lc_get_cases_dashboard_counts

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

No annotations are provided, so the description carries full burden. It does not disclose read-only nature, rate limits, or what the counts represent. The minimal description adds no behavioral context beyond the name.

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 very short (one sentence), which is concise, but lacks substance. It sacrifices necessary detail for brevity, making it insufficiently informative.

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?

With one parameter, no output schema, and no annotations, the description is incomplete. The agent cannot understand the return format, what counts are included, or how to use the output. More detail is needed for a complete understanding.

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 single parameter 'oid' is solely named; its purpose is unclear (org ID? case ID?). Schema coverage is 0%, and the description provides no additional meaning. The agent cannot infer what value to provide.

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 states it gets dashboard counts for cases, which is a specific purpose. It distinguishes from siblings like lc_get_case and lc_list_cases by implying aggregated counts. However, it could be more precise about what 'dashboard counts' entails.

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 on when to use this tool vs alternatives. Among many get/list siblings, the description does not specify context or prerequisites, leaving the agent without decision support.

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