consolidate_memories

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: the tool is not yet implemented (returns placeholder), uses LLM-driven merging, and resolves conflicts. However, it doesn't cover important aspects like permissions needed, rate limits, or error handling, leaving gaps for a mutation tool.

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 appropriately sized. It front-loads the purpose, includes implementation status, and lists parameters clearly. Every sentence adds value, though the placeholder note could be slightly more concise.

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 an output schema (returns are documented elsewhere) and 2 parameters with 0% schema coverage, the description is moderately complete. It covers purpose, status, and parameters but lacks details on behavioral aspects like permissions or error handling, which are important for a mutation tool with no annotations.

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 explains 'cluster_id' as 'Cluster ID to consolidate' and 'mode' with options 'dry_run' or 'apply,' adding meaningful context beyond the bare schema. However, it doesn't detail what a 'Cluster ID' represents or the implications of each mode, leaving some ambiguity.

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: 'Consolidate similar memories using LLM-driven merging.' It specifies the verb (consolidate), resource (memories), and method (LLM-driven merging). However, it doesn't explicitly differentiate from sibling tools like 'cluster_memories' or 'promote_memory,' which prevents a perfect score.

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 minimal usage guidance. It mentions the tool is 'NOT YET IMPLEMENTED' and currently returns a placeholder, which is useful context. However, it lacks explicit guidance on when to use this tool versus alternatives like 'cluster_memories' or 'promote_memory,' and doesn't specify prerequisites or exclusions.

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