Task Completion Enforcer Prover MCP for AI. Forces AI agents to prove they finished every single requirement.
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Task Completion Enforcer Prover provides a rigorous, multi-stage audit for AI outputs. When you run this tool, it forces your agent to execute five checks: listing every original requirement; providing specific code evidence (file/line numbers); identifying all work gaps; completing the missing work immediately; and comparing the final output line-by-line against the initial request.
It stops declaring 'done' until everything is proven.
What your AI can do
Validate task completion
Forces the AI agent to prove task completion by running a five-axis audit: extracting requirements, providing evidence, identifying gaps, closing those gaps, and performing final verification against the original request.
It forces your agent to extract and list every single requirement from the initial user prompt as a numbered checklist.
The tool requires specific artifacts for each requirement, naming file paths, line numbers, or function calls instead of just saying 'I did it'.
It automatically finds and reports any requirements that lack evidence or which the agent skipped over.
The system rejects outputs containing placeholders like TODO, FIXME, or 'TBD' until they are replaced with actual, working code.
It checks that the final output only addresses the original prompt and doesn't veer off into an adjacent, but incorrect, problem area.
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Task Completion Enforcer Prover: 1 Tools for Code Auditing
Use validate_task_completion to run a comprehensive audit on any AI-generated task, ensuring every requirement is met with verifiable proof.
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Forces the AI agent to prove task completion by running a five-axis audit: extracting requirements, providing evidence, identifying gaps...
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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 hardest part about using LLMs isn't generating code; it's proving they finished everything you asked for.
Right now, when you ask your agent to build something complex—say, five API endpoints with full testing and documentation—you get a massive output. You then spend 40 minutes manually scrolling through the response, checking if they forgot one endpoint or if the tests only cover the 'happy path.' You're basically doing the job of an audit team just to trust the AI.
With the Task Completion Enforcer Prover, that tedious manual review disappears. The tool forces a structured workflow: it breaks down your request into numbered items and demands concrete evidence (like file paths or line numbers) for every single one. You get proof, not promises.
Task Completion Enforcer Prover: Demand verifiable completion.
Before this tool, the workflow was: Prompt -> Output (with gaps/TODOs) -> Manual Audit (hours of review). You were always starting the audit process over again because the agent declared 'done' prematurely.
Now, you prompt, and the server runs a formal, multi-step verification cycle. It keeps looping through gap identification and continuation execution until it returns `DELIVERY_PROVEN`. The whole manual auditing cycle is replaced by one reliable tool call.
What your AI can actually do with this
This server's job is simple: it makes sure your AI agent actually finishes what you asked for. It runs a five-axis audit that refuses to let your agent declare 'done' until everything's proven and verified. You won't get vague summaries here; you only get verifiable proof.
The validate_task_completion tool forces your agent through a rigorous, multi-stage process every single time it runs. First, it makes the agent list every original requirement from your prompt as a numbered checklist. This step ensures that nothing gets missed because of oversight or assumption.
For each item on that mandatory list, the server demands proof of implementation. It won't accept 'I did it'; it needs specific artifacts—file paths, line numbers, or function calls pointing directly to the work. If your agent just says it wrote a class, this tool makes it cite exactly where that class lives and what lines are involved.
If anything is missing, the system flags it immediately. It automatically finds and reports any requirement for which there's no evidence, or any piece of work the agent skipped over entirely. Furthermore, it shuts down the output if your agent leaves in placeholders like TODO, FIXME, or 'TBD'; you gotta replace those with actual, working code before anything moves forward.
The process keeps running until every single gap is closed. The agent must address and fix everything flagged by the tool, then call it back out again to re-verify its own work. This isn't a suggestion; it’s mandatory for the task to proceed. Finally, the server performs a deep comparison of the resulting code or document against your initial prompt.
It checks that the final output only addresses what you asked for and doesn't drift off into some adjacent, but totally incorrect, problem area. If even one line of the final product doesn't match the scope laid out in your original request, the whole thing fails the audit, and the loop starts over.
019ea63f-df06-7239-8ed5-4cb4db3e9f44 
Here's how it actually works
The bottom line is: it turns an AI's self-declaration of completion into a structured, auditable, and verifiable engineering deliverable.
Start by feeding the tool your initial request. It immediately extracts a numbered checklist of all requirements.
Your agent then works through the task and calls the tool repeatedly, providing concrete evidence (file/line numbers) for each requirement until zero gaps are reported.
The process concludes only when the system confirms that every line of the original prompt matches verifiable output.
Who is this actually for?
Engineers and technical leads who write prompts for LLMs regularly. You're the one tired of spending half your day manually checking if an AI actually implemented all five endpoints or just wrote a summary that said 'endpoints created.' This tool gives you undeniable proof.
Uses this to validate code generated by their agent, ensuring every single requirement from the ticket is built, tested, and documented before merging.
Runs it over complex system designs to guarantee that scope drift didn't happen—that the AI solved the right problem, not an adjacent one.
Uses this when automating deployment pipelines; they need proof that every configuration file update and script implementation is accounted for.
What Changes When You Connect
Eliminates Requirement Amnesia. You don't have to manually check if the agent forgot one of the five items asked for; the tool forces an explicit, numbered checklist against the original prompt.
Stops Premature Completion. The server won't let your agent call 'done.' It keeps running until it has verified evidence for every single component, preventing summary-based false positives.
Catches Placeholder Infection. If your agent leaves a TODO or FIXME, the tool finds it and forces you to write actual code instead of submitting a skeleton.
Guarantees Scope Adherence. It prevents scope drift by comparing every line of the output back to the original request, ensuring the AI solved that problem, not a related one.
Provides Full Audit Trail. The process generates an audit trail—the five axes (Requirements, Evidence, Gaps, Continuation, Verification)—giving you undeniable proof of completeness.
See it in action
Building a Microservice API
A developer asks their agent to create 5 endpoints and write tests. The agent finishes 3 and calls 'done.' Running the Task Completion Enforcer Prover immediately flags Requirement Amnesia, forcing the agent to build the two missing endpoints before it can proceed.
Updating Compliance Documentation
You ask an agent to update a 50-page compliance manual with three specific regulatory changes. The agent writes the sections but leaves placeholders like [Pending Review] and fails to reference two mandatory source documents. The Prover catches Placeholder Infection, forcing it to cite the real sources.
Refactoring Legacy Codebase
You task your AI agent with modernizing a module's authentication flow (Login, Register, Reset). The agent implements Login but solves a more complex 'user profile update' problem instead. Using the Prover catches Scope Drift, forcing the agent to stick strictly to the original auth requirements.
Completing Multi-Step Onboarding
You ask an agent to set up user roles (Admin, Editor, View Only) across three different systems. The agent implements Admin and Editor but fails on the third system's API calls. The Prover identifies a gap in the final verification step, ensuring all three role levels are accounted for.
The honest tradeoffs
Summarizing Progress
Agent: 'I built most of what you asked and covered everything.' User: 'Okay, great.' Problem: The agent's confidence level is irrelevant; the work must be proven.
Don't accept summaries. Run the Task Completion Enforcer Prover. It forces the AI to list every requirement and attach specific file/line evidence for each item.
Using 'Later' or `TODO`
Agent: 'I added a placeholder here, I'll fix it later.' User: 'Okay, great.' Problem: A placeholder is an admission of failure; the task isn't done.
The Prover detects Placeholder Infection. It demands you fill in every gap immediately and call the tool again until all placeholders are replaced with working code.
Ignoring the Original Goal
Agent: 'I solved your related problem about database indexing.' User: 'Wait, I asked for API endpoints!' Problem: The agent drifted off-topic.
Use the Prover's final verification step. It forces a line-by-line comparison of the output against the original request to catch scope drift.
When It Fits, When It Doesn't
Use this server if your primary pain point is trusting the AI agent's self-report. If you need proof that an LLM followed complex, multi-part instructions—for example, 'Write A, then test B, and update C with specific content'—this tool is mandatory. It provides a structured audit loop for any technical task.
Don't use it if you just need simple brainstorming or general text drafting. For those tasks, simpler prompt engineering works fine. You only need this high level of enforcement when the cost of failure (missing an endpoint, leaving a gap) is genuinely high.
Questions you might have
How does the Task Completion Enforcer Prover know what requirements I asked for? +
It starts by analyzing your original request and automatically generating a numbered, specific checklist of every actionable requirement. This list becomes the absolute standard against which all subsequent work is measured.
Does Task Completion Enforcer Prover just check if I mentioned five endpoints? +
No. It checks for physical evidence. For each endpoint, it requires specific artifacts—a file path, a line number, or a test result—proving the code actually exists and works.
What happens if my agent leaves a TODO comment after using Task Completion Enforcer Prover? +
The tool detects this as Placeholder Infection. It stops execution and forces the agent to replace every placeholder with actual, working code before allowing the process to continue.
Is Task Completion Enforcer Prover useful for general knowledge retrieval? +
No. This tool is built specifically for enforcing complex task completion in technical outputs (code, documentation). It won't help you find a random fact; it only verifies work against a defined scope.
If `validate_task_completion` identifies gaps, does it just stop or force me to finish the work? +
It forces continuous work until all axes pass. If any gap remains after a check, the tool will not declare completion. You must fix the specific missing pieces and call the function again.
What kind of evidence does Task Completion Enforcer Prover accept for proof of work? +
It requires concrete artifacts like file paths, line numbers, or test results. General statements or summaries are not enough; it demands specific proof that the requirement was met.
Does using Task Completion Enforcer Prover require me to keep the original user request visible? +
Yes. The tool must re-read and compare every line of your output against the original prompt multiple times. Keeping the source material accessible is critical for verification.
Can Task Completion Enforcer Prover handle massive, multi-domain requirements in one go? +
It handles complex tasks by breaking them into five verifiable axes. For extremely large requests, splitting the work into smaller, domain-specific chunks often yields the most reliable results.
Why do LLMs forget requirements? +
Autoregressive generation allocates decreasing attention to earlier tokens as output grows. A 10-step task at token 200 competes with 2,000 tokens of generated output for attention. The model literally loses track of requirement #7 while implementing requirement #3. The fix: force a re-read of ALL requirements before declaring completion.
What counts as 'completion evidence'? +
Not 'I implemented the function.' Evidence means: 'Requirement 1: POST /users endpoint at src/routes/users.ts lines 15-42, validates email/name/role via Zod schema, returns 201 with user object.' File path, line number, specific behavior. If you cannot point to the exact artifact, it is not done.
What happens when gaps are found? +
The LLM MUST close them immediately — not later, not in a follow-up. Do the remaining work NOW. Then call this tool AGAIN to verify the gaps are actually closed. The loop continues until EVERY requirement has concrete evidence. 'I will do it later' is rejected. 'I just did it, here is the evidence' is accepted.
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