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openl-tablets

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Update Table Range (raw)

openl_update_table_range
Idempotent

Overwrite a rectangular range of cells in a table in a single operation, anchored at a specified row and column while keeping the table size unchanged. Returns the current table ID after the edit.

Instructions

Overwrite a rectangular RANGE of cells in place, anchored at the top-left ('row','column'), in a table's raw source. 'cells' is a 2D array (rows × that row's cells); the range must cover more than one cell and fit within the table (not resized). For a single cell use openl_update_table_cell. Operates on the table's RAW source, so it works for any table type. Positions are 0-based (row 0 is the header row, column 0 carries the leading labels). An edit that relocates the table (it had no room to grow in place) CHANGES its location-derived id; the response always returns the table's CURRENT id as 'tableId' (plus previousTableId when it changed) — use it for subsequent calls. Note: the studio does not auto-compile after an edit; this tool reads the table back to trigger the recompile, so a subsequent openl_project_status reflects the change.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowYes0-based row index of the top-left cell of the range (0..height-1).
cellsYesBlock rows top to bottom, each a non-empty list of cells left to right. Anchored at ('row','column'); must cover more than one cell and fit within the table (the table is not resized).
columnYes0-based column index of the top-left cell of the range (0..width-1).
tableIdYesTable identifier - unique ID assigned by OpenL Studio (e.g., 'calculatePremium_1234'). VOLATILE: derived from the table's location, so it changes when an edit relocates the table (it had no room to grow in place) — use the 'tableId' returned by the latest openl_update_table/openl_append_table response, or refresh via openl_list_tables().
projectIdYesProject ID returned by backend. Use the exact 'projectId' value from openl_list_projects() response without modification or reformatting.
response_formatNoResponse format: 'json' for structured, round-trippable data (default), 'markdown' for human-readable output, 'markdown_concise' for a brief summary (1-2 paragraphs), or 'markdown_detailed' for full details with contextjson

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changedv1.2.0
    • changedInput schema / properties / response_format / default
      Previous value: -"markdown"New value: +"json"
    • changedInput schema / properties / response_format / description
      Previous value: -"Response format: 'json' for structured data, 'markdown' for human-readable (default), 'markdown_concise' for brief summary (1-2 paragraphs), 'markdown_detailed' for full details with context"New value: +"Response format: 'json' for structured, round-trippable data (default), 'markdown' for human-readable output, 'markdown_concise' for a brief summary (1-2 paragraphs), or 'markdown_detailed' for full details with context"
  2. Addedv1.1.0

TDQS

A4.8/5.0
Behavior5/5

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

The description discloses several non-obvious behaviors beyond the annotations: an edit that relocates the table changes its location-derived id; the response always returns the current tableId and previousTableId when changed; the studio does not auto-compile after an edit; and the tool reads the table back to trigger recompile so openl_project_status reflects the change. This is rich, valuable context that annotations (openWorldHint, idempotentHint) do not convey.

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

Conciseness4/5

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

The description is information-dense with no filler, front-loading the core action and then layering constraints, coordinate conventions, and side-effect warnings. It is longer than strictly necessary but every sentence attends to a distinct fact an agent needs; the structure is well-ordered with the alternative usage and important id-volatility caveat placed logically near related facts.

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 mutation tool with no output schema and only abstract annotations, the description covers all critical operational context: what happens to the table and its id, how the response reports the current id, the recompile side-effect, coordinate conventions, and the single-cell alternative. An agent has enough information to invoke the tool correctly and reason about follow-up calls.

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 100%, so the baseline is 3. The description adds meaningful semantics beyond the schema: it clarifies the 2D structure of 'cells', the anchoring of ('row','column'), the 0-based coordinate system with row 0 as the header row and column 0 carrying leading labels, and the 'not resized' constraint. This is genuinely useful contextual enrichment, though it also repeats some schema details.

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 object: 'Overwrite a rectangular RANGE of cells in place, anchored at the top-left ('row','column')'. It clearly identifies the resource (a table's raw source), the operation (overwrite), and the shape (rectangular range), which distinguishes it from the single-cell sibling openl_update_table_cell.

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?

The description explicitly names an alternative: 'For a single cell use openl_update_table_cell.' It also provides boundary conditions — the range must cover more than one cell, must fit within the table, and operates on the raw source for any table type — which help an agent decide when this tool is appropriate versus other table-edit siblings.

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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