Documentation

How SpreadAPI Works

Transform your spreadsheets into powerful APIs that can be called by applications, AI assistants, or integrated into any workflow. Your Excel expertise becomes instantly accessible.

API Endpoint{"inputs": [...],"outputs": [...]}

Transform Spreadsheets Into Intelligent APIs

SpreadAPI bridges the gap between spreadsheet expertise and modern applications. Your complex calculations, business logic, and data models become instantly accessible through clean API endpoints that any developer or AI assistant can use.

No coding required - use your Excel skills

Instant API generation from any spreadsheet

AI-ready with MCP integration

Upload Your Spreadsheet
Core Concepts

Three Simple Building Blocks

Input Parameters

Define which cells receive values when your API is called. Like function arguments, these are the values users provide to trigger calculations.

Cell B2: interest_rate
Cell B3: loan_amount
Cell B4: years

Output Parameters

Specify which cells contain the results to return. These calculated values become your API response, delivered as clean JSON data.

Cell E2: monthly_payment
Cell E3: total_interest
Cell E4: total_paid

Editable Areas (AI)

Enable AI assistants to interact with cell ranges directly. Perfect for data analysis, what-if scenarios, and formula generation.

Range A1:D10
Permissions: Read/Write
AI can experiment freely
API Workflow

From Spreadsheet to API in Minutes

1

Upload & Configure

Upload your Excel file and select cells for inputs and outputs. No coding needed.

2

Test & Validate

Try your API with sample values. See results instantly. Refine as needed.

3

Publish & Share

Get your unique API endpoint. Share with developers or connect AI assistants.

4

Call & Calculate

Send inputs, receive outputs. Your spreadsheet logic runs in the cloud.

The API Flow

API Call Received

Your service receives a request with input values

Inputs Applied

Values placed into designated cells

Calculation

Formulas automatically recalculate

Response Sent

Results returned as JSON

API Request
GET /api/v1/services/loan_calc/execute
  ?loan_amount=200000
  &interest_rate=0.045
  &years=30
API Response
{
  "serviceId": "loan_calc",
  "inputs": {
    "loan_amount": 200000,
    "interest_rate": 0.045,
    "years": 30
  },
  "outputs": {
    "monthly_payment": 1013.37,
    "total_interest": 164813.42,
    "total_paid": 364813.42
  },
  "metadata": {
    "executionTime": 12,
    "timestamp": "2024-01-15T10:30:00Z",
    "version": "v1"
  }
}

Real Example: Loan Calculator

See how a simple loan calculator spreadsheet becomes a powerful API. Input parameters feed into Excel's PMT function, and the calculated monthly payment is returned instantly.

Input Cells

B2: loan_amount, B3: interest_rate, B4: years

Excel Formula

=PMT(B3/12, B4*12, -B2)

AI Integration

Built for AI Assistants

SpreadAPI supports MCP (Model Context Protocol), enabling AI assistants like Claude to discover and use your spreadsheet services automatically.

Auto-Discovery

AI assistants automatically find and understand your available services

Natural Language

Users can request calculations in plain English - AI handles the rest

Interactive Analysis

AI can work with editable areas to perform complex data analysis

Workflow Automation

Combine multiple services into sophisticated AI-powered workflows

Example: AI Assistant Interaction

User: "Calculate the monthly payment for a $300,000 loan at 4.5% for 30 years"
Claude: "I'll calculate that for you using the loan calculator service..."
Calling: spreadapi_calc_loan_calculator
→ loan_amount: 300000
→ interest_rate: 0.045
→ years: 30
Result: Your monthly payment would be $1,520.06

Best Practices for Success

Clear Naming: Use descriptive names like "interest_rate" not "input1"
Validation: Set min/max values to prevent calculation errors
Documentation: Add descriptions to help users understand parameters
Error Handling: Use IFERROR() in formulas for robustness
Test First: Always test your API before publishing
AI Context: Provide clear descriptions for AI understanding