How do I install Smartsheet Server?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Smartsheet Server need an API key?

No required environment variable is listed on this record.

What transport does Smartsheet Server use?

Smartsheet Server runs locally over stdio.

When was Smartsheet Server last checked?

Last checked 2026-10-28. MCP server providing healthcare analytics capabilities for Smartsheet, including clinical note summarization, patient feedback analysis, and research impact assessment

Smartsheet Server MCP-Server

Smartsheet MCP Server

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.

Overview

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.

Key Benefits

Intelligent Integration: Seamlessly connects AI systems with Smartsheet's collaboration platform
Data Integrity: Enforces validation rules and maintains referential integrity across operations
Formula Management: Preserves and updates formula references automatically
Flexible Configuration: Supports various column types and complex data structures
Error Resilience: Implements comprehensive error handling and validation at multiple layers
Healthcare Analytics: Specialized analysis capabilities for clinical and research data
Batch Processing: Efficient handling of large healthcare datasets
Custom Scoring: Flexible scoring systems for healthcare initiatives and research

Use Cases

Clinical Research Analytics
- Protocol compliance scoring
- Patient data analysis
- Research impact assessment
- Clinical trial data processing
- Automated research note summarization
Hospital Operations
- Resource utilization analysis
- Patient satisfaction scoring
- Department efficiency metrics
- Staff performance analytics
- Quality metrics tracking
Healthcare Innovation
- Pediatric alignment scoring
- Innovation impact assessment
- Research prioritization
- Implementation feasibility analysis
- Clinical value assessment
Automated Document Management
- Programmatic sheet structure modifications
- Dynamic column creation and management
- Automated data validation and formatting
Data Operations
- Bulk data updates with integrity checks
- Intelligent duplicate detection
- Formula-aware modifications
System Integration
- AI-driven sheet customization
- Automated reporting workflows
- Cross-system data synchronization

Integration Points

The server integrates with:

Smartsheet API for data operations
MCP protocol for standardized communication
Local development tools via stdio interface
Monitoring systems through structured logging

Features

Tools (34 Available)

get_column_map (Read)
- Retrieves column mapping and sample data from a Smartsheet
- Provides detailed column metadata including:
  - Column types (system columns, formulas, picklists)
  - Validation rules
  - Format specifications
  - Auto-number configurations
- Returns sample data for context
- Includes usage examples for writing data
get_sheet_info (Read - Alias)
- Alias for get_column_map providing identical functionality
- Maintains backward compatibility with existing integrations
smartsheet_write (Create)
- Writes new rows to Smartsheet with intelligent handling of:
  - System-managed columns
  - Multi-select picklist values
  - Formula-based columns
- Implements automatic duplicate detection
- Appends new rows to the bottom of the sheet (after existing entries)
- Returns detailed operation results including row IDs
smartsheet_update (Update)
- Updates existing rows in a Smartsheet
- Supports partial updates (modify specific fields)
- Maintains data integrity with validation
- Handles multi-select fields consistently
- Returns success/failure details per row
smartsheet_delete (Delete)
- Deletes rows from a Smartsheet
- Supports batch deletion of multiple rows
- Validates row existence and permissions
- Returns detailed operation results
smartsheet_search (Search)
- Performs advanced search across sheets
- Supports multiple search modes:
  - Text search with regex support
  - Exact value matching for PICKLIST columns
  - Case-sensitive and whole word options
- Column-specific search capabilities
- Returns:
  - Matched row IDs (primary result)
  - Detailed match information
  - Search metadata and statistics
smartsheet_add_column (Column Management)
- Adds new columns to a Smartsheet
- Supports all column types:

Resources (4 Static + 5 Dynamic Templates)

The server provides both static resources and dynamic resource templates for enhanced data access and contextual information.

Static Resources

smartsheet://templates/project-plan - Project Plan Template
- Pre-built project plan template with best practices
- Includes optimal column structure for task management
- Provides guidance on dependencies and resource allocation
smartsheet://templates/task-tracker - Task Tracker Template
- Simple task tracking template for team collaboration
- Focused on progress monitoring without complex dependencies
- Ideal for agile teams and simple workflows
smartsheet://schemas/column-types - Column Types Reference
- Complete reference of all supported Smartsheet column types
- Includes API support level for each type (full, limited, read-only)
- Essential for understanding column capabilities and limitations
smartsheet://best-practices/formulas - Formula Best Practices
- Common formula patterns and calculation examples
- Best practices for performance and maintainability
- Cross-sheet reference guidance

Dynamic Resource Templates

smartsheet://{sheet_id}/summary - Sheet Summary
- Auto-generated summary with key metrics and health status
- Progress indicators and completion statistics
- Real-time analysis of sheet data
smartsheet://{sheet_id}/gantt-data - Gantt Chart Data
- Standardized Gantt chart data format for visualization
- Timeline data optimized for project management tools
- Dependency relationships and critical path information
smartsheet://{workspace_id}/overview - Workspace Overview
- Comprehensive overview of workspace contents
- All sheets, reports, and dashboards in structured format
- Access levels and organizational hierarchy
smartsheet://{sheet_id}/dependencies - Dependency Map
- Visual dependency mapping for project sheets
- Task relationships and critical path analysis
- Bottleneck identification and optimization suggestions
smartsheet://{sheet_id}/health-report - Sheet Health Report
- Health analysis identifying data quality issues
- Missing data detection and broken formula identification
- Optimization opportunities and recommendations

Prompts (6 Available)

Intelligent prompt templates that provide guided assistance for common Smartsheet operations and analysis.

create_project_plan - Project Plan Creation Guide
- Guided project plan creation with best practices
- Template suggestions based on project type and duration
- Work breakdown structure recommendations
analyze_project_status - Project Health Analysis
- Comprehensive project health analysis with recommendations
- Timeline adherence and resource utilization insights
- Risk identification and mitigation strategies
optimize_workflow - Workflow Optimization
- Suggestions for improving sheet structure and workflows
- Automation opportunities and efficiency improvements
- User experience enhancement recommendations
generate_insights - Data Insights Extraction
- Extract key insights and patterns from sheet data
- Trend analysis and anomaly detection
- Actionable intelligence and decision support
create_dashboard_summary - Executive Dashboard Creation
- Generate executive summaries from multiple sheets
- High-level KPI tracking and strategic insights
- Leadership-focused reporting and recommendations
setup_conditional_formatting - Conditional Formatting Guide
- Step-by-step conditional formatting setup
- Visual data representation best practices
- Status indicators and progress tracking configuration

Key Capabilities

Column Type Management
- Handles system column types (AUTO_NUMBER, CREATED_DATE, etc.)
- Supports formula parsing and dependency tracking
- Manages picklist options and multi-select values
- Comprehensive column operations (add, delete, rename)
- Formula reference preservation and updates
Data Validation
- Automatic duplicate detection
- Column type validation
- Data format verification
- Column dependency analysis
- Name uniqueness validation
Search Functionality
- Advanced search capabilities
- Type-aware searching:
  - Exact matching for PICKLIST values
  - Pattern matching for text fields
  - Numeric comparisons
- Configurable search options:
  - Case sensitivity
  - Whole word matching
  - Column filtering
- Comprehensive results:
  - Row IDs for matched rows
  - Detailed match context
  - Search statistics
Metadata Handling
- Extracts and processes column metadata
- Handles validation rules
- Manages format specifications
- Tracks formula dependencies
- Maintains column relationships
Healthcare Analytics
- Clinical note summarization using Azure OpenAI
- Patient feedback sentiment analysis
- Protocol compliance scoring
- Research impact assessment
- Resource utilization analysis
- Custom analysis with optimized prompt generation
Batch Processing
- Automatic row batching (3 rows per batch for optimal performance)
- Progress tracking and monitoring
- Error handling and recovery
- Customizable processing goals
- Multi-column analysis support
- Token-aware content chunking for large text
- Background job processing with ThreadPoolExecutor
Job Management
- Real-time status monitoring
- Detailed progress tracking
- Error reporting and logging
- Job cancellation support

Setup

Prerequisites

Node.js and npm
Conda (for environment management)
Smartsheet API access token
Azure OpenAI API access (for batch analysis features)

Environment Setup

Create a dedicated conda environment:

conda create -n cline_mcp_env python=3.12 nodejs -y
conda activate cline_mcp_env

Install Node.js dependencies:

npm install

Install Python dependencies:

cd smartsheet_ops
pip install -e .
cd ..

Note: The Python package includes dependencies for:

smartsheet-python-sdk - Smartsheet API client
python-dotenv - Environment variable management
openai - Azure OpenAI integration
tiktoken - Token counting for AI analysis

Build the TypeScript server:

npm run build

Configuration

The server supports two transport modes:

STDIO Transport (default): For local development and CLI usage
HTTP Transport: For web-based clients and network access

1. Get Your Smartsheet API Key

Log in to Smartsheet
Go to Account → Personal Settings → API Access
Generate a new access token

2. Configure for STDIO Transport (Cline/Local)

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"
      ]
    }
  }
}

3. Configure for HTTP Transport

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"}

Starting the Server

STDIO Transport (Default)

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

HTTP Transport

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

Command Line Options

# 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

Verifying Installation

STDIO Transport

The server should output "Smartsheet MCP server running on stdio" when started
Test the connection using any MCP tool (e.g., get_column_map)

HTTP Transport

The server should output "Smartsheet MCP server running on HTTP port 3000" when started
Test the health endpoint: curl http://localhost:3000/health
Expected response: {"status":"ok","server":"smartsheet-mcp"}

Python Environment

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 client
openai>=1.0.0 - Azure OpenAI integration
tiktoken>=0.5.0 - Token counting for AI analysis
python-dotenv>=1.0.0 - Environment variable management

Usage Examples

Getting Column Information (Read)

// 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",
  },
});

Writing Data (Create)

// 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",
      },
    ],
  },
});

Searching Data

// 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,
    },
  },
});

Updating Data (Update)

// 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",
        },
      },
    ],
  },
});

Deleting Data (Delete)

// 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"],
  },
});

Healthcare Analytics Examples

// 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",
  },
});

Workspace Management Examples

// 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",
  },
});

Resources Usage Examples

// 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",
});

Prompts Usage Examples

// 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",
    },
  },
});

Development

For development with auto-rebuild:

npm run watch

CI/CD Pipeline

This project implements a comprehensive 8-stage CI/CD pipeline with GitHub Actions, ensuring code quality, security, and reliability across all components.

Pipeline Architecture

The CI/CD pipeline consists of 8 coordinated jobs that run in parallel and sequence for optimal efficiency:

TypeScript Quality Checks - ESLint, type checking, formatting validation
Python Quality Checks - Black, Flake8, MyPy type checking
TypeScript Testing - Matrix testing on Node.js 16, 18, 20 with coverage
Python Testing - Matrix testing on Python 3.8, 3.9, 3.10, 3.11 with coverage
Combined Coverage - Unified coverage reporting and Codecov integration
Integration Testing - End-to-end validation and MCP server startup verification
Security Scanning - npm audit, Python safety, Bandit security analysis
Build and Package - Artifact creation and deployment verification

Key Pipeline Features

Quality Assurance:

Multi-language Support: Full TypeScript and Python pipeline coverage
Matrix Testing: Cross-platform compatibility verification
Code Quality Gates: ESLint, Black, Flake8, MyPy, TypeScript strict mode
Coverage Enforcement: Automated coverage threshold validation
Security Scanning: Regular vulnerability assessment with safety and Bandit

Performance Optimization:

Parallel Execution: Independent jobs run concurrently for faster feedback
Intelligent Caching: Node modules and Python dependencies cached across runs
Conditional Execution: Performance tests only on PRs, full coverage on main
Artifact Management: Build artifacts preserved for 7-30 days

Integration and Deployment:

MCP Protocol Validation: Server startup and protocol compliance testing
Docker Support: Multi-platform container builds (linux/amd64, linux/arm64)
Automated Releases: Version-tagged releases with changelog generation
Dependency Management: Weekly security audits and update automation

Workflow Triggers

# Comprehensive testing on main branches
- push: [main, develop]
- pull_request: [main, develop]

# Additional workflows
- release: version tags (v*.*.*)
- security: weekly dependency scans
- performance: PR-specific testing

Status Monitoring

The pipeline provides comprehensive notifications and artifact management, ensuring all stakeholders have visibility into build status, test results, and deployment readiness.

Testing and Quality Assurance

This project maintains comprehensive test coverage and quality assurance across both TypeScript and Python components with automated CI/CD pipelines.

Test Infrastructure

Test Status: 54/54 TypeScript tests passing, 5/5 Python tests passing

Our comprehensive testing strategy includes:

Unit Tests: Jest for TypeScript (54 tests), pytest for Python (5 core tests)
Integration Tests: Cross-component testing and MCP protocol validation
Code Quality: ESLint, TypeScript checking, Black, Flake8, MyPy
Security Scanning: npm audit, Python safety checks, Bandit analysis
Coverage Analysis: Combined coverage reporting with Codecov integration
Performance Testing: Startup time measurement and benchmark tracking

Test Coverage Overview

Current coverage metrics:

TypeScript Coverage: Comprehensive coverage of MCP server implementation
Python Coverage: Core operations and CLI functionality
Combined Reporting: Unified coverage analysis across both languages
Automated Tracking: Real-time coverage monitoring via Codecov

Quick Testing Commands

# Essential testing commands for daily development
npm run ci:check          # Pre-commit validation (recommended before push)
npm run test:all          # Run all tests with coverage
npm run coverage          # Full coverage analysis with combined reporting
npm run coverage:open     # View coverage reports in browser

# Individual test suites
npm test                  # TypeScript tests only
npm run test:python       # Python tests only
npm run test:coverage     # TypeScript with coverage
npm run test:python:coverage  # Python with coverage

# Development testing
npm run test:watch        # Watch mode for continuous testing
npm run coverage:clean    # Coverage without external uploads

Comprehensive Testing Commands

# Quality assurance
npm run lint              # ESLint for TypeScript
npm run lint:fix          # Auto-fix linting issues
npm run format            # Prettier code formatting
npm run typecheck         # TypeScript type validation

# Coverage and reporting  
npm run badges:update     # Generate coverage badges
npm run coverage:ci       # CI-optimized coverage reporting
npm run coverage:view     # Open all coverage reports
npm run coverage:combined # View combined coverage report

# Build and validation
npm run build             # Build TypeScript
npm run watch             # Development build with watch
npm run inspector         # MCP inspector for tool testing

Test Reports and Artifacts

After running tests, detailed reports are available:

TypeScript Coverage: ./coverage/index.html
Python Coverage: ./smartsheet_ops/coverage/index.html
Combined Coverage: ./coverage-combined/index.html
Test Artifacts: Available in CI/CD pipeline runs

Quality Thresholds

The project enforces strict quality standards:

TypeScript Coverage: 60% minimum (configurable per component)
Python Coverage: 80% overall with line-by-line reporting
Code Quality: ESLint rules, TypeScript strict mode, Python Black/Flake8
Security: Regular dependency audits and vulnerability scanning
Performance: Startup time monitoring and regression detection

Docker Support

Build and run the containerized version:

# Build Docker image
docker build -t smartsheet-server .

# Run with environment variables
docker run -e SMARTSHEET_API_KEY=your_key -e PYTHON_PATH=/usr/local/bin/python smartsheet-server

Debugging

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:

Error logging to stderr
Detailed error messages in MCP responses
Type validation at multiple levels
Comprehensive operation result reporting
Dependency analysis for column operations
Formula reference tracking

Error Handling

The server implements a multi-layer error handling approach:

MCP Layer
- Validates tool parameters
- Handles protocol-level errors
- Provides formatted error responses
- Manages timeouts and retries
CLI Layer
- Validates command arguments
- Handles execution errors
- Formats error messages as JSON
- Validates column operations
Operations Layer
- Handles Smartsheet API errors
- Validates data types and formats
- Provides detailed error context
- Manages column dependencies
- Validates formula references
- Ensures data integrity

Contributing

Contributions are welcome! Please ensure:

TypeScript/Python code follows existing style
New features include appropriate error handling
Changes maintain backward compatibility
Updates include appropriate documentation
Column operations maintain data integrity
Formula references are properly handled

Diesen Server hinzufügen

Host a copy with our keys and logs.

Server facts

Tools

Questions

How do I install Smartsheet Server?

Does Smartsheet Server need an API key?

What transport does Smartsheet Server use?

When was Smartsheet Server last checked?

Documentation

Smartsheet MCP Server

Overview

Key Benefits

Use Cases

Integration Points

Features

Tools (34 Available)

Resources (4 Static + 5 Dynamic Templates)

Static Resources

Dynamic Resource Templates

Prompts (6 Available)

Key Capabilities

Setup

Prerequisites

Environment Setup

Configuration

1. Get Your Smartsheet API Key

2. Configure for STDIO Transport (Cline/Local)

3. Configure for HTTP Transport

Starting the Server

STDIO Transport (Default)

HTTP Transport

Command Line Options

Verifying Installation

STDIO Transport

HTTP Transport

Python Environment

Usage Examples

Getting Column Information (Read)

Writing Data (Create)

Searching Data

Updating Data (Update)

Deleting Data (Delete)

Healthcare Analytics Examples

Workspace Management Examples

Resources Usage Examples

Prompts Usage Examples

Development

CI/CD Pipeline

Pipeline Architecture

Key Pipeline Features

Workflow Triggers

Status Monitoring

Testing and Quality Assurance

Test Infrastructure

Test Coverage Overview

Quick Testing Commands

Comprehensive Testing Commands

Test Reports and Artifacts

Quality Thresholds

Docker Support

Debugging

Error Handling

Contributing