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openl-tablets

OpenL MCP Server

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Get Table Structure & Data

openl_get_table
Read-onlyIdempotent

Retrieve detailed table or rule information, either as a parsed structure with conditions and actions or as a raw cell matrix for custom table types.

Instructions

Get detailed information about a specific table/rule. By default returns a parsed table structure with signature, conditions, actions, dimension properties, and row data. Set raw=true to get an unparsed 2D cell matrix (RawTableView) instead — useful for unknown/custom table types or preserving exact cell layout. Note: raw output cannot be passed directly to openl_update_table (which expects the parsed form). A table id changes when an edit relocates the table; if the given id went stale through an edit made via this server, it is resolved to the current id automatically — otherwise refresh ids with openl_list_tables().

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rawNoIf true, returns the raw table view as a 2D matrix of cells without any parsing or structure interpretation. Useful for reading tables of unknown or custom types, preserving exact cell positioning and merge regions.
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?

Annotations already indicate read-only, idempotent, and open-world behavior. The description adds critical behavioral context: tableId volatility, automatic resolution of stale IDs, and the limitation of raw output for update operations. No contradictions.

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?

The description is front-loaded with the core purpose and concisely explains the two modes, use cases, and important caveats. Every sentence adds value with no redundancy.

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 (4 params, no output schema), the description covers all necessary aspects: output types, when to use each, id behavior, and how to handle staleness. It is complete for an agent to invoke correctly.

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 description adds marginal value. However, it provides meaningful context for the raw parameter (usecases) and the tableId parameter (volatility and automatic resolution), which enriches understanding beyond the 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 retrieves detailed information about a specific table/rule, and distinguishes between parsed and raw modes. It explicitly contrasts with the update tool, differentiating itself from siblings.

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?

Provides guidance on when to use raw=true (unknown/custom table types) and notes that raw output cannot be used with openl_update_table. Also addresses staleness of tableId and recommends refresh via openl_list_tables(). Does not explicitly list all alternatives but gives sufficient context.

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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