backlog_recall

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

Without annotations, the description carries the full burden. It discloses that recall returns short digest records captured at task completion or artifact creation, and that they point back to entities via metadata.entity_id. This adequately describes the read-only nature and data origin, though it doesn't specify rate limits or authorization requirements.

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 three sentences, uses bold for key terms, and provides examples. Every sentence adds value without redundancy. It is efficiently structured and easy to parse.

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 five parameters and no output schema, the description covers the purpose and parameter semantics well. However, it does not describe the return structure beyond mentioning metadata.entity_id, which might leave an agent uncertain about the full output format. It is nearly complete but slightly lacking in return details.

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 valuable context: explains 'context' as a parent_id scope, clarifies 'tags' as any-match with examples, and describes 'layers' with default and current state ('only Phase 3'). This goes beyond the schema 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 recalls episodic memories (short digest records) and distinguishes it from backlog_search, which queries live entities. It provides specific use cases like 'what did I finish recently about X?' and 'what artifacts live under FLDR-0001?', making the purpose unambiguous.

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 explicitly contrasts with backlog_search (live entities) and gives concrete examples of when to use it ('what did I finish recently about X?'). It also explains that results reference entities via metadata.entity_id, providing clear context for appropriate usage.

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