Loop MCP Server

An MCP (Model Context Protocol) server that enables LLMs to process arrays item by item with a specific task.

Overview

This MCP server provides tools for:

• Initializing an array with a task description
• Fetching items one by one or in batches for processing
• Storing results for each processed item or batch
• Retrieving all results (only after all items are processed)
• Optional result summarization
• Configurable batch size for efficient processing

Installation

npm install

Usage

Running the Server

npm start

Available Tools

1. initialize_array - Set up the array and task

   • array: The array of items to process
   • task: Description of what to do with each item
   • batchSize (optional): Number of items to process in each batch (default: 1)

2. get_next_item - Get the next item to process

   • Returns: Current item, index, task, and remaining count

3. get_next_batch - Get the next batch of items based on batch size

   • Returns: Array of items, indices, task, and remaining count

4. store_result - Store the result of processing

   • result: The processing result (single value or array for batch processing)

5. get_all_results - Get all results after completion

   • summarize (optional): Include a summary
   • Note: This will error if processing is not complete

6. reset - Clear the current processing state

Example Workflows

Single Item Processing

// 1. Initialize
await callTool('initialize_array', {
  array: [1, 2, 3, 4, 5],
  task: 'Square each number'
});

// 2. Process each item
while (true) {
  const item = await callTool('get_next_item');
  if (item.text === 'All items have been processed.') break;
  
  // Process the item (e.g., square it)
  const result = item.value * item.value;
  
  await callTool('store_result', { result });
}

// 3. Get final results
const results = await callTool('get_all_results', { summarize: true });

Batch Processing

// 1. Initialize with batch size
await callTool('initialize_array', {
  array: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
  task: 'Double each number',
  batchSize: 3
});

// 2. Process in batches
while (true) {
  const batch = await callTool('get_next_batch');
  if (batch.text === 'All items have been processed.') break;
  
  // Process the batch
  const results = batch.items.map(item => item * 2);
  
  await callTool('store_result', { result: results });
}

// 3. Get final results
const results = await callTool('get_all_results', { summarize: true });

Running the Example

node example-client.js

Integration with Claude Desktop

Add to your Claude Desktop configuration:

{
  "mcpServers": {
    "loop-processor": {
      "command": "node",
      "args": ["/path/to/loop_mcp/server.js"]
    }
  }
}