mark_consolidated

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

With no annotations provided, the description carries the burden of disclosing behavioral traits. It clearly states side effects: 'The original episodes are demoted to cold (long-term storage) and linked to the new memory via a derived_from link.' This is a meaningful behavioral disclosure. It doesn't mention reversibility or permissions, but for the scope of this tool, the key side effects are covered.

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 slightly longer than ideal but each sentence serves a purpose: stating the main function, the recommended invocation sequence, the side effects, and an additional use case. It is front-loaded with the primary action and structured logically, making it easy to parse. A few words could be trimmed, but overall it is efficient.

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 two parameters, no annotations, and no output schema, the description is fairly complete. It covers the main workflow, side effects, and an alternative usage. The only notable gap is that the skill_candidates scenario implies a second use but does not clarify what new_memory_id should be in that context, leaving a minor ambiguity.

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 0%, so the description must compensate. It does by contextualizing the parameters: episode_ids are the 'original episodes' being demoted, and new_memory_id refers to the memory created via 'remember'. This adds meaning beyond the raw schema (array of strings, string) and helps the agent understand what values to provide.

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 primary function ('Record that a consolidation has been completed') and elaborates on the specific action of demoting original episodes to cold storage and linking to a new memory. It references consolidation_candidates and skill_candidates, which distinguishes it from sibling tools by clarifying its role in the consolidation workflow.

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 provides when to use this tool: 'Call this after the LLM has summarized a cluster surfaced by consolidation_candidates and created the new memory via remember.' It also mentions an alternative use case with skill_candidates, giving clear context for invocation. This directly guides the agent on the correct sequence.

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