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openl Start Trace

openl_start_trace

Start asynchronous trace execution for a business rules table by providing input parameters or test ranges, then retrieve execution results with get_trace_nodes.

Instructions

Start trace execution for a table. Trace is asynchronous (returns 202 Accepted). For regular rules: provide inputJson with { params: {...}, runtimeContext?: {...} }. For test tables: use testRanges (e.g. '1-3,5'). After starting, use openl_get_trace_nodes to retrieve results; if you get 409 Conflict, trace is still running—wait and retry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectIdYesProject ID returned by backend. Use the exact 'projectId' value from openl_list_projects() response without modification or reformatting.
tableIdYesTable ID to trace (e.g., 'calculatePremium_1234'). Get from openl_list_tables().
testRangesNoFor test tables: comma-separated ranges (e.g., '1-3,5'). Omit for regular rule/table execution.
fromModuleNoModule name for opened module execution. Usually omit.
inputJsonNoFor regular rules: JSON input. Use object with params (required) and runtimeContext (optional). E.g. { params: { age: 25 }, runtimeContext: { lob: 'Auto' } }.
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?

Discloses asynchronous behavior (returns 202 Accepted) and potential 409 Conflict when trace is still running. Provides details on input formats and post-use steps. No annotations provided, so description fully carries behavioral info.

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?

Three concise sentences. First sentence states purpose, second explains parameter usage, third gives follow-up instructions. No unnecessary information; front-loaded and well-structured.

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?

Covers essential aspects: what it does, async behavior, parameter guidance, and post-use actions. No output schema, but description appropriately explains output behavior (202, 409). Minor omission of trace purpose.

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% with good descriptions. Description adds value by clarifying usage scenarios for testRanges and inputJson, and giving examples. Enhances understanding beyond the schema alone.

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?

Clearly states it starts a trace execution for a table. Distinguishes between regular rules and test tables with specific parameter guidance. Differentiates from sibling tools like openl_get_trace_nodes and openl_cancel_trace.

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 explicit instructions on when to use inputJson versus testRanges. Recommends using openl_get_trace_nodes to retrieve results and explains how to handle 409 Conflict. Could mention prerequisites (e.g., project must be open) but overall clear.

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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