String Similarity Batch Connector for AI agents.
2 live capabilities
Perform high-precision fuzzy matching and string comparisons on large text datasets.
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Why people use String Similarity Batch
Stop Hallucinating String Matches with String Similarity Batch Fuzzy Matching
This Connector changes that by giving your agent a calculator. Instead of guessing, your agent calls batch_rank_candidates to get a sorted list of matches based on actual character distance. You get a reliable, sorted list of results every time, letting you automate the cleanup without second-guessing the output.
What Vinkius changes
You get mathematically accurate string comparisons without the hallucination risks of an LLM.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
Deduplicating a messy CSV
An agent takes a list of 10,000 customer names and identifies duplicates using batch_rank_candidates to find and group similar entries.
- Real-world use case 02
Search typo correction
A user types "Appel" into a search bar, and the agent uses compute_single_metric to find the closest match, "Apple".
- Real-world use case 03
Product catalog mapping
An agent takes a vendor's messy product list and matches them to your internal IDs using Jaro-Winkler similarity scores.
Complete set · 2capabilities
The complete String Similarity Batch capability set.
These are the exact actions your AI can choose when you ask it to work with String Similarity Batch.
01—02
2 capabilities in this set.
Part of 2 available through String Similarity Batch.
- 01 Capability
Batch rank candidates
Rank a list of strings against a target using metrics like Jaro-Winkler or Sorensen-Dice. This is useful for finding the best match in a large dataset.
- 02 Capability
Compute single metric
Get a specific similarity score between two strings using algorithms like Levenshtein or LCS. Use this for one-to-one comparisons.
Set up in minutes
One URL. Then ask String Similarity Batch to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use String Similarity Batch 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_2bdBBIz3zwAoWQWPkFs0dCarqz9V041FolEup60x/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 String Similarity Batch, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable String Similarity Batch for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_2bdBBIz3zwAoWQWPkFs0dCarqz9V041FolEup60x/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 String Similarity Batch URL.
- Step 03
Save and start
Save the connection and enable String Similarity Batch in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"string-similarity-batch": {
"url": "https://edge.vinkius.com/vk_preview_2bdBBIz3zwAoWQWPkFs0dCarqz9V041FolEup60x/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 String Similarity Batch
Open Agent mode in chat and ask: "Using String Similarity Batch, help me...". 2 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"string-similarity-batch": {
"url": "https://edge.vinkius.com/vk_preview_2bdBBIz3zwAoWQWPkFs0dCarqz9V041FolEup60x/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 String Similarity Batch
Ask Copilot: "Using String Similarity Batch, help me...". 2 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"string-similarity-batch": {
"url": "https://edge.vinkius.com/vk_preview_2bdBBIz3zwAoWQWPkFs0dCarqz9V041FolEup60x/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 String Similarity Batch
Open Cascade and ask: "Using String Similarity Batch, help me...". 2 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"string-similarity-batch": {
"url": "https://edge.vinkius.com/vk_preview_2bdBBIz3zwAoWQWPkFs0dCarqz9V041FolEup60x/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 String Similarity Batch
Ask Cline: "Using String Similarity Batch, help me...". 2 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add string-similarity-batch --transport http "https://edge.vinkius.com/vk_preview_2bdBBIz3zwAoWQWPkFs0dCarqz9V041FolEup60x/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 String Similarity Batch
Ask Claude: "Using String Similarity Batch, show me...". 2 tools are ready
Where the request belongs
Work String Similarity Batch can move forward.
This is for the data scientist tired of manual deduplication, the backend engineer building a search feature that handles typos, and the product manager trying to map messy vendor catalogs into a clean internal database.
Data Scientist
Cleaning up a messy CSV of 10,000 customer names to find and merge duplicates.
Backend Engineer
Building a product search bar that needs to suggest the right item even when users misspell it.
Product Manager
Mapping thousands of messy vendor product names to a single unified internal SKU list.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse Connectors
Exact Levenshtein Distance Calculator
Compute precise edit distances and string similarity scores.
Exact Levenshtein Distance Calculator Alternative
Compute precise edit distances and string similarity scores.
Embedding Similarity Calculator
Calculate mathematical distances and similarity scores between multidimensional numerical vectors.
Diff & Patch Calculator
Compute line-level and word-level diffs, generate unified patches, and calculate text similarity.
Levenshtein Distance Engine
Calculate the exact edit distance between two strings. Essential for fuzzy matching, spell checking, and deduplication. Stop LLMs from guessing string similarity.
Fuzzy String Distance Engine
Calculate exact Levenshtein, Jaro-Winkler, and Dice distances for fuzzy text matching natively local.
Bring your own AI
Change the model, client or framework. Keep String Similarity Batch 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 String Similarity Batch.
The practical details behind the request, access and result.
Can String Similarity Batch handle large datasets?
Yes, it is specifically built for batch processing. You can rank thousands of strings against a target in one go, making it much faster than individual queries.
What's the difference between Levenshtein and Jaro-Winkler?
Levenshtein counts the number of edits needed to turn one string into another. Jaro-Winkler gives more weight to prefix matches, which is often better for comparing human names.
Does it work for very long text?
It supports strings up to 5,000 characters. This is significantly larger than what you can reliably include in a standard prompt without losing context or hitting limits.
Can I use it to find typos in user input?
That is a perfect use case. Your agent can use the similarity metrics to see if a user's typo is close enough to a real product name to suggest a correction.
Is it faster than just asking the AI?
For large lists, yes. It is faster and more accurate because it runs dedicated math rather than asking the LLM to 'think' about the similarity of every single item.
Does it support different types of matching?
Yes, it includes several algorithms like Sorensen-Dice and Damerau-Levenshtein to suit different types of fuzzy matching needs, from simple typos to structural differences.
Can I use this for large datasets?
Yes, the batch_rank_canditates capability is designed to process arrays of candidates efficiently.
What is the maximum string length?
Each individual string can be up to 5000 characters.
Does it support Jaro-Winkler?
Yes, jaro_winkler is one of the supported metric types.
One connection away
Give your agent a direct line to String Similarity Batch.
Connect String Similarity Batch once. Keep it beside 6,100+ managed Connectors when the next task needs more.
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