memory_absorb

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

With no annotations, the description carries full burden. It details the step-by-step process including LLM classification and consolidation, and mentions dry_run for preview. However, it does not specify the exact actions taken for each relationship type (e.g., what 'update' entails) or potential side effects like overwriting existing memories.

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 concise and well-structured: a brief summary, followed by a process explanation, then a bulleted argument list. Every sentence adds value, and key information is front-loaded.

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's complexity (7 parameters, 1 required, no annotations, output schema present), the description is complete. It covers the core functionality, argument semantics, and behavioral details. Return values are not needed due to output schema.

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?

Each parameter is explained in the Args section, adding meaning beyond the input schema which has 0% description coverage. For example, source lists possible values, dry_run is described as a preview, and facts are noted to be mergeable. This provides clear guidance for an AI agent.

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?

Description clearly states 'Intelligently absorb facts into memory with dedup and consolidation' and explains the process of searching, classifying, and consolidating. It distinguishes from siblings like memory_create by emphasizing dedup and LLM-driven merging.

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 implies usage for absorbing facts that may overlap with existing memories, but does not explicitly state when to use this tool versus alternatives like memory_create_batch or memory_update. No exclusions or alternative names are provided.

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