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logisky

logisheets-mcp

chart_from_block

Create charts from spreadsheet data blocks by naming fields instead of cell ranges. The chart stays linked to the block, so new records and row/column insertions don't break it.

Instructions

Chart a block by naming its fields, instead of working out which cells they occupy. The chart stays bound to the block: it plots whatever the named fields hold, so records added to the block later appear on their own and inserting rows or columns cannot leave it pointing at the wrong cells. A col-schema block works the same way as a row-schema one. Use edit__describe_block or inspect first if you do not know the field names. Blocks with a "random" schema have no field axis and cannot be charted this way — chart their cell range with chart__insert instead. Fields are held by name. Renaming one breaks the link (the series goes empty); moving its column does not.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleNoDefaults to the block's schema name.
anchorNoTop-left cell of the chart. Defaults to two rows below the block.
blockIdYes
sheetIdxYesZero-based sheet index.
chartTypeNoDefaults to col.
sizeInCellsNo
valueFieldsYesField names to plot, one series each, in order.
categoryFieldNoField whose values label the categories (the X axis). The block's key column is not addressable as a range, so name the field that reads as a label. Omitted, the categories are just 1..n.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.1

TDQS

A4.7/5.0
Behavior5/5

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

The description discloses binding behavior beyond annotations: the chart stays bound to the block, updates with new records, survives column moves, but breaks if a field is renamed. Annotations only say readOnlyHint=false and destructiveHint=false, so this adds valuable context about how the chart behaves over time.

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 a compact paragraph that leads with the core purpose, then adds behavioral detail, usage guidance, and a caveat – all in a logical order with no wasted words. Every sentence contributes distinct, actionable information.

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 tool with 8 parameters and no output schema, the description covers the key contextual aspects: what it does, how to prepare, when not to use it, and how the chart behaves. It also addresses schema variations (col vs. row) and the random-schema limitation, leaving nothing essential for an agent to infer.

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

Parameters3/5

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

Schema coverage is 75%, so the baseline is 3. The description adds conceptual context about field-name binding that relates to valueFields and categoryField, but it does not systematically explain each parameter beyond what the schema already provides. It adds some value, but not enough to raise the score.

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 clear, specific statement: 'Chart a block by naming its fields' – a concrete verb and resource. It also distinguishes itself from the sibling chart_insert by explicitly noting that random-schema blocks should use chart_insert instead, so an agent can tell them apart immediately.

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 gives explicit prerequisites ('Use edit__describe_block or inspect first if you do not know the field names') and an explicit exclusion: 'Blocks with a random schema... cannot be charted this way — chart their cell range with chart__insert instead.' This leaves no ambiguity about when to use this tool versus the alternative.

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