store_memory

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

No annotations provided, so description carries full burden. It explains the tool stores text as semantic memory with persistence across sessions. Adds content quality guidance. Does not disclose output format, error handling, or storage limits, but for a store tool it provides reasonable transparency.

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?

Concise description with clear main sentence, followed by bulleted usage examples and a best practice note. No unnecessary words or repetition. Front-loaded with purpose.

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 simplicity (3 parameters, required 1), description covers purpose, usage guidance, and parameter details adequately. Output is not described, but output schema likely covers that. Missing potential constraints like storage limits, but overall complete for the context.

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 coverage is 100%, baseline 3. Description adds value: explains content should be focused; metadata is optional free-text or JSON, stored verbatim and not searched; project_tag is a logical namespace defaulting to global. These details enhance schema descriptions.

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?

Clearly states 'Store a piece of text as a semantic memory in the local vector database,' defining verb and resource. Distinguishes itself from siblings 'search_memory' and 'wipe_project_memories' as the creation tool.

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?

Explicitly says when to call: when user asks to remember something or when context should survive the session. Lists concrete examples (decisions, preferences, file paths, summaries). Provides best practice (one focused idea). Doesn't explicitly state when not to use, but guidance is strong.

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