# Spec Prover MCP for AI Agents AI Agent Connect

> Spec Prover stops broken logic from reaching your codebase. It forces AI agents to prove every specification with real inputs, catching the math errors and edge cases that abstract reviews miss. It's for teams that need to ensure formulas and requirements are bulletproof before a single line of code is written.

## Overview
- **Category:** productivity
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_NbIciblMWXQuIBLxYENirh67y4h51MySXcQihZn2/ai-agent-connect
- **Tags:** specification-verification, structured-reasoning, trace-validation, edge-case-detection, multi-agent, requirements-engineering, formula-proving, agentic-pipeline

## Description

This specification verification MCP stops broken logic from reaching your codebase. When an AI agent writes a product specification, it often guesses at the math. It might write a formula for a discount or a bedtime calculation without realizing what happens at midnight or when a number goes negative. This creates a mess for developers who follow the spec perfectly but end up shipping a broken product. This Connector fixes this by acting as a mandatory checkpoint. It forces your AI client to walk through every step of a calculation with actual numbers. If the math doesn't add up or if an edge case like a zero value isn't handled, the tool flags it immediately. This stops errors at the source instead of letting them cascade through your QA and production cycles. By using this Connector through the Vinkius catalog, you ensure that every requirement is logically sound. It moves the testing phase to the very beginning of the pipeline, making it much harder for your agents to ship fuzzy or incomplete logic.

## Tools

### 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.

## Prompt Examples

**Prompt:** 
```
I have a formula for calculating a loyalty discount based on points. Can you prove it works?
```

**Response:** 
```
Verdict: SPEC_PROVEN ✅

**Trace:**
- Points: 500
- Base Discount: 5%
- Multiplier: (Points / 1000) = 0.5
- Final: 5% * 0.5 = 2.5%

**Edge Case:**
- Points: 0
- Result: 0% (Handled)

**Constants:**
- BASE_RATE: 0.05 (Used)
- MIN_POINTS: 100 (Used)

Everything looks correct for your loyalty logic.
```

**Prompt:** 
```
Check if my new bedtime calculation handles midnight wrap-around correctly.
```

**Response:** 
```
Verdict: SPEC_INCOMPLETE

**Trace:**
- Wake Time: 08:00
- Sleep: 9 hours
- Calculation: 480 - 540 = -60

**Issue:**
The formula results in -60 minutes. Your spec doesn't define how to handle negative values.

**Fix:**
Add a modular arithmetic check: `((-60 + 1440) % 1440) = 1380`. This will correctly wrap to 23:00.
```

**Prompt:** 
```
Are there any constants in my trajectory spec that aren't actually being used?
```

**Response:** 
```
Verdict: ORPHAN_CONSTANTS

The constant `DRAG_COEFFICIENT=0.47` is declared in your requirements but doesn't appear in any of your calculation steps.

**Action:**
Either include it in the trajectory formula or remove it from the constants list to keep the spec clean for the developers.
```

## Capabilities

### Trace formulas with real numbers
Your agent walks through every math step using concrete inputs to see if the logic holds up.

### Verify final output values
The tool checks if the final result of a calculation actually matches the expected outcome.

### Identify orphaned constants
It flags any declared variables that aren't actually used in your formulas to keep specs clean.

### Stress test edge cases
The agent is forced to test zero, negative, and maximum values to find hidden bugs.

### Validate boundary conditions
It ensures your spec explicitly defines what happens at the limits of your data.

## Use Cases

### The Midnight Wrap-around Bug
A PM writes a sleep timer spec. Spec Prover catches the negative time value at midnight and forces a fix for modular arithmetic.

### The Floating-Point Discount
A retail agent creates a discount formula. The tool finds a precision error where $0.10 + $0.20 doesn't equal $0.30.

### The Orphan Constant Cleanup
An agent declares 5 constants but only uses 4. The tool identifies the extra one so the developer isn't confused by dead code.

### The Empty List NaN Error
A dev gets a NaN error because the spec didn't say what to do with zero ratings. Spec Prover forces a null-handling rule.

## Benefits

- Stop logic errors early by using prove_spec_function to catch math bugs before they ever reach the dev team.
- Eliminate orphan constants so your specs stay clean and your developers don't have to guess which values matter.
- Prevent production crashes by forcing agents to handle wrap-around and negative number edge cases.
- Avoid pipeline deadlocks where QA and Dev disagree on how a formula should behave in specific scenarios.
- Build trust in your AI-generated requirements by requiring a SPEC_PROVEN verdict for every new feature.

## How It Works

The bottom line is that your specs get mathematically audited before they ever hit a developer's backlog.

1. Provide the AI agent with a formula or engine function that needs verification.
2. The agent uses the Connector to walk through a representative input and a specific edge case.
3. You get a SPEC_PROVEN status or a clear breakdown of where the logic failed.

## Frequently Asked Questions

**What is Spec Prover for?**
Spec Prover is a tool 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 tool 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 tool rejects the contradiction. The agent does all the math — the tool enforces honesty.

**What happens when the tool rejects my proof?**
The tool 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 tool again with `isRevision: true`.

**What kind of edge cases should I trace?**
The tool 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 tool rejects edge cases that are identical to normal inputs — a second normal case is not an edge case.