What is the difference between Working and Short-Term Memory in this MCP?

The MCP models these as distinct tiers. Working Memory handles immediate, active context, while Short-Term Memory holds recently used, but not immediately active, information. The capability tracks the flow between them.

Can this MCP fix my agent's memory problems?

It doesn't fix the code, but it gives you the data to fix it. You use the capabilities to diagnose memory bottlenecks and calculate the precise changes needed to improve performance.

Does this MCP require me to manage credentials?

No. Vinkius hosts and manages this MCP. You connect your client once, and you get access to the entire calculator through the catalog.

What does 'thrashing risk' mean in the context of this capability?

Thrashing risk means the memory system is spending too much time moving data between tiers rather than processing it. The lifecycle capability detects this pattern, alerting you to potential instability.

Is this only for large language models?

No. This MCP is a deterministic engine for memory management. It applies to any agentic system that uses tiered memory hierarchies, regardless of the underlying LLM.

How does the system decide which memory to demote?

The system uses a scoring metric combining recency, frequency, and importance. When a tier reaches capacity, the memory with the lowest score is demoted to the next tier or evicted.

What is thrashing in the context of memory management?

Thrashing occurs when Working Memory utilization exceeds 95%, causing rapid, repetitive demotions and promotions that degrade agent performance.

Can I optimize my agent's performance?

Yes, you can use optimize_working_memory to calculate the minimum token capacity required to achieve a specific cache hit rate target.

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Connect Agent Memory Tier Calculator to ChatGPT and Claude

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. Pinpoint memory bottlenecks and boost 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 Tier Calculator.

01
Calculate memory lifecycle
Determines specific movements and current state of all memory tiers
02
Optimize working memory
Recommends the ideal Working Memory size
03
Simulate retrieval performance
Evaluates the efficiency of the current memory configuration

Real work

What teams actually run

Long-Term Conversational Agents

An agent needs to remember details from a week-long conversation. You use the memory lifecycle capability to ensure key facts are correctly promoted to Long-Term Memory.

Complex Data Retrieval Systems

A system must retrieve data quickly from massive knowledge bases. You run a performance simulation to guarantee the cache hit rate meets the 95% requirement.

Resource-Constrained Deployments

You are deploying an agent to a device with limited RAM. You use the optimization capability to find the smallest working memory size that still achieves acceptable performance.

System Debugging

The agent is failing intermittently. You check the memory state using the lifecycle capability to diagnose if the failure is due to a specific tier reaching capacity.

One connector, every AI

Agent Memory Tier 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 Tier 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

863ms average. Fast in production.

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

Daily averagePeak 1006ms

Oct 3Today

Fastest day: 732ms
Slowest day: 1006ms
14-day trend: Improving-6%

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 Tier Calculator, so you can see the experience inside your AI.

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

Agent Memory Tier Calculator Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_VjXg1lmsFMEFMjb9yRqgsOO6gwn4M5LRWbqCt3Ir/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 Tier Calculator capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "agent-memory-tier-calculator-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_VjXg1lmsFMEFMjb9yRqgsOO6gwn4M5LRWbqCt3Ir/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 Tier 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 Tier Calculator owners hand off.

This MCP is built for AI architects and ML engineers who aren't satisfied with basic agent functionality. If your agent needs to maintain context over long periods or handle complex data flows, you'll need this level of memory control. It gives you the deterministic data needed to tune performance.

01
AI Architect
Designs the overall system structure, using this MCP to model and predict memory bottlenecks across the entire agent pipeline.
02
ML Engineer
Implements the memory management logic, using the capabilities to calculate optimal capacity and test retrieval efficiency.
03
Prompt Engineer
Tuning complex prompts that rely on long-term context, using the lifecycle capability to ensure context isn't lost or misrepresented.

FAQ

Questions Agent Memory Tier Calculator owners ask.

01
What is the difference between Working and Short-Term Memory in this MCP?
The MCP models these as distinct tiers. Working Memory handles immediate, active context, while Short-Term Memory holds recently used, but not immediately active, information. The capability tracks the flow between them.
02
Can this MCP fix my agent's memory problems?
It doesn't fix the code, but it gives you the data to fix it. You use the capabilities to diagnose memory bottlenecks and calculate the precise changes needed to improve performance.
03
Does this MCP require me to manage credentials?
No. Vinkius hosts and manages this MCP. You connect your client once, and you get access to the entire calculator through the catalog.
04
What does 'thrashing risk' mean in the context of this capability?
Thrashing risk means the memory system is spending too much time moving data between tiers rather than processing it. The lifecycle capability detects this pattern, alerting you to potential instability.
05
Is this only for large language models?
No. This MCP is a deterministic engine for memory management. It applies to any agentic system that uses tiered memory hierarchies, regardless of the underlying LLM.
06
How does the system decide which memory to demote?
The system uses a scoring metric combining recency, frequency, and importance. When a tier reaches capacity, the memory with the lowest score is demoted to the next tier or evicted.
07
What is thrashing in the context of memory management?
Thrashing occurs when Working Memory utilization exceeds 95%, causing rapid, repetitive demotions and promotions that degrade agent performance.
08
Can I optimize my agent's performance?
Yes, you can use optimize_working_memory to calculate the minimum token capacity required to achieve a specific cache hit rate target.

Explore

More in Optimization

Connect Agent Memory Tier 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 lifecycle

Optimize working memory

Simulate retrieval performance

What teams actually run

Long-Term Conversational Agents

Complex Data Retrieval Systems

Resource-Constrained Deployments

System Debugging

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

Agent Memory Tier Calculator Connector

Claude Desktop

AI Architect

ML Engineer

Prompt Engineer

What is the difference between Working and Short-Term Memory in this MCP?

Can this MCP fix my agent's memory problems?

Does this MCP require me to manage credentials?

What does 'thrashing risk' mean in the context of this capability?

Is this only for large language models?

How does the system decide which memory to demote?

What is thrashing in the context of memory management?

Can I optimize my agent's performance?

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