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Connect Tool Selection Efficiency 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. Get faster, more reliable capability routing for your agents.

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

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.

A futuristic MCP client docking bay
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 Tool Selection Efficiency Calculator.

  1. 01

    Calculate routing metrics

    This capability calculates the best path for a task by evaluating efficiency scores and resource headroom. It helps your agent pick the most reliable option based on current performance.

  2. 02

    Summarize performance profile

    This capability provides a high level view of your toolset health. It aggregates data to show how your capabilities are performing overall.

  3. 03

    Validate tool availability

    This capability checks if a specific capability is actually ready to be used. It verifies if usage has hit maximum allowed calls per minute to prevent execution errors.

Real work

What teams actually run

01

Optimizing Agent Latency

An agent picks the fastest capability available for a time sensitive user request.

02

Preventing Rate Limit Hits

The agent checks capacity before calling a capability to avoid hitting API limits.

03

Toolchain Debugging

You analyze performance profiles to find which capability in a sequence is slowing things down.

04

Reliability Engineering

The agent uses routing metrics to choose the most stable capability for critical tasks.

One connector, every AI

Tool Selection Efficiency 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.

ClaudeClaudeChatGPTChatGPTGeminiGeminiPerplexityPerplexityGrokGrokMicrosoft CopilotMicrosoft CopilotCursorCursorVS CodeVS CodeWindsurfWindsurfJetBrainsJetBrainsClineClineLangChainLangChainVercel AI SDKVercel AI SDKLovableLovableZ.aiZ.aiRaycastRaycastQwen CodeQwen CodeKimi CodeKimi CodeLe ChatLe Chat

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

You don't need a client to put Tool Selection Efficiency 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

864ms average. Fast in production.

Tool Selection Efficiency Calculator is checked daily against the live service.

Daily averagePeak 1041ms
Oct 3Today
Fastest day
671ms
Slowest day
1041ms
14-day trend
Improving-16%

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 Tool Selection Efficiency Calculator, so you can see the experience inside your AI.

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

Tool Selection Efficiency Calculator Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_pCUHZoGluRfK0G0J8qJXVpL0thJqTM4A6JmMZfd3/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 — Tool Selection Efficiency Calculator capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "tool-selection-efficiency-calculator-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_pCUHZoGluRfK0G0J8qJXVpL0thJqTM4A6JmMZfd3/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 Tool Selection Efficiency Calculator in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Tool Selection Efficiency Calculator owners hand off.

This MCP is for developers and engineers building complex agentic workflows that require high uptime and predictable response times.

  • 01

    AI Engineer

    Uses the MCP to fine tune how agents select capabilities in production.

  • 02

    Agent Architect

    Relies on performance profiles to design more efficient capability chains.

  • 03

    DevOps Engineer

    Monitors capability availability and rate limit headroom to prevent system failures.

FAQ

Questions Tool Selection Efficiency Calculator owners ask.

  • 01

    How does this MCP help with agent reliability?

    It uses deterministic routing metrics to ensure the agent selects capabilities based on actual capability alignment and current capacity rather than guessing.

  • 02

    Can I use this with Cursor or Windsurf?

    Yes, this MCP is compatible with any MCP-compatible client including Cursor, Windsurf, and VS Code.

  • 03

    How do I know if a capability is about to hit its rate limit?

    You can use the validate_tool_availability capability to check if a capability's current usage is approaching its maximum allowed calls per minute.

  • 04

    What is tool_chain_latency?

    This value represents the total cumulative latency of all capabilities used within a specific fallback sequence.

  • 05

    Do I need to host this myself?

    No, Vinkius hosts and manages the MCP for you, so it is ready to use immediately after connection.

  • 06

    How is the recommended capability selected?

    The recommended capability is the one that maximizes the product of its efficiency score and its current rate limit headroom.

  • 07

    What is the purpose of the fallback chain?

    The fallback chain provides a prioritized list of alternative capabilities, ordered by capability match score, to ensure continuity if the primary capability fails.

  • 08

    Can I monitor the health of my entire toolset?

    Yes, you can use summarize_performance_profile to get high-level metrics like average latency and maximum success rate across all available capabilities.

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