Agent Memory Hierarchy Calculator Connector for AI agents.
3 live capabilities
Manage deterministic memory tiers for agentic reasoning
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Why people use Agent Memory Hierarchy Calculator
Solving agent context drift with Agent Memory Hierarchy Calculator
With this MCP, you stop treating memory like a single bucket. You gain a way to mathematically partition data so the agent always has the right level of detail for the task at hand. You get a predictable, stable cognitive structure instead of a chaotic pile of tokens.
What Vinkius changes
You get a deterministic way to manage an agent's cognitive load and data retention.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,400+ Connectors
- Real-world use case 01
Preventing context window overflow
An engineer building a long-running research agent uses calculate_memory_allocation to ensure the working memory doesn't hit the limit while keeping enough history in short-term memory.
- Real-world use case 02
Optimizing agent response speed
A developer uses estimate_retrieval_performance to see if their massive long-term vector database is going to make the agent too slow to be useful.
- Real-world use case 03
Cleaning up stale agent state
A workflow designer uses calculate_memory_health to detect when an agent's summarized history has decayed too much and needs a fresh consolidation.
Complete set · 3capabilities
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.
01—03
3 capabilities in this set.
Part of 3 available through Agent Memory Hierarchy Calculator.
- 01 Capability
Calculate memory allocation
Calculates the specific data distribution across working, short-term, and long-term memory. This helps you set the right capacity for each tier.
- 02 Capability
Calculate memory health
Checks for data decay and fragmentation within the memory tiers. Use this to decide when to trigger data eviction or consolidation.
- 03 Capability
Estimate retrieval performance
Predicts the latency of accessing long-term memory. It helps you understand the operational impact of your vector storage settings.
Set up in minutes
One URL. Then ask Agent Memory Hierarchy Calculator to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Agent Memory Hierarchy Calculator from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_cVg8Qwy9sVJEou5PgXqCjl1RjQ9oRvy4FJlE5XDI/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Agent Memory Hierarchy Calculator, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Agent Memory Hierarchy Calculator for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_cVg8Qwy9sVJEou5PgXqCjl1RjQ9oRvy4FJlE5XDI/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Agent Memory Hierarchy Calculator URL.
- Step 03
Save and start
Save the connection and enable Agent Memory Hierarchy Calculator in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"agent-memory-hierarchy-calculator": {
"url": "https://edge.vinkius.com/vk_preview_cVg8Qwy9sVJEou5PgXqCjl1RjQ9oRvy4FJlE5XDI/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Agent Memory Hierarchy Calculator
Open Agent mode in chat and ask: "Using Agent Memory Hierarchy Calculator, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"agent-memory-hierarchy-calculator": {
"url": "https://edge.vinkius.com/vk_preview_cVg8Qwy9sVJEou5PgXqCjl1RjQ9oRvy4FJlE5XDI/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Agent Memory Hierarchy Calculator
Ask Copilot: "Using Agent Memory Hierarchy Calculator, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"agent-memory-hierarchy-calculator": {
"url": "https://edge.vinkius.com/vk_preview_cVg8Qwy9sVJEou5PgXqCjl1RjQ9oRvy4FJlE5XDI/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Agent Memory Hierarchy Calculator
Open Cascade and ask: "Using Agent Memory Hierarchy Calculator, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"agent-memory-hierarchy-calculator": {
"url": "https://edge.vinkius.com/vk_preview_cVg8Qwy9sVJEou5PgXqCjl1RjQ9oRvy4FJlE5XDI/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Agent Memory Hierarchy Calculator
Ask Cline: "Using Agent Memory Hierarchy Calculator, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add agent-memory-hierarchy-calculator --transport http "https://edge.vinkius.com/vk_preview_cVg8Qwy9sVJEou5PgXqCjl1RjQ9oRvy4FJlE5XDI/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Agent Memory Hierarchy Calculator
Ask Claude: "Using Agent Memory Hierarchy Calculator, show me...". 3 tools are ready
Where the request belongs
Work Agent Memory Hierarchy Calculator can move forward.
This is for engineers and researchers building autonomous agents that need to maintain long-running, complex state without losing context.
AI Engineer
Designing the memory architecture for autonomous agents that need to handle long-term tasks.
LLM Researcher
Testing how different memory tier distributions affect agent reasoning and performance.
Agentic Workflow Developer
Optimizing the balance between context window usage and vector retrieval speed.
Build the capability set
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Bring your own AI
Change the model, client or framework. Keep Agent Memory Hierarchy Calculator connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
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Before you connect
Questions about Agent Memory Hierarchy Calculator.
The practical details behind the request, access and result.
How does the Agent Memory Hierarchy Calculator help with agent context limits?
It helps you mathematically distribute data across different tiers so you don't overflow your primary context window, keeping the agent focused on what matters.
Can I use the Agent Memory Hierarchy Calculator to speed up my AI agent?
Yes. By predicting retrieval latency and managing how data is moved to long-term storage, you can prevent the agent from becoming slow and unresponsive.
How does this MCP prevent my agent from forgetting things?
It provides capabilities to monitor memory health and decay, allowing you to consolidate or move data into long-term storage before it's lost to the context window.
Is the Agent Memory Hierarchy Calculator compatible with any AI client?
It works with any MCP-compatible client, including Claude, Cursor, and Windsurf, once you connect it through Vinkius.
Does this MCP manage the actual vector database?
No, it provides the logic and calculations to manage how data should be distributed and handled across your existing memory tiers.
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.
One connection away
Give your agent a direct line to Agent Memory Hierarchy Calculator.
Connect Agent Memory Hierarchy Calculator once. Keep it beside 6,400+ managed Connectors when the next task needs more.
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