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KitchenSink4AI

io.github.nometalalchemist/kitchensink4xl

by KitchenSink4AI
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Python
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set_cells

Write many individual cells in one atomic batch, validating all addresses before applying any changes, and supporting formulas with automatic backup.

Instructions

Write many individually addressed cells as ONE atomic batch, the scatter complement to write_range. cells is a list of {cell, value} items (cell is an A1 string or a location object resolving to one cell; 1,000-cell ceiling); every address is resolved BEFORE anything is written, so one bad item refuses the whole batch untouched. '=' strings become formulas, normalized. A hazardous workbook refuses unless allow_loss is true. Auto-backup to .ks4xl-backups; atomic verified save. Refuses while open in Excel.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes
cellsYesScatter writes, {cell, value}; every address resolves before anything is written.
sheetNo
backupNo
allow_lossNo
verify_comNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.2.0
    • changedOutput schema / (root)
      Previous value: -{
-  "additionalProperties": true,
-  "type": "object"
-}New value: +null
  2. Changed4 schema fields changedv1.1.0
    • addedInput schema / properties / cells / description
      Added value: +"Scatter writes, {cell, value}; every address resolves before anything is written."
    • addedInput schema / properties / cells / items / properties
      Added value: +{
+  "cell": {
+    "anyOf": [
+      {
+        "type": "string"
+      },
+      {
+        "type": "object"
+      }
+    ]
+  },
+  "value": {
+    "anyOf": [
+      {
+        "type": "string"
+      },
+      {
+        "type": "number"
+      },
+      {
+        "type": "boolean"
+      },
+      {
+        "type": "null"
+      }
+    ],
+    "description": "A cell value: text, a number, a boolean, or null to clear. A string beginning with '=' is stored as a formula."
+  }
+}
    • addedInput schema / properties / cells / items / required
      Added value: +[
+  "cell"
+]
    • addedInput schema / properties / cells / items / type
      Added value: +"object"
  3. First observedv1.0.0

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With only readOnlyHint=false in annotations, the description carries the full behavioral burden and does so excellently. It discloses atomicity, pre-resolution of all addresses, whole-batch refusal on any bad item, formula normalization, hazardous-workbook refusal unless allow_loss is true, auto-backup, atomic verified save, and refusal while open in Excel. This goes far beyond the annotation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the purpose, then packs essential operational details into tight, information-dense sentences. Every sentence earns its place: atomicity, cell addressing rules, formula handling, hazard protection, backup behavior, and Excel-lock refusal. No filler or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex write operation with a single weak annotation and no output schema, the description covers the critical context: atomic semantics, failure behavior, formula handling, hazard gating, backup, and operating constraints. It does not describe the return value or success signal, and a few parameters remain unexplained, but the core behaviors needed for correct invocation are well covered.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 17%, so the description must compensate. It adds meaningful semantics for the cells parameter: lists of {cell, value}, A1 string or location object, 1,000-cell ceiling, and pre-resolution behavior. It also explains formula behavior for value strings and ties allow_loss to hazardous workbooks. However, path, sheet, and verify_com receive no explanatory treatment, so compensation is strong but not complete.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Write many individually addressed cells as ONE atomic batch.' It also explicitly names write_range as the contrast ('scatter complement'), and the emphasis on 'many' and 'atomic batch' distinguishes it from the sibling set_cell. An agent can identify this tool correctly without inspecting the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly frames when this tool is appropriate: writing many individually addressed cells in a single atomic scatter operation, and it names write_range as the alternative concept. It does not explicitly state when not to use it versus set_cell, but the context makes the intended use clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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