
Connect Context Integrity Prover to ChatGPT and Claude
Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Get exact text measurements that your AI client can actually trust.
Developed, maintained, and hosted by Vinkius.
MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED
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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.
Your AI does the work. We keep it safe and sound.
No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.
Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

Each Connector runs inside its own sealed environment.
Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.
One switch stops every agent. Instantly.
One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.
Spending limits that pause instead of surprise.
Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.
Three controls, built into the layer your AI connects through.
Curated set · 1 capability
We ship only the actions that matter.
01
1 capability in this set.
Part of 1 available through Context Integrity Prover.
- 01
Validate context integrity
The most dangerous failure mode in multi-step reasoning is not being wrong. it is being right about the wrong problem. Each step may look reasonable, but cumulative drift can take you miles from the original request. You must: (1) MAP ORIGINAL CONSTRAINTS. quote the user's exact words. What did they ask for? What did they NOT ask for? What boundaries did they set? What is the precise deliverable? Do not paraphrase. quote. Paraphrasing introduces interpretation drift, (2) MAINTAIN SCOPE BOUNDARIES. define what you are NOT doing. Every task has a boundary. If you cannot name what is out of scope, you have not defined scope. List 3+ things that are explicitly out of scope for this task, (3) REJECT OUT OF SCOPE. explicitly identify and reject at least one tangential task that could be mistaken as in-scope. "While we are at it" and "it would also be nice to" are scope creep. Name the tangential task and state WHY it is out of scope, (4) PREVENT CONTEXT DRIFT. compare your current direction with the original request. Are you still solving the same problem? Has the solution evolved beyond what was asked? Check each step: does it serve the original intent, or has it become self-referential? (5) VALIDATE ASSUMPTIONS. list every assumption you are making that the user did NOT explicitly state. For each: is this assumption necessary? Did you invent a constraint? Could you ask instead of assuming? Hallucinated constraints are invisible. you believe them because you created them, (6) MATCH SOLUTION TO INTENT. prove the solution you are building matches the problem the user stated. Not the problem you find interesting. Not the problem you know how to solve. The problem they asked about. If rejected, you have drifted from the original intent. Re-read the user's request and start from their words.Send ALL parameters in ONE call. the analyses above plus YOUR OWN verdict: every PIVOT boolean, verdict and clarification. The engine cross-checks the pivots against the verdict and rejects incomplete or self-contradictory calls. Structured reflection capability for context integrity validation during multi-step execution. Forces the agent to prove the original user intent is preserved across every step. no scope creep, no context drift, no hallucinated constraints, no gold-plating. Catches Constraint Neglect (losing the original requirements mid-execution. the user said X, you are now doing Y), Scope Creep (adding unrequested features or analyses. "while we are at it" is the death of focus), Failure to Reject (accepting tangential tasks instead of explicitly declining out-of-scope work), Context Drift (progressive deviation from the original goal. each step looks reasonable but the cumulative direction is wrong), Hallucinated Constraints (inventing restrictions the user never stated. "we cannot do X" when nobody said that), and Intent Mismatch (the solution addresses a problem the user did not have). Call once before executing a multi-step plan, and again when you suspect drift
Real work
What teams actually run
Social Media Posting
An agent drafts a post and uses the MCP to ensure it stays under the 280-character limit for X.
SEO Metadata Generation
The agent writes meta descriptions and validates that they do not exceed character limits for search engines.
Academic and Long-form Writing
An agent drafts an essay and checks that the word count falls within a specific range.
Automated Newsletter Drafting
The agent calculates reading time based on word counts for newsletter subscribers.
Automate a task
Put Context Integrity Prover to work on one thing.
Step-by-step automations your AI runs through this Connector: the workflow, the prompts to start with, and the exact capabilities behind each move.
One connector, every AI
Context Integrity Prover works with the most popular AI clients.
These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.
Claude
ChatGPT
Gemini
Perplexity
Grok
Microsoft Copilot
Cursor
VS Code
Windsurf
JetBrains
Cline
LangChain
Vercel AI SDK
Lovable
Z.ai
Raycast
Qwen Code
Kimi Code
Le ChatBuilding your own app? The connector is yours to use.
You don't need a client to put Context Integrity Prover to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.
Observed, not estimated
973ms average. Fast in production.
Context Integrity Prover is checked daily against the live service.
- Fastest day
- 779ms
- Slowest day
- 1189ms
- 14-day trend
- Slowing+8%
Connect your client
One URL. Every client.
Activate the Connector, copy your link, and paste it into the client you already use. 1 capability arrives ready to run.
Preview access · not provider authentication
The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Context Integrity Prover, so you can see the experience inside your AI.
It does not authenticate your account with Context Integrity Prover. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.
Context Integrity Prover Connector
You're all set. Choose your MCP client and follow the setup instructions.
https://edge.vinkius.com/vk_preview_70bXrUoEOZL7ZOMf4REOFw6TwTbYiAu2CFDlbhuI/mcpClaude Desktop
Follow the steps below to connect in seconds.
- 1In Claude Desktop, open Settings → Connectors.
- 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
- 3Click Add and start a new chat — Context Integrity Prover capabilities are ready to use.
{
"mcpServers": {
"context-integrity-prover-mcp": {
"url": "https://edge.vinkius.com/vk_preview_70bXrUoEOZL7ZOMf4REOFw6TwTbYiAu2CFDlbhuI/mcp"
}
}
}
Claude
ChatGPT
Cursor
VS Code
Windsurf
Claude Code
JetBrains
Cline
Step-by-step instructions for each client are in the guide. How to connect
Guided setup for Claude? Set up Context Integrity Prover in Claude and other AIs
Who it's for
Built for the work Context Integrity Prover owners hand off.
This MCP is built for anyone running automated text workflows where length constraints are non-negotiable.
- 01
Content Automators
They hand off bulk text generation tasks to agents that must follow strict formatting rules.
- 02
Social Media Managers
They use agents to draft posts that must fit within specific platform character limits.
- 03
SEO Specialists
They rely on agents to generate meta descriptions and snippets that stay within search engine limits.
FAQ
Questions Context Integrity Prover owners ask.
- 01
Why do AI agents struggle with word counts?
LLMs process text as tokens rather than individual characters or words, which makes native counting inaccurate. This MCP provides the deterministic math the agent lacks.
- 02
Can this MCP help with character limits including spaces?
Yes, the capability can measure exact character lengths both with and without spaces.
- 03
How does this prevent AI hallucinations?
Instead of the agent guessing the length, it uses the MCP to perform a real calculation, providing a factual measurement.
- 04
Which AI clients can use this MCP?
You can use this with any MCP-compatible client, including Claude, Cursor, and Windsurf.
- 05
Can I set a maximum limit for the agent to check against?
Yes, you can pass a max parameter to enable threshold validation, which returns whether the text passes or fails.
- 06
Can it count characters with and without spaces?
Yes, the character counting capability provides an includeSpaces boolean option to include or exclude spaces.
- 07
How does limit validation work for agents?
Agents pass an optional 'max' parameter. The server checks the count against the limit and returns a 'pass' true/false status, along with the percentage filled and characters/words remaining.
- 08
Can it calculate the reading time of an article?
Yes, the estimate_reading_time capability calculates the duration based on a configurable words-per-minute baseline.
- 09
Why force rejection of scope?
LLMs are sycophants. They always say yes to new suggestions. Forcing them to explicitly state what they are rejecting proves they understand where the boundaries are.
- 10
What is a hallucinated constraint?
When an AI invents a rule (like 'we must use TypeScript') that the user never actually asked for. It adds artificial complexity and delays execution.
- 11
How do you prove intent matches?
By doing a parity check between the user's initial prompt and the final deliverable, ensuring no features were skipped and no extra work was injected.
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Connect Context Integrity Prover to Claude, Cursor, ChatGPT & more
