check_replies

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

Annotations already flag readOnlyHint=false and destructiveHint=true, and the description adds useful behavioral detail about sentiment classification and hot-lead surfacing. However, it does not disclose what destructive or state-changing behavior may occur—'conversation management' is vague and leaves the destructive hint unexplained. It does not contradict the annotations, but it undersells them.

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 first two sentences are front-loaded and informative. The third sentence, 'Handles inbox monitoring, lead response tracking, and conversation management,' repeats earlier ideas and introduces vague, low-actionability language. It is not overly long, but not every sentence earns its place.

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 zero-parameter tool with an output schema, the description covers the primary purpose, scope, and key outputs. The main gap is clarifying the destructive consequences implied by destructiveHint=true; without that, an agent might assume this is a passive read-only checker. More explicit mention of side effects would make it complete.

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 input schema has zero properties, so schema coverage is trivially 100% and there is no parameter burden for the description to carry. The tool takes no arguments, so a baseline of 4 is appropriate.

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 opening sentence clearly states a specific verb ('Check') and resource ('new LinkedIn replies across all campaigns'). It also lists tangible outputs like sentiment classification and hot leads, so an agent can recognize this as an inbound-monitoring tool. It does not explicitly differentiate from sibling tools such as inbox or backfill_inbox, so it misses the top score.

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 the tool is for monitoring new messages and surfacing leads needing attention, which gives reasonable context. However, it never states when to prefer this tool over alternative siblings, nor does it mention exclusions or prerequisites. The usage guidance is implied rather than explicit.

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