Spec Prover Connector for AI agents.
1 live capability
Ensure your product specifications are mathematically sound before development starts.
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Why people use Spec Prover
Spec Prover for Requirements Engineering
Spec Prover changes that. Your agent has to prove the math works first. It shows the intermediate steps and checks the boundaries. You get a verified spec that actually works, moving the testing phase to the very beginning of the pipeline.
What Vinkius changes
That your specs get mathematically audited before they ever hit a developer's backlog.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
The Midnight Wrap-around Bug
A PM writes a sleep timer spec.
- Real-world use case 02
The Floating-Point Discount
A retail agent creates a discount formula.
- Real-world use case 03
The Orphan Constant Cleanup
An agent declares 5 constants but only uses 4.
Complete set · 1capability
The complete Spec Prover capability set.
These are the exact actions your AI can choose when you ask it to work with Spec Prover.
01
1 capability in this set.
Part of 1 available through Spec Prover.
- 01 Capability
Prove spec function
Forces the AI to execute a step-by-step mathematical proof for a function, checking for errors in constants and edge cases.
Set up in minutes
One URL. Then ask Spec Prover to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Spec Prover 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_NbIciblMWXQuIBLxYENirh67y4h51MySXcQihZn2/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 Spec Prover, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Spec Prover for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_NbIciblMWXQuIBLxYENirh67y4h51MySXcQihZn2/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 Spec Prover URL.
- Step 03
Save and start
Save the connection and enable Spec Prover in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"spec-prover": {
"url": "https://edge.vinkius.com/vk_preview_NbIciblMWXQuIBLxYENirh67y4h51MySXcQihZn2/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 Spec Prover
Open Agent mode in chat and ask: "Using Spec Prover, help me...". 1 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"spec-prover": {
"url": "https://edge.vinkius.com/vk_preview_NbIciblMWXQuIBLxYENirh67y4h51MySXcQihZn2/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 Spec Prover
Ask Copilot: "Using Spec Prover, help me...". 1 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"spec-prover": {
"url": "https://edge.vinkius.com/vk_preview_NbIciblMWXQuIBLxYENirh67y4h51MySXcQihZn2/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 Spec Prover
Open Cascade and ask: "Using Spec Prover, help me...". 1 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"spec-prover": {
"url": "https://edge.vinkius.com/vk_preview_NbIciblMWXQuIBLxYENirh67y4h51MySXcQihZn2/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 Spec Prover
Ask Cline: "Using Spec Prover, help me...". 1 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add spec-prover --transport http "https://edge.vinkius.com/vk_preview_NbIciblMWXQuIBLxYENirh67y4h51MySXcQihZn2/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 Spec Prover
Ask Claude: "Using Spec Prover, show me...". 1 tools are ready
Where the request belongs
Work Spec Prover can move forward.
This is for anyone managing complex logic in a multi-agent system. It's for the PM who's tired of 'impossible' bugs and the dev who's sick of vague requirements.
Product Manager
Uses this to ensure that every discount, calculation, and rule in a new feature is mathematically sound before handoff.
Technical Writer
Validates complex logic in documentation to ensure that automated agents can interpret the requirements correctly.
QA Engineer
Uses this to catch 'impossible' bugs at the spec level, preventing rework and pipeline deadlocks between teams.
AI Orchestrator
Integrates this into a pipeline to ensure that an agent writing a spec doesn't pass broken math to a coding agent.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
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Delivery Integrity Prover
Forces AI agents to reflect on task execution, matching prompt requirements to actual changes, verifying logs, and declaring gaps before claiming completion.
Inversion Thinking Prover
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Systems Thinking Prover
AI thinks in straight lines. This engine is a 6-pivot cognitive trap that forces the LLM to map feedback loops, second-order effects, and bottlenecks before proposing any architectural change.
Testim
Trigger automated AI tests, inspect execution logs, and manage branches natively via your AI agent.
Reversibility Architect Prover
LLMs suggest irreversible architectural changes. This engine is a 6-pivot cognitive trap that forces the agent to map data rollbacks, blast radius, and canary deployments before executing.
Bring your own AI
Change the model, client or framework. Keep Spec Prover 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 Spec Prover.
The practical details behind the request, access and result.
What is Spec Prover for?
Spec Prover is a capability that ensures your product specifications are mathematically and logically sound. It forces your AI agent to prove that your formulas work before they are passed to a developer.
How does Spec Prover help my dev team?
It prevents 'logic bugs' from reaching the code. By catching errors in the spec first, your developers don't have to waste time fixing math errors that should have been caught during the requirements phase.
Can Spec Prover catch math errors in my AI specs?
Yes, it specifically looks for errors like off-by-one boundaries, floating-point precision issues, and negative number results in your AI-generated requirements.
Does Spec Prover handle edge cases like zero or negative numbers?
Yes, the capability requires your AI agent to explicitly test and handle edge cases like zero, negative values, and maximum limits to ensure the spec is complete.
How does Spec Prover prevent pipeline deadlocks?
It stops the back-and-forth cycle where QA and Dev disagree on how a formula should behave. Because the spec is proven upfront, everyone has a single source of truth.
Can I use Spec Prover for complex financial formulas?
It is ideal for complex financial logic, as it validates the intermediate steps of a calculation to ensure every constant is used and every boundary is handled.
Does Spec Prover compute or verify the arithmetic itself?
No. Spec Prover performs zero computation. It forces the AI agent to structure its own reasoning into traceable steps, then validates that the reasoning is logically consistent. If the agent says the output matches the trace but also says the spec is wrong, the capability rejects the contradiction. The agent does all the math. the capability enforces honesty.
What happens when the capability rejects my proof?
The capability returns a detailed consistency error explaining exactly which Decision Pivot contradicts your verdict. For example, if you mark outputMatchesTrace: true but choose SPEC_WRONG, the rejection will explain that if the output matches your trace, the formula cannot be wrong. re-examine your trace arithmetic. Fix the contradiction and call the capability again with isRevision: true.
What kind of edge cases should I trace?
The capability requires edge case inputs that differ from your normal inputs. Focus on boundaries: zero values (0 cycles), negative results (subtraction below zero), maximum values (24 hours, 1440 minutes), wrap-around conditions (midnight crossover), and empty/null inputs. The capability rejects edge cases that are identical to normal inputs. a second normal case is not an edge case.
One connection away
Give your agent a direct line to Spec Prover.
Connect Spec Prover once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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