# fill-sheet

> Use the sheet-filler MCP server to safely auto-fill tabular data.

- Author: Stanislav Popov
- Repository: popstas/mcp-sheet-filler
- Version: 20260123030658
- Stars: 0
- Forks: 0
- Last Updated: 2026-02-06
- Source: https://github.com/popstas/mcp-sheet-filler
- Web: https://mule.run/skillshub/@@popstas/mcp-sheet-filler~fill-sheet:20260123030658

---

---
name: fill-sheet
description: Use the sheet-filler MCP server to safely auto-fill tabular data.
---

## Purpose

The sheet-filler server manages objects (rows) with fields (columns). It prevents overwriting existing values, making it safe for incremental data collection.

## Workflow

#### 1. Understand the Schema

First, get the field definitions to understand what data to collect: `filler_list_fields()`.

Fields with `auto: true` are candidates for auto-filling. Each field has:
- `name` - field identifier
- `type` - validation type (string, number, date, url, email, json, enum:...)
- `instructions` - how to collect this value
- `example` - example value

#### 2. Get an Object

Retrieve the next object you need to fill: `filler_get_next_missing_fields_object()`.

This returns the first object with missing auto fields, along with what needs to be filled. If `found: false`, all objects are complete.

#### 3. Collect Values

For each missing field, follow the `instructions` to collect the value. Ensure values match the field `type`: 

| Type | Format |
|------|--------|
| `string` | Any text |
| `number` | Numeric value |
| `date` | `YYYY-MM-DD` |
| `datetime` | ISO-8601 |
| `url` | Full URL with protocol |
| `email` | Valid email address |
| `json` | Valid JSON string |
| `enum:a\|b\|c` | One of the listed values |

#### 4. Save Values

Save collected values (won't overwrite existing data):

```
filler_save_object_no_overwrite({
  name: "Acme Corp",
  values: {
    "website": "https://acme.com",
    "founded": "1990"
  }
})
```

Check the result for each field:
- `saved` - value was stored
- `skipped_already_set` - field already has a value (not overwritten)
- `rejected_unknown_field` - field not in schema
- `rejected_invalid_type` - value failed type validation

#### 5. Repeat

Continue with step 2 until no missing auto fields remain.

## Creating New Data

### Add a Field

```
filler_add_field({
  field: {
    name: "revenue",
    description: "Annual revenue",
    type: "number",
    auto: true,
    instructions: "Find the company's annual revenue in USD"
    example: "1000000"
  }
})
```

### Add an Object

```
filler_add_object_by_name({ name: "New Company" })
```

## Example Session

```
# 1. Check schema
filler_list_fields()
→ fields: [name, website (auto), email (auto), founded]

# 2. Get object
filler_get_next_missing_fields_object()
→ { found: true, object: { name: "Acme Corp" }, missing: [{ name: "website", type: "url", instructions: "Find official website" }] }

# 3. Collect and save
filler_save_object_no_overwrite({ name: "Acme Corp", values: { website: "https://acme.com" } })
→ result: { website: "saved" }

## Key Rules

1. **Never guess values** - only save data you have verified
2. **Check field types** - ensure values match the expected format before saving
3. **Trust the no-overwrite** - the server protects existing data, but don't rely on this as a crutch
4. **Follow instructions** - each field's `instructions` describe how to collect that specific value
5. **Handle rejections** - if a value is rejected, check the type and fix accordingly