memory_add

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

With no annotations provided, the description carries full behavioral disclosure. It reveals the distillation pipeline for conversations, desensitization/reconciliation for single content, server-side auto-serialization for arrays, error behavior for invalid types, and default scope fallback. This is strong behavioral context beyond a simple 'write' action, though it stops short of describing return values or side effects.

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 compact, logically organized paragraph with no filler. It front-loads the core action, then covers format, scope, and default behavior in sequence. Every sentence contributes essential information, making it highly 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?

Given no output schema and no annotations, the description covers purpose, key parameter semantics, and processing behavior well. The remaining gaps—such as the exact meaning of optional parameters like entry_id or session_id—are minor because those parameters are either inferable or have defaults. The description is close to complete for an agent to successfully call the tool.

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 input schema has zero parameter descriptions, so the description must compensate. It explains the two most complex parameters: conversation_json (JSON string or array, auto-serialization) and scope (global/repo/agent conventions), and mentions content processing. Other parameters like memory_type, confidence, and source remain undocumented, but their names and defaults make them less ambiguous. The description covers the parameters that truly need clarification.

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 writes memory, using the specific verb '写入记忆' (write memory), and distinguishes it from sibling tools like memory_update and memory_forget by nature. It also explains the processing pipeline for conversation vs single content, which adds further specificity beyond a generic 'write' operation.

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 concrete guidance on how to invoke the tool: recommended conversation_json format, explicit scope value conventions (global, repo:<name>, agent:<name>), and default fallback behavior. It does not explicitly state when to use this tool instead of alternative memory tools, but the detailed usage context is clear.

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