llm_summarize

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

With no annotations, the description carries the full burden for behavioral disclosure. It states only that the tool compresses content, without revealing whether it is read-only, how it handles large inputs, what the output format is, or whether there are side effects such as external LLM calls.

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, focused sentence with no filler. It front-loads the action and lists concrete targets, earning its place with every word.

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 two-parameter tool, the description is mostly sufficient for basic invocation, and the schema covers parameter meanings. However, the absence of any return-format or behavioral details, combined with no annotations or output schema, leaves some gaps for an agent deciding whether this tool fits a workflow.

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 100%, so the schema already documents both parameters clearly. The description adds general context about the type of content (tool outputs, logs, results) but no additional detail about how maxWords behaves beyond the schema's stated default.

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 uses a specific verb ('Compress') with clear targets ('verbose tool outputs, logs, or multi-step execution results'), making the tool's function clear. It does not explicitly contrast itself with sibling tools like llm_query or llm_decompose_task, so it stops short of full differentiation.

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 implies when to use the tool—whenever there is verbose output or logs to condense—but gives no explicit 'when not to use' guidance or comparison to alternatives. The context is inferable but not explicitly stated.

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