How to Use the Fuzzy Match Search MCP in Cursor

Q: Can I run Fuzzy Match Search on my local codebase inside Cursor?

Yes. You can register the server in your project's .cursor/mcp.json file. This lets your Cursor agent scan your local directory, build a target array, and run fuzzy matching on demand.

Q: Does Fuzzy Match Search support custom similarity thresholds in Cursor?

The fuzzymatch tool returns similarity scores for every target string. Your Cursor agent uses these scores to filter out low-confidence matches, ensuring only the most relevant code snippets are suggested.

Q: How do I install this MCP Server for a specific project?

Create a .cursor/mcp.json file in your project root and add the server config. Cursor will automatically detect the server and enable the tool for any agent chats initiated within that workspace.

Keep your coding flow unbroken by using Fuzzy Match Search to find files and variables in Cursor.

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Connect Fuzzy Match Search MCP to Cursor

Create your Vinkius account to connect Fuzzy Match Search to Cursor and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Fuzzy Match Search with Cursor

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Find variables and functions with Fuzzy Match Search in Cursor

When you are writing code in Cursor, spelling a function name wrong halts your agent's progress. The `fuzzy_match` tool lets your editor's Agent mode search your active codebase arrays and resolve typos instantly. The tool returns a list of ranked string matches based on similarity. Your agent can then inject the correct variable name directly into your active file, preventing compilation errors before you even hit save.

Match API endpoints and schema fields during code generation

Generating code that integrates with complex APIs often fails due to slight naming mismatches. This MCP Server lets Cursor match your draft code against a schema array using the `fuzzy_match` tool. Instead of hardcoding incorrect paths, the editor queries the local server to find the closest matching endpoint. You get working code that uses real, verified API keys and variables without manual lookup.

Speed up file searches in massive workspaces

Large codebases slow down standard search tools. By adding this MCP Server to your ~/.cursor/mcp.json, you give your editor a dedicated engine for fast string matching. The agent runs `fuzzy_match` over your file tree array to locate deeply nested files. It bypasses slow file system indexers, returning the correct path to Cursor in milliseconds.

Setup guide

Set up Fuzzy Match Search MCP in Cursor

Prerequisites

Cursor installed (macOS, Windows, or Linux)
Active Vinkius subscription with a valid endpoint token

1

Open MCP Settings

Go to Cursor Settings → MCP or open the Command Palette (Cmd+Shift+P / Ctrl+Shift+P) and search for "MCP: Add Server".
2

Add the Fuzzy Match Search MCP

Cursor will create or open .cursor/mcp.json in your project root. Paste the JSON snippet on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Enable Agent mode

Open Composer (Cmd+I / Ctrl+I) and switch to Agent mode using the dropdown at the top. MCP tools are only available in Agent mode.
4

Verify the connection

Ask Cursor something like "List my recent Fuzzy Match Search transactions." If the MCP tools are loaded correctly, Cursor will call the Fuzzy Match Search tools automatically. You can also check Settings → MCP for a green status indicator.

.cursor/mcp.json

{
  "mcpServers": {
    "fuzzy-match-search-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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Common questions about Fuzzy Match Search MCP in Cursor

It prevents your agent from writing broken code with misspelled variable names. By calling the `fuzzy_match` tool, Cursor verifies your draft code against actual codebase strings and corrects typos before writing files.

Yes. You can register the server in your project's `.cursor/mcp.json` file. This lets your Cursor agent scan your local directory, build a target array, and run fuzzy matching on demand.

The `fuzzy_match` tool returns similarity scores for every target string. Your Cursor agent uses these scores to filter out low-confidence matches, ensuring only the most relevant code snippets are suggested.

Create a `.cursor/mcp.json` file in your project root and add the server config. Cursor will automatically detect the server and enable the tool for any agent chats initiated within that workspace.

Yes, your code arrays and queries are processed within a secure local sandbox. This MCP Server does not transmit your private source code or string variables to third-party APIs for processing.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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