational impact of accessing the long-term memory tier

Real work

What teams actually run

Long-Term Chatbots

Building a customer service bot that needs to remember details from interactions that happened days apart.

Research Assistants

Creating an agent that reads massive documents and needs to recall specific, semantically linked facts later.

Simulation Agents

Developing an agent for a game or simulation that must maintain a consistent, evolving internal state over time.

Knowledge Base Crawlers

Designing a system that continuously ingests and categorizes data, requiring constant memory allocation checks.

One connector, every AI

Agent Memory Hierarchy Calculator works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

Building your own app? The connector is yours to use.

You don't need a client to put Agent Memory Hierarchy Calculator to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

898ms average. Fast in production.

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

Daily averagePeak 1087ms

Oct 3Today

Fastest day: 659ms
Slowest day: 1087ms
14-day trend: Stable+1%

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.

1In Claude Desktop, open Settings → Connectors.
2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
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

Guided setup for Claude? Set up Agent Memory Hierarchy Calculator in Claude and other AIs

See all the AI clients this connector works with ↑

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.
05
How does the memory decay work?
Relevance decays exponentially. You can use calculate_memory_health to determine when a memory's relevance score falls below the 0.1 threshold, triggering eviction from short-term to long-term storage.
06
What is consolidation?
Consolidation is triggered when short-term memory utilization exceeds 80%. The system summarizes the contents and migrates them to long-term memory to prevent overflow.
07
Can I predict retrieval latency?
Yes, use estimate_retrieval_performance to calculate the expected latency based on the number of items requested and the vector search time.

Explore

More in Optimization

Connect Agent Memory Hierarchy Calculator to Claude, Cursor, ChatGPT & more

Your AI does the work. We keep it safe and sound.

Each Connector runs inside its own sealed environment.

One switch stops every agent. Instantly.

Spending limits that pause instead of surprise.

We ship only the actions that matter.

01-03

Calculate memory allocation

Calculate memory health

Estimate retrieval performance

What teams actually run

Long-Term Chatbots

Research Assistants

Simulation Agents

Knowledge Base Crawlers

Building your own app? The connector is yours to use.

Agent Memory Hierarchy Calculator Connector

Claude Desktop

AI Engineer

ML Ops Specialist

Prompt Engineer

Does this MCP handle different types of memory?

What is the difference between STM and LTM?

Can I predict how fast my agent will retrieve data?

Is this for simple chatbots?

How does the memory decay work?

What is consolidation?

Can I predict retrieval latency?

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