discover_continue

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

With no annotations, the description must fully disclose side effects. It only mentions applying scores and emitting a file, failing to describe whether this overwrites existing files, requires prior steps, or has other side effects.

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 sentence, which is concise, but it omits critical information. It is not appropriately sized because it fails to provide necessary details beyond the bare action.

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 complexity (4 parameters, no schema descriptions, no annotations), the description is severely incomplete. An agent cannot correctly invoke this tool without additional context about parameters and behavior.

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 explain parameters. It does not mention any of the four parameters (cluster_slug, scored, threshold, auto_threshold), leaving their meaning and usage completely undefined.

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 action: applying AI scores and emitting a file. It specifies the output (papers_input.json) and its purpose (later ingest). However, it does not differentiate from sibling tools like discover_new or discover_clean, leaving some ambiguity about its exact role in the pipeline.

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?

No guidance is provided on when to use this tool versus alternatives. The description does not mention prerequisites, expected state, or any conditions under which this tool should or should not be invoked.

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