Metis — Capture Observation

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

No annotations are provided, so the description must disclose behavioral traits. It mentions recall in future sessions, implying persistence, but does not address side effects, mutability, authorization needs, or error behavior. This is a significant gap given the absence of annotations.

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 a clear purpose, followed by parameter details. It is concise without fluff, though the parameter list is somewhat lengthy. Front-loading the purpose aids quick understanding.

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 output schema exists, return values are covered. The description explains what the tool does and its parameters adequately. However, it lacks details on error handling, rate limits, or required permissions, leaving some context gaps for an agent.

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?

With 0% schema description coverage, the description fully compensates by explaining each parameter's purpose, expected format (e.g., content: 1–3 sentences), and default behavior (e.g., concepts auto-extracted). This adds meaning beyond the raw schema.

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 records typed observations during an agent run, with specific observation types (discovery, decision, etc.) and the purpose of enabling recall in future sessions. This distinguishes it from sibling tools like add_memory_entry or add_journal_entry.

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?

It advises using the tool throughout a run to capture learning, but does not explicitly state when not to use it or suggest alternatives like add_memory_entry or add_journal_entry. The guidance is implied but lacks explicit exclusions.

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