Train Knowledge Graph from Text (Async)

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

The description discloses the async execution model and the return of a task id for polling, which goes beyond the annotations (readOnlyHint=false). It adds meaningful behavioral context not present in structured metadata. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Single 19-word sentence that front-loads the primary action and includes critical details (async, task id). Every word earns its place with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has no output schema, the description covers the essential return type (task id) and the async behavior. It doesn't mention how to poll, but the sibling lyzr_kg_task_status tool exists. The description is adequate for a straightforward async ingestion tool.

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

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

All 6 parameters have schema descriptions (100% coverage), so the baseline is 3. The tool description does not add any parameter-specific meaning, but the schema descriptions are sufficient for understanding the parameters.

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

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

The description clearly states the action: 'Ingest text into the v4 knowledge graph as an async task'. It identifies the specific resource (v4 knowledge graph) and distinguishes from sibling tools like lyzr_kg_train_text by noting the async nature and return of a task id.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description establishes that this is the async variant ('as an async task') and that the caller must poll the returned task id. This clearly signals when to use this tool, though it does not explicitly mention alternatives or when-not-to-use. The context is clear but exclusions are absent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.