distill_session

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

No annotations provided, so the description carries full burden. It discloses that the tool runs a background job, summarizes on a cheap coding model, and saves facts to knowledge memory. It does not mention potential side effects (e.g., overwriting), but overall provides sufficient behavioral context for a safe read-like operation.

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 concise and well-structured: a one-sentence summary, followed by details on the background job and parameter. No redundant sentences; every part adds value.

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 (1 parameter) and the presence of an output schema (context signal), the description covers all necessary aspects: purpose, background job mechanism, output format, and parameter semantics. It is complete for an AI agent to select and invoke correctly.

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 description adds meaning beyond the input schema by explaining the session parameter as 'Name of a stored chat session.' Since schema coverage is 0%, this additional context is crucial and adequately clarifies the parameter's role.

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: 'Distill a chat session's stored history into durable knowledge facts.' This is a specific verb+resource combination. It distinguishes itself from sibling tools like remember_fact and forget_fact by focusing on summarizing chat sessions into facts.

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 explains that it submits a background job similar to research jobs and suggests polling with get_research_job. It also mentions the output format (3-8 facts with source='session:<name>'). While it doesn't explicitly state when not to use it, the context is clear enough for an AI 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.