What kind of data does this MCP need to run?

The MCP requires data about your startup cohort's skill needs, the available mentors' expertise, and the current engagement levels. You feed this data into your AI client's context.

Is this capability just for large accelerators?

No. While it handles large cohorts, it works anytime you need to systematically check alignment. You can use it to evaluate any group of mentors against any set of required skills.

Does it tell me which mentors are the best fit?

Yes, by using the evaluate_match_quality capability, you get a specific score that quantifies how strong the alignment is between a mentor and a startup.

What does 'match coverage' mean?

Match coverage tells you what percentage of the total skills needed by the startups are currently covered by the expertise of the available mentor pool.

How is match coverage calculated?

The analyze_match_coverage capability calculates coverage by comparing the unique skill requirements of all startups against the skills provided by assigned mentors.

Can I check if a mentor is over-leveraged?

Yes, use the calculate_utilization_rate capability to measure mentor hour allocation and ensure workload balance across the cohort.

What factors influence the match quality score?

The evaluate_match_quality capability determines the score based on the intersection of mentor expertise with startup needs and the alignment of the mentor's experience with the startup's current stage.

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Connect Accelerator Mentor Match 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. Ensure every startup gets the right expertise, every mentor stays balanced.

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 Accelerator Mentor Match.

01
Evaluate match quality
Provides a granular assessment of how good the specific pairings are based on expertise and stage
02
Analyze match coverage
Determines how effectively the available mentors cover the total skill requirements of the startup cohort
03
Calculate utilization rate
Measures the efficiency of mentor hour allocation to ensure workload balance

Real work

What teams actually run

End of a Cohort Cycle Review

Run an analysis to see if the mentor pool covered all the technical and business needs of the entire group, identifying areas for improvement in the next cycle.

Onboarding a New Batch

Before the new startups even meet anyone, check the match coverage to know what resources you need to bring in immediately.

Addressing Underutilized Mentors

Check the utilization rate to identify mentors who have capacity and can be assigned to startups needing help.

High-Stakes Pairing Decisions

When a startup needs a critical mentor, use the match quality score to confirm the pairing is optimal before scheduling the meeting.

Automate a task

Put Accelerator Mentor Match to work on one thing.

Step-by-step automations your AI runs through this Connector: the workflow, the prompts to start with, and the exact capabilities behind each move.

One connector, every AI

Accelerator Mentor Match 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 Accelerator Mentor Match 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

892ms average. Fast in production.

Accelerator Mentor Match is checked daily against the live service.

Daily averagePeak 1102ms

Oct 3Today

Fastest day: 726ms
Slowest day: 1102ms
14-day trend: Slowing+27%

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 Accelerator Mentor Match, so you can see the experience inside your AI.

It does not authenticate your account with Accelerator Mentor Match. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Accelerator Mentor Match Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_20phNeormGQUqEuEuOZY51qFatUpfRT4rfvGwoNk/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 — Accelerator Mentor Match capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "accelerator-mentor-match-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_20phNeormGQUqEuEuOZY51qFatUpfRT4rfvGwoNk/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 Accelerator Mentor Match in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Accelerator Mentor Match owners hand off.

This MCP is built for program directors and operational managers running structured accelerator programs. If your job involves matching high-potential startups with specialized industry experts, this capability saves you countless hours of manual analysis. You hand off the complex data crunching to your AI client, getting actionable insights instead of raw numbers.

01
Program Director
Determines overall program health by checking if the mentor pool meets the total skill requirements of the cohort.
02
Operations Manager
Monitors mentor workload to ensure equitable distribution of time and resources across all participants.
03
Venture Partner
Quickly assesses the quality of specific mentor-startup pairings to recommend immediate next steps.

FAQ

Questions Accelerator Mentor Match owners ask.

01
What kind of data does this MCP need to run?
The MCP requires data about your startup cohort's skill needs, the available mentors' expertise, and the current engagement levels. You feed this data into your AI client's context.
02
Is this capability just for large accelerators?
No. While it handles large cohorts, it works anytime you need to systematically check alignment. You can use it to evaluate any group of mentors against any set of required skills.
03
Does it tell me which mentors are the best fit?
Yes, by using the evaluate_match_quality capability, you get a specific score that quantifies how strong the alignment is between a mentor and a startup.
04
What does 'match coverage' mean?
Match coverage tells you what percentage of the total skills needed by the startups are currently covered by the expertise of the available mentor pool.
05
How is match coverage calculated?
The analyze_match_coverage capability calculates coverage by comparing the unique skill requirements of all startups against the skills provided by assigned mentors.
06
Can I check if a mentor is over-leveraged?
Yes, use the calculate_utilization_rate capability to measure mentor hour allocation and ensure workload balance across the cohort.
07
What factors influence the match quality score?
The evaluate_match_quality capability determines the score based on the intersection of mentor expertise with startup needs and the alignment of the mentor's experience with the startup's current stage.

Explore

More in Business

Connect Accelerator Mentor Match 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

Evaluate match quality

Analyze match coverage

Calculate utilization rate

What teams actually run

End of a Cohort Cycle Review

Onboarding a New Batch

Addressing Underutilized Mentors

High-Stakes Pairing Decisions

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

Accelerator Mentor Match Connector

Claude Desktop

Program Director

Operations Manager

Venture Partner

What kind of data does this MCP need to run?

Is this capability just for large accelerators?

Does it tell me which mentors are the best fit?

What does 'match coverage' mean?

How is match coverage calculated?

Can I check if a mentor is over-leveraged?

What factors influence the match quality score?

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