lbb_rdf

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

Annotations already mark the tool as non-read-only, non-open-world, idempotent, and destructive. The description goes well beyond these by explaining the permanent storage-mode selection on first write, refusal of later property-graph commits, retry deduplication behavior, and scheduled publication after a completed write. No contradiction with annotations exists; the destructiveHint is consistent with irreversible storage-mode selection.

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 dense but every sentence carries unique information. It is front-loaded with the core purpose and action variants, then systematically covers constraints, storage implications, retries, and publication. No filler or repetition of schema details.

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 complex tool with 10 parameters and no output schema, this description is unusually complete. It explains the two execution modes, supported formats, unsupported operations, named-graph restrictions, storage-mode implications, retry semantics, and the publication workflow. The only minor omission is detailed return-value structure, but the description directs users to inspect action=publication and verify with lbb_query.

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?

Schema coverage is 70%, and the description substantially compensates: it explains that source preserves OWL axioms and external IRIs as graph facts, that update is submitted unchanged and fails without mutation for unsupported SPARQL operations, that dataset formats must contain only default-graph quads, and that blank_node_scope provides stable labels across import chunks. This adds significant meaning beyond the schema's terse field descriptions.

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 tool stores and extends complete RDF/OWL documents, and explicitly distinguishes the two actions: import accepts RDF serializations without conversion, while update executes SPARQL INSERT DATA for additive edits. This clearly separates it from sibling tools like lbb_configure, which handles native schema metadata.

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 gives explicit when-to-use and when-not-to-use guidance: replacing/removing axioms is unsupported and users should use a new versioned LBB graph instead. It also warns about named-graph limitations, storage mode selection before bootstrap, and retries deduplication, and distinguishes these graph facts from lbb_configure's metadata.

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