memory_summarize

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

The description discloses that it uses an LLM and creates a single summary memory, but it does not explain whether the original memories are deleted, kept, or modified. Since no annotations are present, the description carries the full burden, and this missing side-effect information reduces 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?

The description is two concise sentences with no superfluous content. It is well-structured and immediately communicates the core functionality, making it easy for an agent to parse.

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?

The description sufficiently covers the main action and its parameters. No output schema exists, so no return value details are needed. It lacks a mention of error conditions or edge cases, but for the given complexity, it is reasonably 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?

The schema already provides descriptions for all four parameters, achieving 100% coverage. The description does not add any additional meaning or context beyond what the schema already states, so the baseline of 3 is appropriate.

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: summarizing old memories into a single summary memory, and compressing N interactions into 1 summary via LLM. This clearly differentiates it from siblings like memory_merge or memory_compile_context by focusing on summarization of old memories.

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 does not provide explicit guidance on when to use this tool versus alternatives. It does not mention conditions, exclusions, or comparisons with sibling tools such as memory_prune or memory_merge, leaving the agent to infer appropriate usage.

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