Accelerator Mentorship Analytics Connector for AI agents.
3 live capabilities
Quantify mentor impact and optimize startup engagement intensity
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Why people use Accelerator Mentorship Analytics
Stop guessing mentor impact with Accelerator Mentorship Analytics
This MCP changes the math. Instead of staring at spreadsheets, you ask your agent to run a correlation analysis. It pulls the data, adjusts for how good the startups were to begin with, and tells you exactly how much your mentorship is actually moving the needle. You get clear, actionable answers about where to put your time and money.
What Vinkius changes
You turn qualitative mentorship efforts into quantitative performance data.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,800+ Connectors
- Real-world use case 01
Fixing inefficient mentor allocation
A program manager notices some mentors are overworked while others are idle.
- Real-world use case 02
Proving program value to LPs
An accelerator director needs to show investors that their mentorship actually drives startup growth.
- Real-world use case 03
Identifying high-impact mentorship patterns
A director wants to know if more hours always equals more success.
Complete set · 3capabilities
The complete Accelerator Mentorship Analytics capability set.
These are the exact actions your AI can choose when you ask it to work with Accelerator Mentorship Analytics.
01—03
3 capabilities in this set.
Part of 3 available through Accelerator Mentorship Analytics.
- 01 Capability
Calculate mentor roi
Calculates the value generated by mentorship relative to the time invested. It helps you see which mentors provide the most bang for your buck.
- 02 Capability
Find optimal intensity
Identifies the most efficient amount of mentorship time needed for success. It prevents you from over-investing in companies that don't need it.
- 03 Capability
Get correlation analysis
Analyzes the relationship between mentor effort and startup outcomes. It uses quality scores to ensure the data isn't skewed by high-performing companies.
Set up in minutes
One URL. Then ask Accelerator Mentorship Analytics to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Accelerator Mentorship Analytics 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_XwBv1fcwuTJ5H08XdC2irZCpmZ2DmoH3GVtR08vm/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 Accelerator Mentorship Analytics, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Accelerator Mentorship Analytics for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_XwBv1fcwuTJ5H08XdC2irZCpmZ2DmoH3GVtR08vm/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 Accelerator Mentorship Analytics URL.
- Step 03
Save and start
Save the connection and enable Accelerator Mentorship Analytics in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"accelerator-mentorship-analytics": {
"url": "https://edge.vinkius.com/vk_preview_XwBv1fcwuTJ5H08XdC2irZCpmZ2DmoH3GVtR08vm/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 Accelerator Mentorship Analytics
Open Agent mode in chat and ask: "Using Accelerator Mentorship Analytics, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"accelerator-mentorship-analytics": {
"url": "https://edge.vinkius.com/vk_preview_XwBv1fcwuTJ5H08XdC2irZCpmZ2DmoH3GVtR08vm/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 Accelerator Mentorship Analytics
Ask Copilot: "Using Accelerator Mentorship Analytics, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"accelerator-mentorship-analytics": {
"url": "https://edge.vinkius.com/vk_preview_XwBv1fcwuTJ5H08XdC2irZCpmZ2DmoH3GVtR08vm/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 Accelerator Mentorship Analytics
Open Cascade and ask: "Using Accelerator Mentorship Analytics, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"accelerator-mentorship-analytics": {
"url": "https://edge.vinkius.com/vk_preview_XwBv1fcwuTJ5H08XdC2irZCpmZ2DmoH3GVtR08vm/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 Accelerator Mentorship Analytics
Ask Cline: "Using Accelerator Mentorship Analytics, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add accelerator-mentorship-analytics --transport http "https://edge.vinkius.com/vk_preview_XwBv1fcwuTJ5H08XdC2irZCpmZ2DmoH3GVtR08vm/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 Accelerator Mentorship Analytics
Ask Claude: "Using Accelerator Mentorship Analytics, show me...". 3 tools are ready
Where the request belongs
Work Accelerator Mentorship Analytics can move forward.
This is built for accelerator directors and program managers who need to prove their program's value to stakeholders and optimize how they deploy their mentor network.
Accelerator Director
Uses the data to report program ROI and justify budget allocations to investors.
Program Manager
Identifies which mentors are most effective and which startups need more or less attention.
Venture Capitalist
Evaluates the quality of an accelerator's mentorship model before committing capital.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse Connectors
Accelerator Mentor Match
Evaluates mentor-startup matching effectiveness using expertise, capacity, and stage alignment.
Accelerator Alumni Mentor Conversion
Analyze alumni-to-mentor conversion rates and optimize mentor recruitment incentives.
Accelerator Cohort Optimizer
Optimize accelerator cohort selection for diversity, sector balance, and synergy.
Accelerator Program ROI Analyzer
Quantify the true impact of accelerator participation by analyzing financial ROI, equity dynamics, and total net value.
Accelerator Founder Coaching Hours
Calculates optimal coaching hour distribution and topic prioritization for accelerator founders.
Accelerator & Incubator Economics Model
Calculate incubator service costs, equity value exchange, and program sustainability.
Bring your own AI
Change the model, client or framework. Keep Accelerator Mentorship Analytics connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Accelerator Mentorship Analytics.
The practical details behind the request, access and result.
How can Accelerator Mentorship Analytics help me prove my program's value?
You can use it to generate hard data showing the correlation between mentor hours and startup success. This allows you to present clear ROI metrics to your investors and stakeholders.
Can this MCP help me prevent mentor burnout?
Yes. By finding the optimal intensity for mentorship, you can identify when you are over-investing time in certain companies and redistribute those hours more effectively.
How does this capability handle the fact that some startups are just better than others?
The capability uses company quality scores to adjust the correlation analysis. This ensures that your data reflects the actual impact of the mentor, not just the inherent quality of the startup.
Is it easy to connect my existing accelerator data to this MCP?
Yes, you can connect your data through the Vinkius platform, which allows your AI client to immediately start running analyses on your cohort metrics.
Can I use this to decide which mentors to invite back next year?
Absolutely. You can use the ROI calculations to see which mentors consistently drive the most progress, helping you build a more effective mentor network.
How does this capability handle self-selection bias?
The get_correlation_analysis capability uses company quality scores to adjust the correlation coefficient, ensuring that the impact of mentorship is not overstated due to high-quality startups naturally succeeding.
What is the purpose of finding optimal intensity?
The find_optimal_intensity capability identifies the point where adding more mentor hours yields diminishing returns, helping accelerators allocate resources efficiently.
Can I calculate the efficiency of my mentors?
Yes, you can use calculate_mentor_roi to determine the value generated per unit of mentor time and quality.
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