Task Completion Enforcer Prover Connector for AI agents.
1 live capability
Guarantee your agent finishes every coding requirement and documentation task.
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Why people use Task Completion Enforcer Prover
Task Completion Enforcer Prover for Reliable Software Engineering
Task Completion Enforcer Prover stops this cycle. It forces your agent to create a literal punch list of your requirements before it touches the code. It then checks off every item with hard evidence, like a specific line number or a passing test result. You get a finished product that actually matches your request.
What Vinkius changes
That your agent stops making excuses and starts finishing every part of your request.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
The Done Lie
An agent says it finished 5 APIs but only did 3.
- Real-world use case 02
Placeholder Cleanup
You get a codebase full of TODO comments.
- Real-world use case 03
Documentation Drift
The code is done but the docs are old.
Complete set · 1capability
The complete Task Completion Enforcer Prover capability set.
These are the exact actions your AI can choose when you ask it to work with Task Completion Enforcer Prover.
01
1 capability in this set.
Part of 1 available through Task Completion Enforcer Prover.
- 01 Capability
Validate task completion
This capability forces the agent to prove it finished every requirement by mapping artifacts to a checklist. It catches placeholders and scope drift before the agent declares the task complete.
Set up in minutes
One URL. Then ask Task Completion Enforcer Prover to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Task Completion Enforcer Prover from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_TRZuombSa31k5jscX1KCJ089zhMtfMmLCTAbBgKK/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Task Completion Enforcer Prover, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Task Completion Enforcer Prover for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_TRZuombSa31k5jscX1KCJ089zhMtfMmLCTAbBgKK/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Task Completion Enforcer Prover URL.
- Step 03
Save and start
Save the connection and enable Task Completion Enforcer Prover in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"task-completion-enforcer-prover": {
"url": "https://edge.vinkius.com/vk_preview_TRZuombSa31k5jscX1KCJ089zhMtfMmLCTAbBgKK/mcp"
}
}
} - Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Task Completion Enforcer Prover
Open Agent mode in chat and ask: "Using Task Completion Enforcer Prover, help me...". 1 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"task-completion-enforcer-prover": {
"url": "https://edge.vinkius.com/vk_preview_TRZuombSa31k5jscX1KCJ089zhMtfMmLCTAbBgKK/mcp"
}
}
} - Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Task Completion Enforcer Prover
Ask Copilot: "Using Task Completion Enforcer Prover, help me...". 1 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"task-completion-enforcer-prover": {
"url": "https://edge.vinkius.com/vk_preview_TRZuombSa31k5jscX1KCJ089zhMtfMmLCTAbBgKK/mcp"
}
}
} - Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Task Completion Enforcer Prover
Open Cascade and ask: "Using Task Completion Enforcer Prover, help me...". 1 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"task-completion-enforcer-prover": {
"url": "https://edge.vinkius.com/vk_preview_TRZuombSa31k5jscX1KCJ089zhMtfMmLCTAbBgKK/mcp"
}
}
} - Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Task Completion Enforcer Prover
Ask Cline: "Using Task Completion Enforcer Prover, help me...". 1 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add task-completion-enforcer-prover --transport http "https://edge.vinkius.com/vk_preview_TRZuombSa31k5jscX1KCJ089zhMtfMmLCTAbBgKK/mcp" - Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Task Completion Enforcer Prover
Ask Claude: "Using Task Completion Enforcer Prover, show me...". 1 tools are ready
Where the request belongs
Work Task Completion Enforcer Prover can move forward.
Software engineers who are tired of manual QA on AI-generated code and project managers who need to ensure complex multi-step instructions do not get lost in translation.
Software Engineer
Uses it to ensure every edge case and documentation requirement in a PR is actually handled.
QA Engineer
Uses it to verify that test coverage matches the specific functional requirements provided.
Technical Product Manager
Uses it to ensure that complex feature requests do not get diluted or half-done during implementation.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsDelivery Integrity Prover
Forces AI agents to reflect on task execution, matching prompt requirements to actual changes, verifying logs, and declaring gaps before claiming completion.
Technical Writing Prover
An AI wrote API documentation for 'developers.' No expertise level. No prerequisites. A wall of text with no headings. Code examples that referenced a deprecated method. untested. Passive voice throughout: 'it is recommended that the configuration be updated.' A junior engineer followed the docs, deployed to production with the wrong config, and caused a 4-hour outage. This capability forces audience definition, task-based structure, tested examples, ambiguity elimination, and completeness verification.
Requirement Decomposition Prover
AI generates the happy path but omits error handling, edge cases, security, and observability. the '80% Problem'. This capability forces complete requirement decomposition BEFORE code generation: specify inputs/outputs, map failure modes, cover boundary conditions, validate OWASP, plan logging.
LLM Output Format Drift Detector
Detect structural deviations in LLM outputs against reference templates.
ContextQA
Automate testing via ContextQA. manage test suites, track AI-healing executions, trigger automated runs, and audit API tests directly from any AI agent.
Testim
Trigger automated AI tests, inspect execution logs, and manage branches natively via your AI agent.
Bring your own AI
Change the model, client or framework. Keep Task Completion Enforcer Prover connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Task Completion Enforcer Prover.
The practical details behind the request, access and result.
How does Task Completion Enforcer Prover stop my agent from leaving TODOs?
It forces the agent to identify every requirement as a checklist. If it finds a gap or a placeholder, it is required to fix it immediately before it can move to the next step.
Can Task Completion Enforcer Prover handle complex coding tasks?
Yes. It is specifically designed for multi-step coding projects where missing a single detail like a test case or a specific documentation update causes a headache.
Does Task Completion Enforcer Prover work with Cursor or VS Code?
It works with any MCP-compatible client, including Cursor, VS Code, and Claude, allowing you to use it directly within your existing development environment.
Why does my agent keep saying it's done when it isn't?
LLMs often prioritize finishing a generation over finishing a task. This Connector forces the agent to provide verifiable evidence for every requirement before it is allowed to say it is done.
How does the evidence mapping work in Task Completion Enforcer Prover?
The agent must link every finished requirement to a specific artifact, such as a file path, a line number, or a specific test result, to prove the work was actually completed.
Will Task Completion Enforcer Prover help with documentation?
Yes. It treats documentation as a mandatory requirement. The agent cannot declare the task finished until it provides evidence that the docs have been updated as requested.
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 capability 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.
One connection away
Give your agent a direct line to Task Completion Enforcer Prover.
Connect Task Completion Enforcer Prover once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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