agentmem

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions token budgeting and relevance ranking, which are useful behavioral traits. However, it doesn't cover critical aspects like whether this is a read-only operation (implied but not stated), potential rate limits, authentication requirements, or how 'most relevant' is determined. For a tool with zero annotation coverage, this leaves significant gaps.

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 extremely concise—just two sentences—with zero wasted words. The first sentence states the purpose, and the second provides usage guidance. It's front-loaded with essential information and efficiently structured.

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 complexity (memory retrieval with token budgeting), lack of annotations, and no output schema, the description is minimally adequate. It covers the core purpose and usage timing but omits details on behavior, parameter specifics, and return values. For a tool with 2 parameters and no structured support, it should do more to be fully complete.

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 description coverage is 0%, so the description must compensate. It implies the 'query' parameter is for the 'topic' and 'max_tokens' controls the 'token budget', adding some semantic meaning. However, it doesn't fully explain parameter purposes, constraints, or interactions (e.g., how token budgeting affects result selection). The description provides partial compensation but not enough to overcome the low schema coverage completely.

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's purpose: 'Get the most relevant memories for a topic, fitted to a token budget.' This specifies the verb ('Get'), resource ('memories'), and scope ('most relevant...fitted to a token budget'), making it easy to understand. However, it doesn't explicitly differentiate from siblings like 'search_memory' or 'list_memories', which prevents a perfect score.

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 clear usage context: 'Use at the start of a task to load context.' This gives practical guidance on when to invoke the tool. However, it doesn't specify when NOT to use it or name alternatives among the many sibling tools (e.g., when to use 'search_memory' instead), which would be needed for a score of 5.

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