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google-sheet-batch-assistant-mcp
Advanced tools
MCP server for efficient batch processing of Google Sheets data with API quota management
An MCP (Model Context Protocol) server that enables AI agents to efficiently read and write Google Spreadsheets data. This library is designed for batch processing by AI agents, providing management of task lists, result data, and progress information through spreadsheets.
npm install -g google-sheet-batch-assistant-mcp
Or build locally:
git clone https://github.com/ideamans/node-google-sheet-batch-assistant-mcp.git
cd node-google-sheet-batch-assistant-mcp
yarn install
yarn build
npm link
google-sheet-batch-assistant-mcp <spreadsheetId> <sheetName> [options]
--service-account <path>
: Path to service account JSON file (default: ./service-account.json)--log-file <path>
: Path to log file (default: ./google-sheet-batch-assistant-mcp.log)--read-interval <ms>
: Read interval in milliseconds (default: 5000)--batch-interval <ms>
: Batch update interval in milliseconds (default: 5000)--key, -k <keyColumn>
: Key column name or letter (e.g., 'id' or 'A') (default: A)--header, -h <headerRow>
: Header row number (1-based) (default: 1)// Configure settings
await client.callTool("configure", {
keyColumn: "A",
headerRow: 1,
});
// Query data
const result = await client.callTool("query", {
conditions: [["status", "==", "pending"]],
limit: 10,
});
// Get data by key
const data = await client.callTool("get", { key: "item001" });
// Update data (batched)
await client.callTool("update", {
key: "item001",
column: "status",
value: "completed",
});
// Immediate update
await client.callTool("flush", {
key: "item001",
column: "lock",
value: "agent1",
});
// Append value
await client.callTool("append_value", {
key: "item001",
column: "history",
value: "2025-01-15: Process completed",
separator: "\\n",
});
git clone https://github.com/ideamans/node-google-sheet-batch-assistant-mcp.git
cd node-google-sheet-batch-assistant-mcp
yarn install
Create a Service Account in GCP
Prepare Test Spreadsheet
xxxx@xxxx.iam.gserviceaccount.com
) as an editor/d/
and /edit
in the URL)Configure Credentials
# Save service account key
cp ~/Downloads/your-service-account-key.json ./service-account.json
# Create environment file
cp .env.example .env
# Edit .env and set TEST_SHEET_ID
# TEST_SHEET_ID=your-spreadsheet-id-here
Run Tests
# Build
yarn build
# Unit tests
yarn test
# Integration tests
yarn test:integration
Configure the following in GitHub repository Settings > Secrets and variables > Actions:
testing
)Prepare .mcp.json
cp .mcp.json.example .mcp.json
Edit .mcp.json
{
"mcpServers": {
"google-sheet-batch-assistant": {
"command": "node",
"args": ["dist/index.js", "your-spreadsheet-id-here", "live"]
}
}
}
Test with Claude
# Start MCP server and connect from Claude
claude --mcp-config .mcp.json
Add the following to your Claude Desktop MCP settings:
{
"mcpServers": {
"backlog": {
"command": "npx",
"args": [
"-y",
"google-sheet-batch-assistant-mcp",
"<sheetId>",
"<sheetName>"
]
}
}
}
Replace <sheetId>
with your Google Spreadsheet ID and <sheetName>
with the target sheet name.
You can add more options to the args array:
{
"mcpServers": {
"backlog": {
"command": "npx",
"args": [
"-y",
"google-sheet-batch-assistant-mcp",
"<sheetId>",
"<sheetName>",
"--service-account",
"/path/to/service-account.json",
"--read-interval",
"3000",
"--batch-interval",
"3000",
"--key",
"task_id",
"--header",
"2"
]
}
}
}
yarn build
# Type checking
yarn typecheck
# Linting
yarn lint
MIT
FAQs
MCP server for efficient batch processing of Google Sheets data with API quota management
The npm package google-sheet-batch-assistant-mcp receives a total of 2 weekly downloads. As such, google-sheet-batch-assistant-mcp popularity was classified as not popular.
We found that google-sheet-batch-assistant-mcp demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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