lbb_decode

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

The description discloses key behaviors: database narrowing, model invocation with constraints, and type recovery. Annotations declare readOnlyHint=true, and the description does not contradict this; it adds detail about the model's vocabulary constraint. No mention of side effects, which aligns with read-only nature.

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 four sentences, front-loading the purpose. While informative, it contains some technical detail (e.g., 'mode: forced') that might be slightly dense. Every sentence earns its place, but it could be trimmed without losing clarity.

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?

The description covers the main return fields (relation, candidates, signature_forced) and explains the two modes. Without an output schema, this is sufficient for an agent to understand what the tool provides. However, it does not describe how the 'detail' parameter affects output, leaving a minor gap.

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 100%, so baseline is 3. The description adds meaning beyond the schema by explaining the interplay between source/target types and the optionality of providing them (e.g., 'omit to have the DB recover it from the name'). It also describes the 'use_model_when_forced' parameter's effect. This adds integration context.

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 starts with a specific verb and resource: 'Name the relation between two entities.' It further clarifies its use cases: 'fill a missing edge label, verify a relationship, or assemble structured triples.' This clearly distinguishes it from siblings like lbb_query or lbb_search.

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 explains the two modes (forced vs model_narrowed) and when to omit types. It provides clear context for usage: to fill edge labels or verify relationships. However, it does not explicitly state when not to use it or name alternatives, though the sibling list implies distinction.

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