semantic_summarize_episode

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

With no annotations provided, the description carries the full behavioral disclosure burden. It clearly reveals that this tool has side effects (API cost, external LLM call) and sets an explicit precondition (confirm=true and exact api_cost_ack) before the external call occurs. This is useful transparency, though it does not cover all behavioral consequences such as caching, write effects, or failure modes.

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 a single front-loaded sentence with no filler, and it highlights the most critical operational caveat (cost and confirm/ack gating) early. It is appropriately concise, though slightly cryptic due to mixed-language jargon.

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 a 12-parameter tool with no annotations and only an output schema, the description is too thin to be complete. It explains the cost gate but does not clarify what input the tool expects beyond the ack, how to choose episode_ref, what configuration parameters like model/base_url/api_key_env do, or what the semantic summary result looks like.

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 adds meaning for confirm and api_cost_ack by identifying them as required gates for the external LLM call, but the other 10 parameters (force, model, base_url, podcast_id, api_key_env, episode_ref, allow_partial, chunk_seconds, max_segments_per_chunk) receive no semantic explanation beyond their raw schemas.

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 identifies the tool as an 'API-cost side-effect tool' that calls an external LLM when confirm=true and api_cost_ack match, but it never explicitly states that the tool summarizes an episode. The intended action is mostly inferable from the tool name and sibling context, making the purpose vague rather than fully specified.

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 no guidance on when to use this tool versus alternatives like summarize_episode_extractive or other workflow tools. It only implies caution due to cost, but does not state conditions for selection, exclusions, or recommended alternatives.

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