Structured Output Extractor Connector for AI agents.
3 live capabilities
Recover clean JSON from messy LLM text responses
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Why people use Structured Output Extractor
Fixing broken LLM data with Structured Output Extractor
With this MCP, that headache disappears. You stop fighting the model's personality and start treating its output as a raw resource. You feed the messy text into the extractor, and it pulls out exactly what you asked for, leaving the conversational fluff behind. You get clean, actionable data every single time.
What Vinkius changes
You stop writing custom regex for every single LLM error.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
Fixing broken JSON in production
An agent returns a JSON object wrapped in a long explanation, causing a parser error.
- Real-world use case 02
Automated data entry from emails
You feed an unstructured email body into the MCP to pull out names, dates, and amounts into a clean format.
- Real-world use case 03
Validating agentic workflows
A developer uses validate_schema_integrity to make sure their complex nested schemas are actually usable by the extractor.
Complete set · 3capabilities
The complete Structured Output Extractor capability set.
These are the exact actions your AI can choose when you ask it to work with Structured Output Extractor.
01—03
3 capabilities in this set.
Part of 3 available through Structured Output Extractor.
- 01 Capability
Extract structured data
Pulls specific fields from a block of text using a provided schema. It finds the signal in the noise of conversational responses.
- 02 Capability
Validate schema integrity
Checks if your schema definition is logically sound. It ensures your naming conventions won't cause extraction failures.
- 03 Capability
Get extraction summary
Provides a high-level overview of how reliable an extraction attempt was. It helps you judge the quality of the results.
Set up in minutes
One URL. Then ask Structured Output Extractor to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Structured Output Extractor 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_t2JRhUtfAFgS7pSyrQIWZ5Ig2o8n0AmHETaNyAuC/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 Structured Output Extractor, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Structured Output Extractor for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_t2JRhUtfAFgS7pSyrQIWZ5Ig2o8n0AmHETaNyAuC/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 Structured Output Extractor URL.
- Step 03
Save and start
Save the connection and enable Structured Output Extractor in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"structured-output-extractor": {
"url": "https://edge.vinkius.com/vk_preview_t2JRhUtfAFgS7pSyrQIWZ5Ig2o8n0AmHETaNyAuC/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 Structured Output Extractor
Open Agent mode in chat and ask: "Using Structured Output Extractor, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"structured-output-extractor": {
"url": "https://edge.vinkius.com/vk_preview_t2JRhUtfAFgS7pSyrQIWZ5Ig2o8n0AmHETaNyAuC/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 Structured Output Extractor
Ask Copilot: "Using Structured Output Extractor, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"structured-output-extractor": {
"url": "https://edge.vinkius.com/vk_preview_t2JRhUtfAFgS7pSyrQIWZ5Ig2o8n0AmHETaNyAuC/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 Structured Output Extractor
Open Cascade and ask: "Using Structured Output Extractor, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"structured-output-extractor": {
"url": "https://edge.vinkius.com/vk_preview_t2JRhUtfAFgS7pSyrQIWZ5Ig2o8n0AmHETaNyAuC/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 Structured Output Extractor
Ask Cline: "Using Structured Output Extractor, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add structured-output-extractor --transport http "https://edge.vinkius.com/vk_preview_t2JRhUtfAFgS7pSyrQIWZ5Ig2o8n0AmHETaNyAuC/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 Structured Output Extractor
Ask Claude: "Using Structured Output Extractor, show me...". 3 tools are ready
Where the request belongs
Work Structured Output Extractor can move forward.
This is for developers and data engineers who are tired of their automated workflows crashing because an LLM decided to say 'Sure, here is your data:' before the JSON.
AI Engineer
Fixing broken JSON outputs in production pipelines so the agentic workflow doesn't stall.
Data Engineer
Converting unstructured text blobs from various LLMs into clean, queryable database records.
Backend Developer
Ensuring that AI-generated responses don't break strict API contracts or database schemas.
Build the capability set
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Each Connector adds new actions and data without changing how you work.
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Bring your own AI
Change the model, client or framework. Keep Structured Output Extractor 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 Structured Output Extractor.
The practical details behind the request, access and result.
How can I use Structured Output Extractor to fix JSON errors?
When an AI client returns text instead of pure JSON, this MCP uses pattern matching to find the JSON block and pull out the specific fields you need, effectively bypassing the conversational filler.
Will Structured Output Extractor work with any AI client?
Yes, as long as you are using an MCP-compatible client like Claude, Cursor, or Windsurf, you can use this to clean up the data those models produce.
Can I use Structured Output Extractor for non-JSON data?
Yes. While it's great for JSON, it can extract any specific fields you define in your schema from a block of unstructured text.
How do I know if the extraction was successful?
You can use the summary capability to get a high-level report on the reliability and success of your extraction attempts, helping you spot patterns of failure.
Does Structured Output Extractor require a lot of setup?
No. You connect it via Vinkius and then simply provide the text and the schema you want to use. It's designed to be a plug-and-play rescue capability.
How does this capability extract data without using an LLM?
The capability uses deterministic regex patterns to locate field names (anchors) and extract the subsequent values based on the expected type (string, number, boolean, or list).
What happens if the LLM response is completely unstructured?
The extract_structured_data capability will attempt to find the requested fields. If the patterns do not match, the fields will be returned in the missingFields list with a low confidence score.
Can I use this with Cursor or Claude Desktop?
Yes. You can connect this MCP server to Cursor, VS Code, Claude Desktop, Windsurf, and any other MCP-compatible client via Vinkius Edge.
One connection away
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