Integrate with Smartsheet for project management and data analytics, requiring an API access token.
A Model Context Protocol (MCP) server that provides seamless integration with Smartsheet, enabling automated operations on Smartsheet documents through a standardized interface. This server bridges the gap between AI-powered automation tools and Smartsheet's powerful collaboration platform.
The Smartsheet MCP Server is designed to facilitate intelligent interactions with Smartsheet, providing a robust set of tools for document management, data operations, and column customization. It serves as a critical component in automated workflows, enabling AI systems to programmatically interact with Smartsheet data while maintaining data integrity and enforcing business rules.
Clinical Research Analytics
Hospital Operations
Healthcare Innovation
Automated Document Management
Data Operations
System Integration
The server integrates with:
get_column_map (Read)
get_sheet_info (Read - Alias)
get_column_map providing identical functionalitysmartsheet_write (Create)
smartsheet_update (Update)
smartsheet_delete (Delete)
smartsheet_search (Search)
smartsheet_add_column (Column Management)
smartsheet_delete_column (Column Management)
smartsheet_rename_column (Column Management)
smartsheet_bulk_update (Conditional Updates)
get_all_row_ids (Utility)
start_batch_analysis (Healthcare Analytics)
get_job_status (Analysis Monitoring)
cancel_batch_analysis (Job Control)
list_workspaces (Workspace Management)
get_workspace (Workspace Management)
create_workspace (Workspace Management)
create_sheet_in_workspace (Workspace Management)
list_workspace_sheets (Workspace Management)
The server provides both static resources and dynamic resource templates for enhanced data access and contextual information.
smartsheet://templates/project-plan - Project Plan Template
smartsheet://templates/task-tracker - Task Tracker Template
smartsheet://schemas/column-types - Column Types Reference
smartsheet://best-practices/formulas - Formula Best Practices
smartsheet://{sheet_id}/summary - Sheet Summary
smartsheet://{sheet_id}/gantt-data - Gantt Chart Data
smartsheet://{workspace_id}/overview - Workspace Overview
smartsheet://{sheet_id}/dependencies - Dependency Map
smartsheet://{sheet_id}/health-report - Sheet Health Report
Intelligent prompt templates that provide guided assistance for common Smartsheet operations and analysis.
create_project_plan - Project Plan Creation Guide
analyze_project_status - Project Health Analysis
optimize_workflow - Workflow Optimization
generate_insights - Data Insights Extraction
create_dashboard_summary - Executive Dashboard Creation
setup_conditional_formatting - Conditional Formatting Guide
Column Type Management
Data Validation
Search Functionality
Metadata Handling
Healthcare Analytics
Batch Processing
Job Management
conda create -n cline_mcp_env python=3.12 nodejs -y
conda activate cline_mcp_env
npm install
cd smartsheet_ops
pip install -e .
cd ..
Note: The Python package includes dependencies for:
smartsheet-python-sdk - Smartsheet API clientpython-dotenv - Environment variable managementopenai - Azure OpenAI integrationtiktoken - Token counting for AI analysisnpm run build
The server supports two transport modes:
The configuration path depends on your operating system:
macOS:
~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json
Windows:
%APPDATA%\Code\User\globalStorage\saoudrizwan.claude-dev\settings\cline_mcp_settings.json
Linux:
~/.config/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json
{
"mcpServers": {
"smartsheet": {
"command": "/Users/[username]/anaconda3/envs/cline_mcp_env/bin/node",
"args": [
"/path/to/smartsheet-server/build/index.js",
"--transport",
"stdio"
],
"env": {
"PYTHON_PATH": "/Users/[username]/anaconda3/envs/cline_mcp_env/bin/python3",
"SMARTSHEET_API_KEY": "your-api-key",
"AZURE_OPENAI_API_KEY": "your-azure-openai-key",
"AZURE_OPENAI_API_BASE": "your-azure-openai-endpoint",
"AZURE_OPENAI_API_VERSION": "your-api-version",
"AZURE_OPENAI_DEPLOYMENT": "your-deployment-name"
},
"disabled": false,
"autoApprove": [
"get_column_map",
"smartsheet_write",
"smartsheet_update",
"smartsheet_delete",
"smartsheet_search",
"smartsheet_add_column",
"smartsheet_delete_column",
"smartsheet_rename_column",
"smartsheet_bulk_update",
"start_batch_analysis",
"get_job_status",
"cancel_batch_analysis",
"get_all_row_ids",
"list_workspaces",
"get_workspace",
"create_workspace",
"create_sheet_in_workspace",
"list_workspace_sheets"
]
}
}
}
For web-based MCP clients or network access, use the HTTP transport mode:
Start the server:
# Start with default port (3000)
SMARTSHEET_API_KEY=your-api-key PYTHON_PATH=/path/to/python smartsheet-server --transport http
# Start with custom port
SMARTSHEET_API_KEY=your-api-key PYTHON_PATH=/path/to/python smartsheet-server --transport http --port 8080
Client Configuration:
{
"mcpServers": {
"smartsheet-server": {
"type": "http",
"url": "http://localhost:3000/mcp",
"headers": {
"Authorization": "Bearer your-optional-auth-token"
}
}
}
}
Health Check:
The HTTP server provides a health check endpoint:
curl http://localhost:3000/health
# Response: {"status":"ok","server":"smartsheet-mcp"}
The server will start automatically when Cline or Claude Desktop needs it. However, you can also start it manually for testing.
macOS/Linux:
# Activate the environment
conda activate cline_mcp_env
# Start with STDIO transport (default)
PYTHON_PATH=/Users/[username]/anaconda3/envs/cline_mcp_env/bin/python3 SMARTSHEET_API_KEY=your-api-key node build/index.js
# Or explicitly specify STDIO transport
PYTHON_PATH=/Users/[username]/anaconda3/envs/cline_mcp_env/bin/python3 SMARTSHEET_API_KEY=your-api-key node build/index.js --transport stdio
Windows:
:: Activate the environment
conda activate cline_mcp_env
:: Start with STDIO transport
set PYTHON_PATH=C:\Users\[username]\anaconda3\envs\cline_mcp_env\python.exe
set SMARTSHEET_API_KEY=your-api-key
node build\index.js --transport stdio
For web-based clients or network access:
macOS/Linux:
# Activate the environment
conda activate cline_mcp_env
# Start HTTP server on default port (3000)
PYTHON_PATH=/Users/[username]/anaconda3/envs/cline_mcp_env/bin/python3 SMARTSHEET_API_KEY=your-api-key node build/index.js --transport http
# Start HTTP server on custom port
PYTHON_PATH=/Users/[username]/anaconda3/envs/cline_mcp_env/bin/python3 SMARTSHEET_API_KEY=your-api-key node build/index.js --transport http --port 8080
Windows:
:: Activate the environment
conda activate cline_mcp_env
:: Start HTTP server
set PYTHON_PATH=C:\Users\[username]\anaconda3\envs\cline_mcp_env\python.exe
set SMARTSHEET_API_KEY=your-api-key
node build\index.js --transport http --port 3000
# View help
node build/index.js --help
# Available options:
--transport <type> # "stdio" (default) or "http"
--port <number> # HTTP port (default: 3000, only used with --transport http)
--help, -h # Show help message
curl http://localhost:3000/health{"status":"ok","server":"smartsheet-mcp"}Check the Python environment has the required packages installed:
conda activate cline_mcp_env
pip show smartsheet-python-sdk openai tiktoken python-dotenv
The Python package should include these key dependencies:
smartsheet-python-sdk>=2.105.1 - Smartsheet API clientopenai>=1.0.0 - Azure OpenAI integrationtiktoken>=0.5.0 - Token counting for AI analysispython-dotenv>=1.0.0 - Environment variable management// Get column mapping and sample data
const result = await use_mcp_tool({
server_name: "smartsheet",
tool_name: "get_column_map",
arguments: {
sheet_id: "your-sheet-id",
},
});
// Write new rows to Smartsheet
const result = await use_mcp_tool({
server_name: "smartsheet",
tool_name: "smartsheet_write",
arguments: {
sheet_id: "your-sheet-id",
column_map: {
"Column 1": "1234567890",
"Column 2": "0987654321",
},
row_data: [
{
"Column 1": "Value 1",
"Column 2": "Value 2",
},
],
},
});
// Basic text search
const result = await use_mcp_tool({
server_name: "smartsheet",
tool_name: "smartsheet_search",
arguments: {
sheet_id: "your-sheet-id",
pattern: "search text",
options: {
case_sensitive: false,
whole_word: false,
columns: ["Column1", "Column2"], // Optional: limit search to specific columns
},
},
});
// Search PICKLIST column with exact matching
const result = await use_mcp_tool({
server_name: "smartsheet",
tool_name: "smartsheet_search",
arguments: {
sheet_id: "your-sheet-id",
pattern: "In Progress",
options: {
columns: ["Status"], // PICKLIST column
case_sensitive: true,
whole_word: true,
},
},
});
// Update existing rows
const result = await use_mcp_tool({
server_name: "smartsheet",
tool_name: "smartsheet_update",
arguments: {
sheet_id: "your-sheet-id",
column_map: {
Status: "850892021780356",
Notes: "6861293012340612",
},
updates: [
{
row_id: "7670198317295492",
data: {
Status: "In Progress",
Notes: "Updated via MCP server",
},
},
],
},
});
// Delete rows from Smartsheet
const result = await use_mcp_tool({
server_name: "smartsheet",
tool_name: "smartsheet_delete",
arguments: {
sheet_id: "your-sheet-id",
row_ids: ["7670198317295492", "7670198317295493"],
},
});
// Example 1: Pediatric Innovation Scoring
const result = await use_mcp_tool({
server_name: "smartsheet",
tool_name: "start_batch_analysis",
arguments: {
sheet_id: "your-sheet-id",
type: "custom",
sourceColumns: ["Ideas", "Implementation_Details"],
targetColumn: "Pediatric_Score",
rowIds: ["row1", "row2", "row3"], // Optional: specify rows, or omit for all rows
customGoal:
"Score each innovation 1-100 based on pediatric healthcare impact. Consider: 1) Direct benefit to child patients, 2) Integration with pediatric workflows, 3) Implementation feasibility in children's hospital, 4) Safety considerations for pediatric use. Return only a number.",
},
});
// Example 2: Clinical Note Summarization
const result = await use_mcp_tool({
server_name: "smartsheet",
tool_name: "start_batch_analysis",
arguments: {
sheet_id: "your-sheet-id",
type: "summarize",
sourceColumns: ["Clinical_Notes"],
targetColumn: "Note_Summary",
rowIds: ["row1", "row2"], // Optional: specify rows, or omit for all rows
},
});
// Example 3: Patient Satisfaction Analysis
const result = await use_mcp_tool({
server_name: "smartsheet",
tool_name: "start_batch_analysis",
arguments: {
sheet_id: "your-sheet-id",
type: "sentiment",
sourceColumns: ["Patient_Feedback"],
targetColumn: "Satisfaction_Score",
rowIds: ["row1", "row2"], // Optional: specify rows, or omit for all rows
},
});
// Example 4: Get All Row IDs for Batch Processing
const allRows = await use_mcp_tool({
server_name: "smartsheet",
tool_name: "get_all_row_ids",
arguments: {
sheet_id: "your-sheet-id",
},
});
// Example 5: Monitor Analysis Job Progress
const jobStatus = await use_mcp_tool({
server_name: "smartsheet",
tool_name: "get_job_status",
arguments: {
sheet_id: "your-sheet-id",
jobId: "job-uuid-from-start-analysis",
},
});
// List all accessible workspaces
const workspaces = await use_mcp_tool({
server_name: "smartsheet",
tool_name: "list_workspaces",
arguments: {},
});
// Get details of a specific workspace
const workspace = await use_mcp_tool({
server_name: "smartsheet",
tool_name: "get_workspace",
arguments: {
workspace_id: "6621332407379844",
},
});
// Create a new workspace
const newWorkspace = await use_mcp_tool({
server_name: "smartsheet",
tool_name: "create_workspace",
arguments: {
name: "Project Management",
},
});
// Create a sheet in a workspace
const newSheet = await use_mcp_tool({
server_name: "smartsheet",
tool_name: "create_sheet_in_workspace",
arguments: {
workspace_id: "6621332407379844",
name: "Task Tracker",
columns: [
{ title: "Task Name", type: "TEXT_NUMBER" },
{ title: "Due Date", type: "DATE" },
{
title: "Status",
type: "PICKLIST",
options: ["Not Started", "In Progress", "Completed"],
},
],
},
});
// List all sheets in a workspace
const sheets = await use_mcp_tool({
server_name: "smartsheet",
tool_name: "list_workspace_sheets",
arguments: {
workspace_id: "6621332407379844",
},
});
// Access static resources
const projectTemplate = await access_mcp_resource({
server_name: "smartsheet",
uri: "smartsheet://templates/project-plan",
});
const columnTypes = await access_mcp_resource({
server_name: "smartsheet",
uri: "smartsheet://schemas/column-types",
});
const formulaGuide = await access_mcp_resource({
server_name: "smartsheet",
uri: "smartsheet://best-practices/formulas",
});
// Access dynamic resources
const sheetSummary = await access_mcp_resource({
server_name: "smartsheet",
uri: "smartsheet://8596778555232132/summary",
});
const ganttData = await access_mcp_resource({
server_name: "smartsheet",
uri: "smartsheet://8596778555232132/gantt-data",
});
const workspaceOverview = await access_mcp_resource({
server_name: "smartsheet",
uri: "smartsheet://6621332407379844/overview",
});
const dependencyMap = await access_mcp_resource({
server_name: "smartsheet",
uri: "smartsheet://8596778555232132/dependencies",
});
const healthReport = await access_mcp_resource({
server_name: "smartsheet",
uri: "smartsheet://8596778555232132/health-report",
});
// Project plan creation guidance
const projectPlanPrompt = await use_mcp_tool({
server_name: "smartsheet",
tool_name: "get_prompt",
arguments: {
name: "create_project_plan",
arguments: {
project_name: "Website Redesign",
project_type: "software",
duration_estimate: "3 months",
},
},
});
// Project health analysis
const analysisPrompt = await use_mcp_tool({
server_name: "smartsheet",
tool_name: "get_prompt",
arguments: {
name: "analyze_project_status",
arguments: {
sheet_id: "8596778555232132",
focus_area: "timeline",
},
},
});
// Workflow optimization suggestions
const optimizationPrompt = await use_mcp_tool({
server_name: "smartsheet",
tool_name: "get_prompt",
arguments: {
name: "optimize_workflow",
arguments: {
sheet_id: "8596778555232132",
workflow_type: "approval",
},
},
});
// Data insights extraction
const insightsPrompt = await use_mcp_tool({
server_name: "smartsheet",
tool_name: "get_prompt",
arguments: {
name: "generate_insights",
arguments: {
sheet_id: "8596778555232132",
insight_type: "bottlenecks",
},
},
});
// Executive dashboard creation
const dashboardPrompt = await use_mcp_tool({
server_name: "smartsheet",
tool_name: "get_prompt",
arguments: {
name: "create_dashboard_summary",
arguments: {
workspace_id: "6621332407379844",
summary_focus: "risks",
},
},
});
// Conditional formatting setup
const formattingPrompt = await use_mcp_tool({
server_name: "smartsheet",
tool_name: "get_prompt",
arguments: {
name: "setup_conditional_formatting",
arguments: {
sheet_id: "8596778555232132",
formatting_goal: "status indicators",
},
},
});
For development with auto-rebuild:
npm run watch
Since MCP servers communicate over stdio, debugging can be challenging. The server implements comprehensive error logging and provides detailed error messages through the MCP protocol.
Key debugging features:
The server implements a multi-layer error handling approach:
MCP Layer
CLI Layer
Operations Layer
Contributions are welcome! Please ensure:
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