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openl-tablets

OpenL MCP Server

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openl Create Project Table

openl_create_project_table

Create a new table in an OpenL project module, including decision rules, spreadsheets, datatypes, or test tables. Provide the project ID, module name, and complete table structure.

Instructions

Create a new table/rule in OpenL project using BETA API (Create New Project Table). This is the recommended tool for creating new OpenL tables programmatically. Use cases: Create Rules (decision tables), Spreadsheet tables, Datatype definitions, Test tables, or other table types. Requires moduleName (existing project module name) and complete table structure (EditableTableView). The table structure must include at least tableType, kind, name, plus type-specific data (rules/headers for Rules tables, rows for Spreadsheet, fields for Datatype). id is optional for create requests. Use get_table() on an existing table as a reference for the structure. This tool uses the Create New Project Table (BETA) API endpoint.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectIdYesProject ID returned by backend. Use the exact 'projectId' value from openl_list_projects() response without modification or reformatting.
moduleNameYesName of an existing project module where the table will be created (for example, 'Rules').
sheetNameNoName of the sheet where the table will be created within the Excel file. If not provided, the table name will be used as the sheet name.
tableYesComplete table structure (EditableTableView). Must include at least tableType, kind, and name, plus type-specific data (for example rules/headers for Rules tables, rows for Spreadsheet, fields for Datatype). id is optional for create requests.
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
Behavior4/5

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

The description discloses it uses a BETA API, structural requirements, and suggests using get_table() as a reference. Annotations only include openWorldHint, so the description adds valuable behavioral context beyond that.

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 a concise paragraph with a clear front-loaded purpose sentence. Every sentence adds value without redundancy.

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?

The description covers purpose, usage, structural requirements, and BETA caveat. For a tool with 5 parameters, nested objects, and no output schema, it provides sufficient context, though error handling is not addressed.

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?

With 100% schema coverage, the baseline is 3. The description adds meaningful guidance on required table fields, optional id, and suggests using get_table() for reference, going beyond the schema's property descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it creates a new table/rule in OpenL project, with specific verb and resource. It lists use cases but does not explicitly differentiate from siblings like openl_update_table or openl_append_table, though the creation intent is unambiguous.

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 identifies itself as the recommended tool for creating OpenL tables programmatically and lists use cases. However, it does not provide explicit when-not-to-use guidance or alternative tools for other operations.

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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