log_observation

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 explaining behavioral impact. It discloses that the tool captures tool outputs, that observations will be 'compressed and summarized for future retrieval,' and that this builds session memory. This gives the agent a clear model of the write side effect and downstream behavior.

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 well-structured with sections and front-loaded intent, but it repeats the same instruction multiple times: 'Call this after significant tool executions' appears in the triggers, workflow position, and general guidance. Some redundancy could be trimmed without losing meaning.

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?

For a simple logging tool with no output schema and no annotations, the description covers purpose, parameters, trigger conditions, workflow position, and post-logging behavior. It does not mention the return value, but for a fire-and-forget observation logger this is a minor gap rather than a critical omission.

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%, but the PARAMETERS section compensates fully by defining every parameter in plain language. It explains session_id as coming from session_start, gives examples for tool_name, and clarifies what tool_input and tool_output represent. This adds meaning well beyond the bare schema titles and types.

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 opening sentence clearly identifies the verb and resource: 'Log an observation from tool execution to build session memory.' This distinguishes it from sibling retrieval tools like query_memory and search_memory, which read memory rather than write it. The purpose is explicit and actionable.

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 gives explicit trigger conditions: call when a tool execution produces important or surprising results, when you want to remember what happened, or when building context for a session summary. It also provides a negative guideline: 'Don't log every single tool call - only significant ones with novel insights.' It does not name alternative write tools, but the when/when-not guidance is strong.

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