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KitchenSink4AI

io.github.nometalalchemist/kitchensink4xl

by KitchenSink4AI
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Local

get_cells

Read-only

Read many individually addressed cells in one call, with honest cached/formula/absent/value labels, so a formula without a cached result is never shown as blank. Read-only while file open in Excel.

Instructions

Read many individually addressed cells in one call, the scatter complement to the rectangular read_range. cells is a list of A1 strings or location objects, each resolving to ONE cell (1,000-cell ceiling); values is cached, formula, or both, and every returned value carries the honest label (cached, absent, formula, value), so a formula with no cached value is never passed off as blank. Read-only; works while the file is open in Excel.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes
cellsYesIndividually addressed cells: A1 strings or location objects, each resolving to ONE cell.
sheetNo
valuesNocached

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. Changed2 schema fields changedv1.1.0
    • addedInput schema / properties / cells / description
      Added value: +"Individually addressed cells: A1 strings or location objects, each resolving to ONE cell."
    • addedInput schema / properties / cells / items / anyOf
      Added value: +[
+  {
+    "type": "string"
+  },
+  {
+    "type": "object"
+  }
+]
  3. First observedv1.0.0

TDQS

A4.9/5.0
Behavior5/5

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

Annotations only provide readOnlyHint, but the description adds substantial behavioral detail: the 1,000-cell ceiling, the values options (cached, formula, both), the honest result labels (cached, absent, formula, value), and the guarantee that formulas without cached values are not misrepresented as blank. This is far beyond what annotations convey.

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?

Dense but well organized: the core purpose is front-loaded, and every clause contributes either scope, limits, return semantics, or runtime context. There is no filler or repetition of annotation data.

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

Completeness5/5

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

For a read-only scatter read with no output schema, the description covers the operation's scope, limits, alternative, return labeling behavior, and runtime conditions. An agent has enough context to select and invoke the tool correctly without guessing.

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?

The description adds real semantic value for the main parameters: cells must resolve to exactly one cell and has a 1,000-cell ceiling, while values accepts cached, formula, or both. However, the path and sheet parameters are not described, leaving a minor gap given the low schema coverage.

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 clearly states a specific operation: reading many individually addressed cells in one call. It names the resource type (cells), the addressing mechanism (A1 strings or location objects), and explicitly positions itself as the scatter complement to read_range, distinguishing it from siblings.

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

Usage Guidelines5/5

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

It explicitly contrasts with read_range ('scatter complement to the rectangular read_range'), giving the agent a clear rule for when to choose this tool over a sibling. It also adds context that the tool is read-only and works while the file is open in Excel.

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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