LLM XML Tag Parser Connector for AI agents.

Q: Can I use this to validate if my prompt output is well-formed?

Yes, by using the validatetagintegrity tool, you can check if every opening tag has a corresponding closing tag and verify that the nesting depth is balanced.

Q: What happens if a tag is not found?

If you use extractsingletag and the target tag does not exist in the input string, the tool will return null for the extracted content.

3 live capabilities

Extract structured data from messy XML-style model outputs.

Live agent request LLM XML Tag Parser / Connector

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AI Agent

Why people use LLM XML Tag Parser

LLM XML Tag Parser for cleaning messy model outputs

With this MCP, your agent handles the extraction for you. It looks at the raw text, finds the tags, and gives you back exactly what is inside them, clean and ready to use.

What Vinkius changes

You get structured data from messy text without writing custom regex.

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

One account · 5,900+ Connectors

Real-world use case 01

Parsing reasoning blocks

An engineer needs to separate <think> blocks from final answers to clean up logs.
Real-world use case 02

Batching records from text

A developer wants to extract all <item> tags from a single large response.
Real-world use case 03

Validating model outputs

An automation script receives a malformed XML string.

Complete set · 3capabilities

The complete LLM XML Tag Parser capability set.

These are the exact actions your AI can choose when you ask it to work with LLM XML Tag Parser.

Capability set01 / 01

01—03

3 capabilities in this set.

Part of 3 available through LLM XML Tag Parser.

01 Capability
Extract single tag
Grabs the very first occurrence of a specific tag. Use this when you only need one specific piece of information.
02 Capability
Validate tag integrity
Checks if your XML tags are properly balanced and nested. It prevents errors caused by broken structures.
03 Capability
Extract all tags
Finds every top-level instance of a specific tag in your text. This is great for pulling multiple records at once.

Set up in minutes

One URL. Then ask LLM XML Tag Parser to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use LLM XML Tag Parser 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_w58Iyf6iS1Dvk9wxnoCriPGJu7Dnb9x0GKyijsMS/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 LLM XML Tag Parser, and paste the URL above.
Step 03

Turn it on in chat

Select +, open Connectors, and enable LLM XML Tag Parser for the conversation.

ChatGPT · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP

https://edge.vinkius.com/vk_preview_w58Iyf6iS1Dvk9wxnoCriPGJu7Dnb9x0GKyijsMS/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 LLM XML Tag Parser URL.
Step 03

Save and start

Save the connection and enable LLM XML Tag Parser in your conversation. Desktop may ask you to restart once.

Cursor · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "llm-xml-tag-parser": {
      "url": "https://edge.vinkius.com/vk_preview_w58Iyf6iS1Dvk9wxnoCriPGJu7Dnb9x0GKyijsMS/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 LLM XML Tag Parser

Open Agent mode in chat and ask: "Using LLM XML Tag Parser, help me...". 3 tools available

VS Code Copilot · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "llm-xml-tag-parser": {
      "url": "https://edge.vinkius.com/vk_preview_w58Iyf6iS1Dvk9wxnoCriPGJu7Dnb9x0GKyijsMS/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 LLM XML Tag Parser

Ask Copilot: "Using LLM XML Tag Parser, help me...". 3 tools available

Windsurf · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "llm-xml-tag-parser": {
      "url": "https://edge.vinkius.com/vk_preview_w58Iyf6iS1Dvk9wxnoCriPGJu7Dnb9x0GKyijsMS/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 LLM XML Tag Parser

Open Cascade and ask: "Using LLM XML Tag Parser, help me...". 3 tools available

Cline · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "llm-xml-tag-parser": {
      "url": "https://edge.vinkius.com/vk_preview_w58Iyf6iS1Dvk9wxnoCriPGJu7Dnb9x0GKyijsMS/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 LLM XML Tag Parser

Ask Cline: "Using LLM XML Tag Parser, help me...". 3 tools available

Claude Code · Terminal command

Official guide ↗

Advanced setup

claude mcp add llm-xml-tag-parser --transport http "https://edge.vinkius.com/vk_preview_w58Iyf6iS1Dvk9wxnoCriPGJu7Dnb9x0GKyijsMS/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 LLM XML Tag Parser

Ask Claude: "Using LLM XML Tag Parser, show me...". 3 tools are ready

Where the request belongs

Work LLM XML Tag Parser can move forward.

Built around the request

Developers and prompt engineers who are tired of parsing broken XML strings manually.

AI Engineer

Automating the extraction of specific parameters from model outputs during evaluation.

Automation Specialist

Building reliable pipelines that depend on consistent, structured data from LLM responses.

Software Developer

Integrating LLM-generated content into existing applications without manual cleanup.

Build the capability set

Each Connector adds new actions and data without changing how you work.

Browse Connectors

01 3 capabilities New

RAG Payload Metadata Extractor

Extracts metadata, structural markers, and efficiency metrics from RAG source documents using deterministic regex patterns.

View Connector

02 1 capability New

XML Tag Structure Compliance Checker

Validate XML tag integrity, nesting hierarchy, and tag-to-text density.

View Connector

03 3 capabilities New

LLM Output JSON Extractor

Isolates valid JSON objects from mixed markdown/text LLM outputs using deterministic bracket-matching.

View Connector

04 1 capability

Regex Extractor Engine

Stop LLM hallucination when extracting data. Run pure Regex across massive text blocks and guarantee 100% accurate array extraction without missing a single match.

View Connector

05 1 capability

Regex High-Perf Parser

Stop LLM hallucination when extracting entities. Run pure Regex across massive text blocks and guarantee 100% accurate array extraction.

View Connector

06 12 capabilities

Parsio

Extract structured data from emails and PDFs automatically with AI-powered parsing templates that learn from your documents.

View Connector

Bring your own AI

Change the model, client or framework. Keep LLM XML Tag Parser 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 LLM XML Tag Parser.

The practical details behind the request, access and result.

How can I use LLM XML Tag Parser to clean up Claude outputs?

You can use it to separate reasoning from final answers by targeting specific tags like or , leaving you with just the useful data.

Does LLM XML Tag Parser work with nested tags?

Yes, it is designed to track depth and handle complex, multi-layered XML structures without losing track of where tags begin and end.

Can I use LLM XML Tag Parser for batch data extraction?

Absolutely. You can instruct your agent to find every instance of a specific tag in a large block of text to pull out multiple records at once.

Will LLM XML Tag Parser help prevent errors in my automation pipeline?

It helps significantly by allowing you to validate that the tags in an LLM response are properly balanced and structurally sound before you process them.

Is LLM XML Tag Parser useful for parsing model reasoning?

Yes, it allows you to isolate the or blocks from the actual output so your application only processes the final result.

How does the parser handle nested tags?

The parser uses an integer depth counter. When it encounters an opening tag, it increments the counter; when it finds a closing tag, it decrements it. This ensures that extract_all_tags correctly identifies fully closed pairs even in complex structures.

Can I use this to validate if my prompt output is well-formed?

Yes, by using the validate_tag_integrity capability, you can check if every opening tag has a corresponding closing tag and verify that the nesting depth is balanced.

What happens if a tag is not found?

If you use extract_single_tag and the target tag does not exist in the input string, the capability will return null for the extracted content.

One connection away

Give your agent a direct line to LLM XML Tag Parser.

Connect LLM XML Tag Parser once. Keep it beside 5,900+ managed Connectors when the next task needs more.

Explore every Connector No credit card required · Free tier available

LLM XML Tag Parser for cleaning messy model outputs

You get structured data from messy text without writing custom regex.

Parsing reasoning blocks

Batching records from text

Validating model outputs

Extract single tag

Validate tag integrity

Extract all tags

Open Connectors

Add the URL

Turn it on in chat

Open MCP settings

Add the URL

Save and start

Open MCP Settings

Add the server config

Save the file

Start using LLM XML Tag Parser

Create MCP config

Add the server config

Enable Agent mode

Start using LLM XML Tag Parser

Open MCP Settings

Add the server

Save and reload

Start using LLM XML Tag Parser

Open Cline MCP Settings

Add remote server

Enable the server

Start using LLM XML Tag Parser

Install Claude Code

Add the Connector

Verify the connection

Start using LLM XML Tag Parser

AI Engineer

Automation Specialist

Software Developer

RAG Payload Metadata Extractor

XML Tag Structure Compliance Checker

LLM Output JSON Extractor

Regex Extractor Engine

Regex High-Perf Parser

Parsio

Give your agent a direct line to LLM XML Tag Parser.