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openl-tablets

OpenL MCP Server

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Merge Table Cells (raw)

openl_merge_table_cells
Idempotent

Merge a rectangular block of cells in a table's raw source into one cell, retaining the top-left value, to consolidate layout and improve table structure.

Instructions

Merge a rectangular range of cells into one in a table's raw source, keeping the value of the top-left cell at ('row','column'). The range ('rowspan'×'colspan') must cover more than one cell and stay within the table. 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).
columnYes0-based column index of the top-left cell of the range (0..width-1).
colspanYesNumber of columns the merged cell spans (>= 1).
rowspanYesNumber of rows the merged cell spans (>= 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

A4.5/5.0
Behavior5/5

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

The description goes well beyond the sparse annotations (openWorldHint, idempotentHint) by disclosing that the top-left value is retained, that a relocating edit changes the location-derived id, and that the response returns the current tableId plus previousTableId when changed. It also explains the recompile trigger for openl_project_status and defines the 0-based coordinate semantics for header and label columns. No contradiction with annotations.

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 carries an operational fact: action, constraint, raw-source scope, indexing, id volatility, and recompile behavior. The description is longer than average but front-loads the core action and then layers necessary caveats; there is no filler or repeated schema 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?

With no output schema, the description explicitly states what the response contains (current tableId, previousTableId when changed) and why it matters for subsequent calls. It also covers the side effect on openl_project_status and the range validity constraint, so an agent has what it needs to call the tool and interpret its result. Minor omissions like error behavior are secondary given the detailed schema.

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 all parameters are individually documented; the description adds value by clarifying the coordinate frame (row 0 = header, column 0 = leading labels) and the validity condition that the range must cover more than one cell. It also reinforces the volatile tableId behavior that the schema already partially states.

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: 'Merge a rectangular range of cells into one in a table's raw source.' It states the top-left value retention behavior and the validity constraints, and it distinguishes the tool from siblings like openl_unmerge_table_cells and openl_update_table_cell by emphasizing the RAW source and any-table-type applicability.

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

Usage Guidelines3/5

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

The description implies the tool is intended for raw-source merging and notes it works for any table type, but it never names alternative tools or states when to prefer this over openl_update_table_range or openl_update_table_cell. There are no explicit when-not-to-use conditions, leaving the agent to infer routing from the sibling list.

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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