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openl-tablets

OpenL MCP Server

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Append Table Rows (raw)

openl_append_table_rows

Append one or more rows to the end of a table's raw source, including blank cells and spans. Returns the current table ID and triggers recompilation so project status reflects the change.

Instructions

Add ONE OR MORE rows to the END of a table's raw source. 'cells' is a 2D array: outer = rows top to bottom, inner = that row's cells left to right (one per column; use { value: null } for blanks). Pass a single row to add one, several for a block. 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
cellsYesRows top to bottom, each a non-empty list of cells left to right (one cell per column; use { value: null } for a blank cell). Pass one row to add/insert a single row, several for a block. Each row as wide as 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().
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

A4.1/5.0
Behavior4/5

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

Annotations only include openWorldHint=true, so the description carries most of the burden. It discloses important behaviors: it appends to raw source, can change the table's ID due to relocation, returns current tableId and previousTableId, and triggers a recompile via reading the table back. This exceeds the minimal safety disclosure expected.

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 long but every sentence earns its place: it covers data structure, positioning, raw source, ID volatility, and recompile side-effect. It's front-loaded with the core append behavior. Slightly dense, but not bloated.

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 4-param tool with no output schema and only openWorldHint annotation, the description is quite complete. It explains side effects that an agent must know (ID changes, recompile trigger). It doesn't detail response format beyond tableId/previousTableId, but since no output schema exists, a brief note on response shape could push it to 5.

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 100%, so the schema already documents all parameters. The description adds useful context about the 2D array orientation and blank-cell syntax, but it doesn't add much beyond the schema's own descriptions. Baseline 3 is appropriate because the schema does the heavy lifting.

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 tool adds one or more rows to the end of a table's raw source, with specific details on the 2D array structure. It distinguishes itself from siblings like openl_insert_table_rows (adds rows elsewhere) and openl_update_table_row by emphasizing 'END' and 'raw source'.

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 description gives clear usage context—when to use it to add rows, how to handle blanks, and mentions it works for any table type because it operates on raw source. It doesn't explicitly contrast with openl_insert_table_rows or openl_append_table (which may be a high-level wrapper), but the positional and raw-source details provide enough guidance. No explicit exclusions, though.

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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