Delivery Integrity Prover MCP. Stop agents from claiming tasks are finished prematurely.
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Delivery Integrity Prover forces your AI agent to prove it finished a task. It acts as a quality gate, making agents map every prompt requirement to actual code changes, verifying build logs, and explicitly listing any gaps before it will declare 'done.'
What your AI agents can do
Verify delivery
Structured validation tool that forces the agent to map requirements to changes, verify logs, and expose gaps before declaring task completion.
The tool forces the agent to check if all initial requirements were met, document the changes, and provide proof of execution.
It ensures every single task requirement listed in the prompt is traceable to a specific file or code change.
The server requires the agent to supply actual build, compile, or test logs, preventing the use of placeholders.
It forces the agent to list any remaining tasks, missing migrations, or items that need human review.
It provides a final verdict on whether the overall implementation is clean, verifiable, and truly done.
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Supported MCP Clients
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019e5994verify delivery
Structured validation tool that forces the agent to map requirements to changes, verify logs, and expose gaps before declaring task completion.
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What you can do with this MCP connector
Delivery Integrity Prover makes your AI agent prove it finished a task. It's a quality gate. When your agent uses the verify_delivery tool, it has to map every prompt requirement to actual code changes, verify build logs, and list any gaps before it can declare 'done.'
When you run verify_delivery, your agent can't just claim the job is finished. It has to show you that all initial requirements were met, document every change, and provide proof of execution. The tool forces the agent to map every single task requirement listed in the prompt to a specific file or code change.
It demands that your agent supply actual build, compile, or test logs, so you don't get placeholders. You'll also make it list any remaining tasks, missing migrations, or items that need human review. This process audits the implementation's completeness, giving you a final verdict on whether the overall work is clean, verifiable, and truly done.
It's designed to add cognitive friction, breaking the agent's tendency to close tasks too early. By demanding command execution outputs and logs, you stop the agent from just guessing that the code compiles.
How Delivery Integrity Prover MCP Works
- 1 Call the
verify_deliverytool at the end of the agent's work. You must state the task objective and checklist all prompt requirements against the files the agent modified. - 2 The tool forces the agent to provide empirical validation logs (test outputs, build logs) and explicitly expose any remaining gaps or limitations.
- 3 The agent commits to the required boolean pivots (e.g.,
requirementsMapped=true) and provides a final, defensible verdict. If the tool rejects the submission, the agent must fix the highlighted issue before declaring the task finished.
The bottom line is that it prevents agents from making false claims of completion by forcing a structured, evidence-based review of the entire development cycle.
Who Is Delivery Integrity Prover MCP For?
The technical lead who has to review every pull request before merging. The architect who needs to enforce process compliance across a team of AI agents. Anyone whose job depends on the accuracy of AI-generated code, not just the speed. You need proof, not promises.
Enforces adherence to development standards by making sure AI agents don't skip critical steps like running full test suites or documenting technical debt.
Uses the tool to audit the output of junior agents, confirming that every piece of code they wrote actually addressed a specific requirement from the ticket.
Integrates the tool into CI/CD pipelines to gate deployments, ensuring that any code promoted has validated logs and no unaddressed gaps.
What Changes When You Connect
- Guaranteed Requirement Traceability: It forces the agent to checklist every prompt requirement against the files it changed. You stop guessing if the agent missed a step; the tool demands proof that every line of code maps back to an initial objective.
- Proof of Execution: Instead of accepting 'everything worked fine,' the tool requires actual build, compile, and test logs. This stops agents from assuming success and forces them to provide empirical evidence.
- Mandatory Gap Identification: The system forces the agent to explicitly name any outstanding work, out-of-scope items, or manual steps needed. You get a single, auditable list of what's not done.
- Stops Placeholder Code: It validates that all modified files are complete. The agent can't leave TODO comments or half-written helper functions in the code base and still claim delivery.
- Structured Audit Trail: By forcing commitment to five distinct pivots (e.g.,
artifactsModified,gapsIdentified), you get a machine-readable, repeatable audit trail for every task outcome.
Real-World Use Cases
Reviewing a complex feature implementation
A developer asks their agent to build a user profile migration. The agent finishes the code but forgets to mention that the database schema also needs manual updating. Running verify_delivery forces the agent to list the missing schema migration step under 'gaps identified,' preventing a production bug.
Validating a core service refactor
The agent refactors a critical microservice. It submits the code and says 'done.' The verify_delivery tool rejects the claim because the agent failed to provide the actual npm run test output, forcing the agent to run the full test suite and prove the refactor didn't break anything.
Debugging a failed PR submission
A team member submits a pull request claiming completion, but the logs are generic ('Looks good'). Running verify_delivery immediately flags 'UNVERIFIED_CHANGES,' forcing the agent to execute the actual compile command and supply the real console output.
Multi-agent task coordination
When three different agents contribute code to a single module, verify_delivery is run last. It ensures that all three agents' work is mapped against the original prompt and that no agent left behind any unverified dependencies or remaining tasks.
The Tradeoffs
Relying on 'It looks fine'
The agent finishes the task and simply says, 'I fixed the bug, everything looks fine.' This is a placeholder claim that ignores the required steps.
→
Run verify_delivery. The tool forces the agent to provide specific, empirical evidence. You must supply the full test output or build logs, not a general statement.
Skipping gap analysis
The agent addresses 9 out of 10 requirements and just submits the code. It fails to mention the remaining manual steps or edge cases.
→
Use verify_delivery. It forces the agent to check for 'gaps identified.' This ensures the agent doesn't just ignore tasks that are out of scope or need human review.
Assuming code completeness
The agent leaves a // TODO: implement caching comment in the code and submits the task as finished.
→
Run verify_delivery. The tool checks if all target files are updated with zero placeholders. It will flag the incomplete functions, forcing the agent to finish the work.
When It Fits, When It Doesn't
Use this server if your development workflow relies on automated agents or LLMs doing complex, multi-step coding tasks. You need an immutable, structured proof of work before merging anything.
Don't use this if your task is simple (e.g., just writing a single function or updating a config file). For simple changes, manual review is faster. You only need this quality gate when the complexity of the task increases the risk of the agent skipping steps or lying about its progress. If you just need a summary, don't use this; you need the full, structured validation provided by verify_delivery.
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 server provides 1 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
The biggest headache in AI-assisted coding isn't writing the code—it's knowing if the code is actually finished.
Today, when an AI agent 'completes' a feature, you get a message: 'Task Complete.' You then have to manually check the PR description, cross-reference the original ticket requirements, and then ask a teammate to run the full test suite just to confirm the agent didn't skip the database migration or leave a TODO comment. It's a tedious, multi-step audit process that kills momentum.
With Delivery Integrity Prover, that process collapses. You run the tool. It instantly forces the agent to map every requirement to a specific file change, provide the full test logs, and declare any missing steps. You get a clean, actionable verdict, not just a status message.
Delivery Integrity Prover MCP Server: Prove your work, don't just claim it.
The tool eliminates the need to manually check the five critical pivots: Did it address every requirement? Are there placeholders? Did they run the tests? Are there gaps? It handles all of it in one structured call.
It changes the development loop from 'Here's the code, check it' to 'Here's the proof, check the proof.' It’s the definitive quality gate for AI-driven development.
Common Questions About Delivery Integrity Prover MCP
How does the Delivery Integrity Prover work? +
It forces the agent to submit a structured report that maps every prompt requirement to specific code changes. This report must also include proof of execution, like passing test logs, before the agent can declare the task complete.
Can I use verify_delivery for small changes? +
While you can, it's overkill for simple fixes. Use it when the task involves multiple files, complex logic, or potential for omitted steps. It's built for large, multi-stage development cycles.
Does verify_delivery check for security flaws? +
No, the tool checks structural completeness and adherence to requirements. It validates the process (did they test it?) but doesn't scan for vulnerabilities.
What happens if verify_delivery fails? +
The tool rejects the submission and provides a detailed error message, pointing out exactly which of the five pivots failed (e.g., 'requirementsMapped' failed). You must fix that issue first.
How does calling `verify_delivery` help prevent incomplete code? +
It forces your AI agent to perform a structured self-reflection. Before declaring success, the tool mandates mapping all prompt requirements to specific file changes and explicitly listing any remaining gaps. This stops agents from making false completion claims.
Does `verify_delivery` require running actual test logs? +
Yes, it demands empirical evidence. The tool requires you to provide actual build, compile, or test output logs. Using generic statements like 'everything worked' won't pass validation.
What types of tasks are best suited for `verify_delivery`? +
This tool works best on complex feature implementation. Use it when the task involves multiple files, significant logic changes, or requires adherence to specific, detailed instructions. It's a quality gate for large changes.
Is `verify_delivery` compatible with different programming languages? +
Yes, it validates the process of development, not the language itself. As long as your agent can generate structured outputs, provide file paths, and run logs, the tool works.
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.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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