Delivery Integrity Prover MCP for AI. Stop trusting 'task complete' messages.

Q: What is the best way to integrate verifydelivery into an existing agent pipeline?

Call verifydelivery as the absolute last step of your agent's execution sequence, right before any deployment trigger. This ensures that no matter how many steps happen beforehand, the task cannot be claimed complete without passing this structured audit gate.

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

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Works with every AI agent you already use

…and any MCP-compatible client

Connect to your AI in seconds.

Delivery Integrity Prover forces your AI agent to prove its work at every step. It stops agents from declaring a task 'done' until you see concrete proof: a checklist mapping every requirement, specific file changes with line numbers, verifiable build or test logs, and an explicit list of any remaining gaps.

What your AI can do

Verify delivery

This tool forces the agent to provide a structured report detailing how it met all requirements, listing specific file changes and providing necessary build logs before giving a final verdict.

Map requirements to changes

It forces the agent to create a checklist showing exactly which lines of code address each specific item in your prompt.

Validate file modifications

The MCP ensures that every claimed change lists exact file paths and line number ranges, preventing vague claims like 'updated the whole module'.

Supply execution evidence

It requires compilation output, test results, or build logs to prove code actually ran successfully.

Identify remaining gaps

The system forces the agent to list any outstanding assumptions, manual checks required, or features that were out of scope.

Ask an AI about this

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AI Agent

Delivery Integrity Prover: 1 Tool Available

This single tool lets you run a rigorous quality gate process, ensuring AI-generated code is fully verified against its original objectives.

Make your AI actually useful.

Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

Start using Delivery Integrity Prover on Vinkius

Verify Delivery

This tool forces the agent to provide a structured report detailing how it met all requirements, listing specific file changes and...

Security and governance baked right in.

Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.

Claude AI

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

Start a conversation

Open a new chat. The Delivery Integrity Prover integration is available immediately — no restart needed.

Antigravity

Configure Agent Environment

Open your Antigravity agent's workspace configuration or mcp-servers.json file.

Bind the Endpoint

Add the Vinkius endpoint URL to your agent's MCP connections list:

"mcp_servers": {
  "delivery-integrity-prover": {
    "serverUrl": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
  }
}

Provide your secure token in place of [YOUR_TOKEN_HERE] to ensure your agent requests are authenticated.

Execute

Start your Antigravity session. The agent will autonomously discover and utilize the Delivery Integrity Prover tools with full Vinkius guardrails applied.

VS Code Copilot

⚡

One-Click Install (Recommended)

In your Vinkius Dashboard, simply click the Add to VS Code button for this server. We'll automatically configure your local workspace.

Or configure manually

Open MCP Settings

Open VS Code, press Ctrl/Cmd + Shift + P, and search for GitHub Copilot: MCP Servers.

Add Server Config

Add the Vinkius endpoint configuration to your mcp-servers.json file:

"delivery-integrity-prover": {
  "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}

Ensure you replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.

LangChain

Install Dependencies

Install the LangChain MCP adapters for your environment:

pip install langchain-mcp-adapters

Connect the Server

Use the SSEClient in LangChain to connect to the Vinkius managed endpoint:

from langchain_mcp_adapters.client import SSEClient

# Connect to Vinkius
client = SSEClient(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
tools = client.get_tools()

CrewAI

Define the Tool

Load the Vinkius MCP tools into your CrewAI agents:

from crewai import Agent
from mcp_crewai import MCPTool

# Connect securely to Vinkius
vinkius_tools = MCPTool(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

# Assign to Agent
researcher = Agent(
    role='Data Researcher',
    tools=vinkius_tools.get_all()
)

Execute Task

Run your CrewAI process. The agent will autonomously route tasks to the Vinkius managed server.

Choose How to Get Started

Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.

Build Your Own

Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

Import from OpenAPI, Swagger, or YAML specs
Create Agent Skills with progressive disclosure
Deploy to edge with MCPFusion framework
Built in DLP, auth, and compliance on every call
Real time usage dashboard and cost metering
Publish to catalog or keep private

Start building

Make Your AI Do More

Start with Delivery Integrity Prover, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.

Use this MCP plus 5,100+ others, all in one place
Add new capabilities to your AI anytime you want
Every connection is secured and compliant automatically
Track usage and costs across all your servers
Works with Claude, ChatGPT, Cursor, and more
New servers added to the catalog every week

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Delivery Integrity Prover. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This connection provides 1 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

The Audit Nightmare: Tracking AI Changes Today

Right now, when an agent completes a complex feature, you're left sifting through chat histories and vague summary blocks. You have to manually cross-reference the original prompt against the file diffs, then remember to ask for build logs—a process that involves jumping between three different tabs just to verify one function.

With this MCP, your workflow changes entirely. Instead of manual auditing, you run a single validation check. The system aggregates everything: requirements mapped, files specified with line ranges, and the necessary execution evidence. You get immediate confirmation on whether the delivery meets technical standards.

Using verify_delivery for Proof

You stop having to ask: 'Did you run the tests?' or 'Which files did you change?' The tool handles this. It demands explicit proof of execution, forcing the agent to provide compilation output and test results as part of its completion package.

The difference is moving from trusting a verbal claim to reviewing an objective, structured report. You get verifiable closure.

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What your AI can actually do with this

AI models can be fast, but they often rush to the finish line. They might declare success even if half the required code is missing or if they just left 'TODO' comments in the files. This MCP acts as a mandatory quality gate for your development work. Instead of accepting an agent’s summary message, it forces the system to map every single user requirement back to actual file changes; it demands execution logs—like successful build outputs or passing test suites.

If anything is unverified, incomplete, or assumed, this connector flags it immediately. By connecting Delivery Integrity Prover through your Vinkius catalog, you ensure that your AI client doesn't just say the task is done; it proves it with evidence.

Built · Hosted · Managed by Vinkius Delivery Integrity Prover - Verify AI Code Changes

Server ID 019e5994-610c-7263-a340-5affa6b2a94c

Vinkius Inspector

Compliance Grade A+

Score 100/100

Report View Report ↗

What Changes When You Connect

You stop accepting vague claims. The verify_delivery tool demands a clear, auditable checklist that maps every single prompt requirement to an actual code change.

It prevents incomplete commits. By requiring specific file paths and line ranges for modifications, you eliminate ambiguity about what actually changed in the codebase.

You gain proof of execution. The MCP requires compilation output or test logs; 'it should work' is rejected if there are no verifiable outputs.

You catch blind spots. It forces the agent to explicitly list any remaining assumptions or gaps that need manual review, making the delivery risk visible.

It enforces quality at scale. Integrating this into your workflow makes it impossible for an AI agent to skip critical steps and still claim success.

See it in action

01 01

Refactoring a core service endpoint

A developer asks their agent to refactor authentication logic. The agent claims success, but the verify_delivery tool immediately flags that while the main file was updated, the necessary corresponding unit tests were not run or logged.

02 02

Building a multi-step data pipeline

The goal is to process user data through three services. The agent completes steps 1 and 2 but forgets step 3 (logging). Running the verification tool forces the agent to document the missing third service, preventing a broken release.

03 03

Migrating an old API structure

The prompt requires updating five files and changing three endpoints. The agent updates only four files and leaves one endpoint untouched. Running verify_delivery generates the 'INCOMPLETE REQUIREMENTS' verdict, pointing directly to the missing file.

04 04

Implementing security patches

The patch requires updating middleware and running full regression tests. The agent only changes the code and provides a placeholder log ('Looks good'). Running verify_delivery rejects this immediately, demanding real build logs or the process fails.

The honest tradeoffs

Assuming completion

Anti-pattern

The agent sends 'Task finished. Everything works!' and provides no supporting evidence.

The Fix

Instead, run verify_delivery. You must provide the objective, map every requirement, list modified files/lines, supply logs, and explicitly state any gaps.

Vague file updates

Anti-pattern

The agent writes 'Updated the user model in src/user.ts' without specifying line numbers.

The Fix

Use verify_delivery to enforce precision. The output must specify: 'src/user.ts:L47-52 — fixed token refresh logic.' Lines matter.

Ignoring edge cases

Anti-pattern

The agent completes the core functionality but fails to mention that dynamic routes were not tested.

The Fix

Do not assume perfection. verify_delivery forces a review of outstanding tasks and assumptions, flagging any potential gaps.

When It Fits, When It Doesn't

Use this MCP if your task involves critical path development, architectural changes, or anything that impacts production stability. You need to know how the code was changed (lines/files), if it compiled (logs), and what is still missing (gaps). Don't use it for simple text generation, documentation drafting, or low-stakes content creation—those tasks don't require this level of rigorous proof. If your goal is just to summarize information, you need a different type of connector; if the task involves code, verify_delivery is mandatory.

Questions you might have

Why are placeholder logs like 'tests passed' rejected? +

AI agents frequently assume that code works without executing it. Requiring actual command output logs forces them to run verification scripts, catching syntax errors and test failures early.

What counts as a remaining gap? +

A remaining gap includes any manual check required by the user, edge cases that were explicitly left out of scope, or dependencies on other teams. Banning 'none' forces agents to acknowledge limitations.

How does this prevent agents from lying about completion? +

It converts simple guidelines into strict tool-call checks. The agent must successfully match requirements to modified code lines and paste actual command outputs to get an approval verdict.

How does using verify_delivery ensure that my AI agents pass security audits? +

It enforces strict, auditable proof of work, which is essential for secure codebases. By requiring explicit mapping of every requirement and listing file changes with specific line ranges, it prevents agents from making unrecorded or undocumented modifications before deployment.

If an AI agent hits a compile error, how should I use verify_delivery? +

You must provide the actual compiler output as part of your verification logs. The tool demands empirical evidence; generic failure statements are rejected and require the agent to explicitly document and prove the fix before marking the task complete.

When a user requirement is subjective, like 'improve UX,' how can I use verify_delivery? +

You must capture the implementation of those requirements in the ‘Remaining Gaps’ section. While code changes are mandatory for technical items, non-code decisions or required manual review steps belong explicitly listed as outstanding tasks to be audited later.

Does running verify_delivery multiple times impact my API rate limits or performance? +

It consumes standard agent execution resources, but the quality assurance it provides outweighs that cost. Since it forces a full verification cycle (logs + files), treat it as your mandatory final CI/CD gate step within the workflow.

What is the best way to integrate verify_delivery into an existing agent pipeline? +

Call verify_delivery as the absolute last step of your agent's execution sequence, right before any deployment trigger. This ensures that no matter how many steps happen beforehand, the task cannot be claimed complete without passing this structured audit gate.

Connect to your AI in seconds.

Verify delivery

Delivery Integrity Prover: 1 Tool Available

Make your AI actually useful.

Verify Delivery

Security and governance baked right in.

Claude AI

Open Claude Settings

Add Custom Connector

Start a conversation

Claude Code

Open your terminal

Add the MCP Server

Start coding

Cursor

One-Click Install (Recommended)

Open Cursor Settings

Add New Server

Use in Composer

Antigravity

Configure Agent Environment

Bind the Endpoint

Execute

VS Code Copilot

One-Click Install (Recommended)

Open MCP Settings

Add Server Config

Windsurf

One-Click Install (Recommended)

Open Windsurf Settings

Add Server Endpoint

LangChain

Install Dependencies

Connect the Server

CrewAI

Define the Tool

Execute Task

Choose How to Get Started

Build Your Own

Make Your AI Do More

Works with Claude, ChatGPT, Cursor, and more

The Audit Nightmare: Tracking AI Changes Today

Using verify_delivery for Proof

What your AI can actually do with this

Here's how it actually works

Who is this actually for?

What Changes When You Connect

See it in action

Refactoring a core service endpoint

Building a multi-step data pipeline

Migrating an old API structure

Implementing security patches

The honest tradeoffs

Assuming completion

Vague file updates

Ignoring edge cases

When It Fits, When It Doesn't

Questions you might have