lbb_observe

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

The description adds significant behavioral context beyond the minimal annotations (readOnlyHint=false, etc.). It explains the branching model, the anchoring vs. needs_review outcomes, the auto-merge with SHACL validation, and the server flag dependency. 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?

The description is well-structured and front-loaded with the main purpose. All sentences add value, though some technical details (e.g., auto_merge behavior) could be slightly more streamlined. It is appropriately sized for the tool's complexity.

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 9 parameters and no output schema, the description covers the core logic (anchoring, confidence, auto-merge) and mentions the server flag. However, it does not describe the return format or error conditions, leaving agents to infer the response structure.

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 description coverage is 100%, providing baseline parameter documentation. The description adds interpretive context (e.g., 'omit with extract:false to store the episode only', 'confidence >= 0.8 to mint new entities'), but does not fully explain all parameters beyond what the schema already states.

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's purpose: storing conversation turns verbatim as an EPISODE evidence entity and anchoring/gating facts on an observe branch. It uses specific verbs ('remember', 'store', 'anchor', 'gate') and distinguishes it from siblings by highlighting the observe branch and the fact anchoring behavior.

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 provides context on when to use the tool (for adding facts without poisoning the main graph) and explains the anchoring logic and confidence threshold. However, it lacks explicit 'when not to use' or direct comparisons to sibling tools like lbb_commit or lbb_ground.

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