memory_extract_learnings

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

The description discloses key behaviors: heuristic analysis, deduplication against existing memories, and optional auto-store. Since annotations only provide openWorldHint, this adds significant behavioral context beyond what annotations offer.

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 a single sentence that efficiently conveys the core functionality and key features, with no wasted words.

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 8 parameters and no output schema, the description covers the main action but does not explain what is returned (e.g., whether extracted learnings are returned or only stored). It adequately describes the process but leaves some ambiguity about output.

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 descriptions cover 100% of parameters with clear definitions. The tool description provides overall context but does not add additional meaning beyond the schema details, meeting the baseline for high coverage.

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 extracts decisions, patterns, error fixes, and conventions from a session transcript using heuristic analysis. It also mentions deduplication and optional auto-store, distinguishing it from siblings like memory_extract_entities or memory_ingest.

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 extracting learnings from transcripts but does not explicitly state when to use this tool over alternatives, nor does it provide exclusionary guidance. It is adequate but lacks context for tool selection.

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