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logisky

logisheets-mcp

by logisky

convert_to_block

Convert an existing cell table into a named block with fields and row keys, enabling formula references by name instead of cell coordinates.

Instructions

Turn a table that already exists in ordinary cells into a block, in place, without touching its values.

This is how you adopt a workbook someone hands you. create_block is for new tables and refuses to write over existing data; this one takes the data as it stands and gives it a name, fields and row keys, so you can address it as (block, row_key, field) and reference it from formulas by name instead of by coordinate.

Give position and the counts for the DATA only, leaving out any header row, then either header_row to read the field names from the titles or fields to state them. The first field is the row-key column, so put the column that identifies each record first.

Converting is a one-time cost: the block and its schema survive saving and reloading, and afterwards the region behaves exactly like one created as a block.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesRef name for the new block — BLOCKREF's first argument. Must be unique.
sheetYesSheet the table is on.
fieldsNoField names, when there is no header row. Length must equal col_count.
positionYesTop-left cell of the data, excluding the header row.
col_countYesNumber of columns.
row_countYesNumber of data rows.
header_rowNoRow holding the column titles; field names are read from it. Usually one row above `position`.
Behavior5/5

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

Annotations only say readOnly=false, destructive=false, so the description carries the behavioral burden. It adds crucial traits: values are untouched, conversion is an in-place one-time structural change, and the block plus schema survive save/reload and behave like a created block.

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?

Every sentence earns its place: purpose, use case, sibling contrast, parameter guidance, and persistence behavior. It is front-loaded with the core action and avoids boilerplate.

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 7-parameter mutation tool with no output schema, the description covers why to use it, how to parameterize it, what the result is, and how it persists. No critical operational gap is apparent.

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

Parameters5/5

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

Although schema coverage is 100%, the description adds meaning beyond property descriptions: position/counts must exclude the header row, header_row and fields are alternatives, and the first field is the row-key column. This materially improves correct invocation.

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?

Description opens with a specific verb and resource: 'Turn a table that already exists in ordinary cells into a block, in place, without touching its values.' It also distinguishes itself from create_block, making the tool's purpose unmistakable.

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?

Explicitly tells when to use: 'This is how you adopt a workbook someone hands you' and contrasts with create_block, which 'is for new tables and refuses to write over existing data.' It also gives concrete instructions on supplying data-only counts and choosing header_row vs fields.

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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