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openl-tablets

OpenL MCP Server

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Delete Table Columns (raw)

openl_delete_table_columns
Destructive

Remove one or more columns from a table's raw source at a specified position, shifting the remaining columns left to keep the table compact. Works for any table type and returns the current table ID for subsequent operations.

Instructions

Delete ONE OR MORE columns starting at 'position' (1..width-1) from a table's raw source, shifting the columns to the right left. 'count' defaults to 1. The leading-label column (0) cannot be deleted. 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
countNoNumber of columns to delete starting at 'position' (default 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().
positionYes0-based index of the first column to delete (1..width-1). The leading-label column (0) cannot be deleted. Columns to the right of the deleted block shift left.
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

A4.6/5.0
Behavior5/5

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

The description goes well beyond the destructiveHint annotation by explaining the actual destructive behavior (shifting columns, leading-label protection), the possibility of id changes, the response always returning the current tableId, and the recompile side effect. This is rich behavioral disclosure.

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?

Every sentence carries necessary information, and the core operation is front-loaded in the first clause. The description is long, but the complexity of the tool (id volatility, raw-source semantics, recompile behavior) justifies the length. Some sentences are dense and could be split, but nothing is filler.

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 destructive mutation tool with no output schema, the description covers the essential context: what gets deleted, what cannot be deleted, how positions work, how ids may change, what the response contains, and a caveat about compilation. An agent has enough to call this tool correctly and handle side effects.

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 100%, so the baseline is 3. The description adds meaningful semantic context beyond the schema: 0-based indexing clarifies the header row, column 0 as leading labels, and the default count of 1. It reinforces and slightly extends the schema explanations without redundancy.

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 clearly states the verb 'delete' and resource 'columns from a table's raw source', specifying position-based deletion and the shifting behavior. It also clarifies the leading-label column cannot be deleted, unambiguously distinguishing it from other table mutation tools.

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

Usage Guidelines4/5

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

The context is clearly defined: it operates on the RAW source, works for any table type, and explains the exact positional semantics. It doesn't explicitly name sibling alternatives or say when not to use this tool, but the 'raw source' framing and row/column behavior give sufficient guidance for correct selection.

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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