memory_absorb

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

No annotations are provided, so the description fully discloses behavior: searching for similar memories, LLM classification, and consolidation. It mentions the dry_run option and default confidence, but does not cover side effects like potential memory deletions.

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 (about 150 words) with a clear structure: summary paragraph followed by Args list. Every sentence adds value; no redundancy.

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, output schema exists), the description covers the core algorithm and parameter semantics well. Minor gaps exist (e.g., error handling, performance implications), but overall complete enough for effective use.

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?

The description compensates for 0% schema coverage with a thorough docstring explaining each parameter's purpose, defaults, and behavior (e.g., 'facts can be granular — related ones get merged'). This adds significant value beyond the schema.

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 the tool ingests facts into memory with deduplication and consolidation via LLM classification, distinguishing it from simpler tools like memory_create or memory_import by specifying the intelligent processing.

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 explains when to use the tool (to absorb facts) but does not explicitly contrast with siblings like memory_create_batch or memory_update. However, the detailed process implicitly guides usage decisions.

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