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openl-tablets

OpenL MCP Server

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Update Table Column (raw)

openl_update_table_column
Idempotent

Replace the contents of an existing column in a raw table source at a specified position, top to bottom, without resizing. Works for any table type and returns the current table ID for subsequent calls.

Instructions

Overwrite the cells of an existing column at 'position' (0..width-1) in a table's raw source, top to bottom. The table is not resized. 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
cellsYesColumn cells, top to bottom. Required and non-empty — provide one cell per row (use { value: null } for a blank cell). A cell may set colspan/rowspan to merge. Must not be taller than the table.
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().
positionYes0-based index of the column to overwrite (0..width-1). The table is not resized.
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

A4.3/5.0
Behavior5/5

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

Beyond the annotations, the description discloses important non-obvious behavior: an edit relocating the table changes its location-derived id and the response returns currentTableId plus previousTableId; the studio does not auto-compile and the tool reads the table back to trigger recompile. This is exactly the kind of behavioral context an agent needs, and it does not contradict the annotations.

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 relatively long but every section earns its place: operation, positioning, raw-source scope, id volatility, and recompile behavior. It is front-loaded with the core action. There is minor redundancy with the schema for 0-based positioning, but the extra context is valuable enough to justify the length.

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

Completeness4/5

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

For a complex table mutation tool with no output schema, the description covers the critical operational details: position bounds, header/label conventions, no resize, raw source behavior, id relocation, response tableId semantics, and recompile triggering. It does not describe the full response shape beyond the id, but the schema and response_format parameter cover the important remaining usage details.

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 coverage is already 100%, so a baseline of 3 is appropriate. The description adds value by clarifying positional semantics not fully captured in the schema: 'row 0 is the header row, column 0 carries the leading labels' and that cells are written 'top to bottom.' This helps an agent construct the cells array correctly.

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 uses a specific verb and resource: 'Overwrite the cells of an existing column at position (0..width-1)... top to bottom.' It also clarifies the scope ('the table is not resized', 'works for any table type'), making it clearly distinguishable from sibling table tools like openl_update_table_cell or openl_update_table_range even though no sibling is named.

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 usage context: it updates an existing column, is not for resizing, and works on the RAW source for any table type. However, it never explicitly says when to prefer this over siblings such as openl_update_table_row, openl_update_table_cell, or openl_append_table_columns, and no alternatives or exclusions are named.

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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