Is this different from a simple router?

Yes. A simple router just sends the task to the first agent it thinks of. This MCP calculates the optimal path by analyzing capability intersection, performance metrics, and real-time load. It's a decision layer, not just a switch.

What kind of input does it need?

It requires a clear user intent. You can also provide it with a list of available agents, which it uses to calculate scores and identify missing skills.

Can it handle multiple steps?

Absolutely. The plan_multi_agent_composition capability is designed specifically to determine the correct sequence of agents when one single agent isn't enough.

Does it check if the plan is safe?

Yes. You can use validate_routing_health to check the plan for risks, such as low routing scores or large capability gaps, before you commit to running the workflow.

How does the engine select the best agent?

The engine uses calculate_routing_scores to compute a composite score for each agent based on capability match, success rate, latency, and current system load.

What happens if no single agent has all the required capabilities?

You can use plan_multi_agent_composition to identify the optimal sequence of multiple agents that, when combined, satisfy all requirements.

How can I check if a routing plan is reliable?

Use the validate_routing_health capability to check if the routing plan is risky due to low scores or high capability gaps.

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Connect Agent Capability Matching Engine 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. It ensures your AI workflow always uses the optimal agent.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

Waiting for input…

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 · 4 capabilities

We ship only the actions that matter.

Capability set01 / 01

01-04

4 capabilities in this set.

Part of 4 available through Agent Capability Matching Engine.

01
Analyze capability gaps
Identifies what capabilities are missing from the agent pool to satisfy a specific intent
02
Calculate routing scores
Determines the optimal agent(s) for a specific user intent by calculating composite scores
03
Plan multi agent composition
Determines the optimal sequence of agents required to fulfill an intent when no single agent is sufficient
04
Validate routing health
Checks if the current routing plan is risky due to low scores or high gaps

Real work

What teams actually run

Complex Customer Onboarding

When a user needs to update their profile, check billing, and generate a welcome guide, the MCP plans the sequence of three different agents.

Technical Documentation Retrieval

A request for a specific API endpoint requires searching documentation, cross-referencing code examples, and summarizing the result, all managed by the MCP.

Automated Research Reports

The system needs to gather data from multiple sources, analyze the trends, and write a summary. The MCP orchestrates the search, analysis, and writing agents.

Dynamic Content Generation

Generating a marketing campaign requires checking brand guidelines, writing copy, and creating an image concept. The MCP routes these three distinct tasks.

One connector, every AI

Agent Capability Matching Engine 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 Capability Matching Engine 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

894ms average. Fast in production.

Agent Capability Matching Engine is checked daily against the live service.

Daily averagePeak 975ms

Oct 3Today

Fastest day: 802ms
Slowest day: 975ms
14-day trend: Slowing+22%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 4 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 Capability Matching Engine, so you can see the experience inside your AI.

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

Agent Capability Matching Engine Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_pjXBUVcUztBQMt9Kffkjs0TwG0wBLAQejYsiOszR/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 Capability Matching Engine capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "agent-capability-matching-engine-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_pjXBUVcUztBQMt9Kffkjs0TwG0wBLAQejYsiOszR/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 Capability Matching Engine in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Agent Capability Matching Engine owners hand off.

This MCP is for technical teams building complex, multi-step AI systems. If your application relies on routing tasks between several specialized agents, this capability provides the necessary intelligence layer. It moves your AI architecture from simple calls to reliable, orchestrated workflows.

01
AI Architect
Designs the overall system structure, using this MCP to map user intent to the correct agent sequence.
02
ML Engineer
Implements the decision-making logic, relying on the scoring and gap analysis capabilities for robustness.
03
Workflow Developer
Builds and tests multi-agent compositions, using the planning capabilities to ensure the workflow executes correctly.

FAQ

Questions Agent Capability Matching Engine owners ask.

01
Is this different from a simple router?
Yes. A simple router just sends the task to the first agent it thinks of. This MCP calculates the optimal path by analyzing capability intersection, performance metrics, and real-time load. It's a decision layer, not just a switch.
02
What kind of input does it need?
It requires a clear user intent. You can also provide it with a list of available agents, which it uses to calculate scores and identify missing skills.
03
Can it handle multiple steps?
Absolutely. The plan_multi_agent_composition capability is designed specifically to determine the correct sequence of agents when one single agent isn't enough.
04
Does it check if the plan is safe?
Yes. You can use validate_routing_health to check the plan for risks, such as low routing scores or large capability gaps, before you commit to running the workflow.
05
How does the engine select the best agent?
The engine uses calculate_routing_scores to compute a composite score for each agent based on capability match, success rate, latency, and current system load.
06
What happens if no single agent has all the required capabilities?
You can use plan_multi_agent_composition to identify the optimal sequence of multiple agents that, when combined, satisfy all requirements.
07
How can I check if a routing plan is reliable?
Use the validate_routing_health capability to check if the routing plan is risky due to low scores or high capability gaps.

Explore

More in Routing

Connect Agent Capability Matching Engine 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.

01-04

Analyze capability gaps

Calculate routing scores

Plan multi agent composition

Validate routing health

What teams actually run

Complex Customer Onboarding

Technical Documentation Retrieval

Automated Research Reports

Dynamic Content Generation

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

Agent Capability Matching Engine Connector

Claude Desktop

AI Architect

ML Engineer

Workflow Developer

Is this different from a simple router?

What kind of input does it need?

Can it handle multiple steps?

Does it check if the plan is safe?

How does the engine select the best agent?

What happens if no single agent has all the required capabilities?

How can I check if a routing plan is reliable?

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