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Vinkius

Microcopy Analyzer Connector for AI agents.

5 live capabilities

Audit UI text for clarity and user friction to improve accessibility.

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Why people use Microcopy Analyzer

Microcopy UX Friction Analyzer for UX Audits

This Connector changes that by letting you run a systematic audit on your copy directly through your AI client. You just feed in the text and get a breakdown of friction points, clarity scores, and specific suggestions to make the interface more accessible.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • Visual Studio Code
  • Windsurf

What Vinkius changes

You get a data-backed audit of your UI copy to make your product more accessible.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 6,100+ Connectors

  1. Real-world use case 01

    Auditing a 'Password Incorrect' flow

    A UX writer notices that the error message feels aggressive.

  2. Real-world use case 02

    Cleaning up a messy dashboard

    A designer finds that many tooltips repeat what the label already says.

  3. Real-world use case 03

    Accessibility check for a new app

    An accessibility lead wants to ensure the app is readable for everyone.

Complete set · 5capabilities

The complete Microcopy Analyzer capability set.

These are the exact actions your AI can choose when you ask it to work with Microcopy Analyzer.

Capability set01 / 02

01—03

3 capabilities in this set.

Part of 5 available through Microcopy Analyzer.

  1. 01 Capability

    Check element redundancy

    Use check_element_redundancy to find out if your labels and hints are repeating the same information.

  2. 02 Capability

    Detect linguistic friction

    Use detect_linguistic_friction to spot tone mismatches or language that blames the user for an error.

  3. 03 Capability

    Evaluate clarity metrics

    Use evaluate_clarity_metrics to get a score on how easy it is for a human to understand a specific string.

Capability set02 / 02

04—05

2 capabilities in this set.

Part of 5 available through Microcopy Analyzer.

  1. 04 Capability

    Verify actionability

    Use verify_actionability to see if your instructions actually provide a clear next step for the user.

  2. 05 Capability

    Assess reading complexity

    Use assess_reading_complexity to check if your text matches your target audience's reading level.

Set up in minutes

One URL. Then ask Microcopy Analyzer to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Microcopy Analyzer from the conversation.

Choose your client

Live preview
Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP
https://edge.vinkius.com/vk_preview_20onXYeaJduzGoRNe2i4bdYjfTwkKruoGTVECgGh/mcp
  1. Step 01

    Open Connectors

    In Claude Web or Claude Desktop, open Settings and choose Connectors.

  2. Step 02

    Add the URL

    Choose Add custom connector, name it Microcopy Analyzer, and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable Microcopy Analyzer for the conversation.

Where the request belongs

Work Microcopy Analyzer can move forward.

Built around the request

UX writers who need to audit thousands of strings quickly, or accessibility leads making sure the interface doesn't leave anyone behind.

01

UX Writer

Auditing a new design system for consistency and tone on a Tuesday afternoon.

02

Product Designer

Checking error states for a high-traffic dashboard to ensure they aren't frustrating.

03

Accessibility Specialist

Ensuring screen reader labels aren't redundant and match the target reading level.

Bring your own AI

Change the model, client or framework. Keep Microcopy Analyzer connected.

  • 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 Microcopy Analyzer.

The practical details behind the request, access and result.

Can the Microcopy UX Friction Analyzer help me make my app more accessible?

Yes, it helps by checking reading levels and identifying linguistic barriers. It ensures your text is clear and easy for everyone to understand, which is a key part of accessibility.

How does the Microcopy UX Friction Analyzer find user-blaming language?

It scans your text for specific patterns that put the fault on the user. It then suggests neutral alternatives that focus on solving the problem instead of pointing fingers.

Can I use this to check my tooltips?

Yes, it's great for tooltips. It checks for clarity and redundancy to make sure your tooltips are actually helpful and not just adding to the clutter.

Does the Microcopy UX Friction Analyzer give specific fix suggestions?

Yes, it provides concrete ways to rewrite your text. It identifies where a string is failing and gives you a clear path to make it more actionable.

Is this good for B2B software?

It's excellent for B2B. Complex software often has dense language; this capability helps simplify that language so users can complete tasks faster without confusion.

How does this help with reducing cognitive load?

It identifies jargon and redundant text that force users to think harder. By simplifying those interactions, it makes your app feel much easier to use.

What does the clarity metric measure?

It calculates jargon density and average word length to determine how difficult a text string is to understand.

How can I detect user-blaming language?

Use the detect_linguistic_friction capability to identify passive or accusatory phrasing in your error messages.

Can this capability check for redundant UI elements?

Yes, the check_element_redundancy capability compares labels, input hints, and helper text to find semantic overlaps.

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