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openl-tablets

OpenL MCP Server

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Replace Entire Table

openl_update_table
Idempotent

Replace the entire table structure to modify, delete, or reorder rows. Use after retrieving the full table with get_table() and applying changes.

Instructions

Replace the ENTIRE table structure with a modified version. Use for MODIFYING existing rows, DELETING rows, REORDERING rows, or STRUCTURAL changes. CRITICAL: Must send the FULL table structure (not just modified fields). DO NOT use for simple additions - use append_table instead. Required workflow: 1) Call get_table() to retrieve complete structure, 2) Modify the returned object, 3) Pass the ENTIRE modified object to update_table(). IMPORTANT: 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 all subsequent calls. Note: the studio does not auto-compile after an edit (it only resets the previous compile status); this tool reads the table back after updating to trigger the recompile, so a subsequent openl_project_status reflects the change.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
viewYesFULL table structure from get_table() with your modifications applied. MUST include: id, tableType, kind, name, plus type-specific data (rules for SimpleRules, rows for Spreadsheet, fields for Datatype). Keep 'tableType' EXACTLY as get_table() returned it (it is a CASE-SENSITIVE discriminator: Datatype, Spreadsheet, SimpleRules, SmartRules, SimpleSpreadsheet, Vocabulary, Data, Test, SimpleLookup, SmartLookup, RawSource — lowercase is rejected). Do NOT send only the changed fields - send the complete structure. Workflow: 1) currentTable = get_table(), 2) currentTable.rules[0]['Column'] = newValue, 3) update_table(view=currentTable)
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 data, 'markdown' for human-readable (default), 'markdown_concise' for brief summary (1-2 paragraphs), 'markdown_detailed' for full details with contextmarkdown
Behavior5/5

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

Adds significant context beyond annotations: tableId volatility (changes when table relocates), return fields (tableId and previousTableId), and that the tool reads the table back to trigger recompile (studio does not auto-compile). This goes well beyond the idempotentHint and openWorldHint 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 somewhat long but well-structured with warnings, steps, and notes. It is front-loaded with the core purpose. Every sentence provides essential guidance for correct usage. Could be slightly condensed, but the detail is justified given the tool's complexity.

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?

Given the tool's complexity (nested object, volatile IDs, compile implications, required workflow), the description covers all critical aspects: how to obtain the table, what to include, how to handle tableId changes, response behavior, and side effects. No output schema exists, but description sufficiently describes return values. Complete context for an agent to use correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but description adds extra meaning: for 'view' it explains the full structure requirement, workflow, and field constraints; for 'tableId' it explains volatility and how to obtain; for 'projectId' it stresses exact value; for 'response_format' it explains each option. Adds significant value beyond schema.

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 replaces the entire table structure and lists specific use cases (modifying, deleting, reordering rows, structural changes). It distinguishes itself from the sibling 'openl_append_table' by specifying when not to use it. The verb 'Replace' and resource 'Entire Table' are explicit.

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?

Provides explicit when-to-use (modifications, deletions, reordering) and when-not-to-use (simple additions, use append_table). Includes a required workflow with steps (get_table, modify, send full object). Also discloses critical behavioral details like tableId volatility and compile behavior.

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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