# Delivery Integrity Prover MCP for AI Agents AI Agent Connect > Delivery Integrity Prover stops AI agents from lying about finishing a task. It forces your agent to map every prompt requirement to specific code changes, provide actual logs, and admit what's still missing before it says "done." This acts as a quality gate to catch placeholders and unverified code. ## Overview - **Category:** productivity - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_47XZJ7Jwbw4wbyoAk0N57vFtYyOnkyNmEkgNiNe8/ai-agent-connect - **Tags:** delivery-integrity, task-completion, verification, self-reflection, ai-coaching, quality-gate, testing ## Description You’ve seen it happen. You ask your agent to build a feature, it spits out a bunch of code, and then immediately tells you it’s finished. But when you dig in, there are still TODOs, the tests didn't actually run, and it missed two out of five requirements. Delivery Integrity Prover fixes this by acting as a mandatory checkpoint at the end of every task. It forces the agent to prove its work. Instead of just saying "it's done," the agent has to go through a mental checklist. It has to map every single line of your prompt to a specific file change, show you the exact line numbers, and provide the actual output from your test runners or compilers. If the logs aren't there, the task isn't done. If there's a gap in the requirements, it has to admit it. It creates a moment of friction that stops the agent from rushing. It turns "trust me" into "here is the evidence." You can find this and thousands of other tools in the Vinkius catalog to build a more reliable dev environment. It ensures that when your agent says a task is complete, it actually means it. ## Tools ### verify_delivery Forces the agent to map requirements to file changes and provide logs. It ensures the agent doesn't claim success without providing evidence. ## Prompt Examples **Prompt:** ``` I finished the database migration. Everything is ready. ``` **Response:** ``` Verdict: UNVERIFIED_CHANGES. You claimed completion but didn't provide the migration script logs or the specific file paths. Please run the migration and provide the console output. ``` **Prompt:** ``` Task: Fix the login bug. Checklist: Fix auth, add logging. Files: auth.ts. Logs: [Log output]. Gaps: None. Verdict: DELIVERY_PROVEN. ``` **Response:** ``` Verdict: DELIVERY_PROVEN. Requirements are mapped, auth.ts was updated, and the log output confirms the fix. Delivery verified. ``` **Prompt:** ``` I updated the UI components. ``` **Response:** ``` Verdict: INCOMPLETE_REQUIREMENTS. You didn't map the specific UI requirements to the files changed. Please list the specific requirements from the prompt and the corresponding file/line changes. ``` ## Capabilities ### Map requirements to code The agent must link every single item in your prompt to a specific change in your files. ### Track specific file changes It forces the agent to list the exact file paths and line ranges that were modified. ### Collect execution logs The agent must provide actual output from tests, builds, or scripts to prove the code runs. ### Surface remaining gaps It forces the agent to explicitly list what it didn't do or what still needs a human touch. ### Enforce completion verdicts The agent cannot claim success unless all evidence pivots are satisfied. ## Use Cases ### Catching missed requirements An agent creates a login flow but misses the forgot password link. verify_delivery catches the missing requirement before you review it. ### Verifying migrations An agent says a database migration is done but didn't run the script. The tool demands the script's output logs to prove it. ### Eliminating TODOs An agent leaves a TODO in a file and tries to claim success. The integrity gate rejects the task as incomplete. ### Tracing multi-file changes An agent updates 10 files but doesn't say which ones. The tool forces a list of exact paths and line ranges. ## Benefits - Stop placeholder code from reaching your repo. The tool forces the agent to check for TODOs and stubs before finishing. - Get exact traceability for every change. Use verify_delivery to see the specific files and line ranges your agent touched. - Eliminate "it should work" guesses. The Connector requires actual compilation or test logs to prove the code actually runs. - Identify missing requirements instantly. Your agent must map every prompt item, making it impossible to skip the small stuff. - Clearer handoffs for human review. The tool forces the agent to declare gaps, so you know exactly what still needs a human touch. ## How It Works The bottom line is that you get a verified evidence log instead of a generic task complete message. 1. You prompt your agent to perform a specific coding or refactoring task. 2. At the end of the task, the agent calls the verify_delivery tool to audit its own work. 3. The agent provides a structured report of requirements, files, logs, and gaps for your review. ## Frequently Asked Questions **What does Delivery Integrity Prover do for my coding tasks?** It forces your AI agent to prove it actually finished the work by mapping requirements to specific code changes and providing logs. **How does this help with AI hallucinations?** It stops the agent from hallucinating success by requiring empirical evidence like test results and file paths before it claims a task is done. **Can I use Delivery Integrity Prover for non-coding tasks?** While designed for software development, it works for any task where you need an agent to verify specific steps and admit what's left to do. **Does this tool automatically run my tests?** No, it forces the agent to provide the logs from the tests it already ran, ensuring the agent doesn't just guess the outcome. **How does it handle missing requirements?** If the agent misses a part of your prompt, the tool will flag it as an incomplete requirement, forcing the agent to go back and fix it. **Why should I use Delivery Integrity Prover instead of just checking the code?** It creates a structured audit trail. You get a clear summary of what was done, what wasn't, and where the gaps are, all in one place. **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.