Train Knowledge Graph From File

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

Annotations already indicate a non-read-only, non-idempotent, open-world operation, but the description adds no behavioral detail. It does not disclose whether training appends to or replaces existing graph data, whether it is synchronous, or what side effects it has. The annotation openWorldHint=true is acknowledged, but the description fails to explain what 'train' actually does in practical terms.

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?

The description is a single concise sentence that front-loads the core action and target. It wastes no words and is appropriate for a tool that mostly relies on the schema for parameter details. However, the brevity also omits helpful context, which prevents a perfect score.

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?

For a tool with 6 parameters, no output schema, and complex behavior (training a knowledge graph), this description is far too minimal. It does not mention asynchronous behavior, return values, data size limits, or how to handle the rag_id. Given the extensive list of sibling tools, the lack of contextual guidance makes it hard for an agent to select and invoke this tool confidently.

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?

The schema covers 100% of the parameters with descriptions, so the baseline is 3. The tool description does not add any extra meaning beyond the schema; it only repeats 'base64-encoded' which is already in the schema. The description could contextualize how rag_id, filename, and file_content_base64 relate to each other, but it does not, so it stays at baseline.

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 ('Upload a file') and the target ('train the Neo4j knowledge graph'), naming both the input medium and the system. However, it does not differentiate this from sibling variants like lyzr_kg_ext_train_file_neo4j or lyzr_kg_train_text, so the purpose is clear but not uniquely positioned.

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?

No guidance is provided on when to use this tool versus alternatives such as lyzr_kg_train_text or lyzr_kg_ext_train_file_neo4j. The description offers no prerequisites (e.g., need an existing rag_id), no exclusions, and no mention of which scenario (file upload) makes this the appropriate choice. This is a significant gap given the many closely related sibling tools.

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