count_issues

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

The annotations already disclose that the tool is read-only, idempotent, and non-destructive, so the description need not restate those traits. It does add useful context by specifying the operation returns a count of matching issues rather than the issues themselves, and that the scope is defined by the given query. This is a meaningful behavioral trait beyond annotations, though it does not mention how instance selection or query limitations affect the count.

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 extremely concise and well-structured. The main purpose is captured in a single front-loaded sentence, followed by a compact 'Args:' block. There is no redundant or filler content — every line provides directly actionable information for invoking the tool. This is an appropriate level of detail for such a straightforward operation.

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 tool's low complexity, the read-only and idempotent annotations, and the presence of an output schema, the description is largely complete for a successful invocation. The one element that could improve completeness is a note about the relationship between this tool and sibling search/list tools, plus a brief clarification on how to specify the 'instance' parameter. Apart from those points, the agent has enough information to call count_issues correctly.

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 no descriptions (0% coverage), so the description must compensate. The 'Args:' block does add some semantic value: it explains that 'query' is a YouTrack search query and that 'instance' is an optional YouTrack instance. However, these descriptions are minimal — they do not specify the expected format of the instance identifier, nor do they give examples of valid query syntax, leaving gaps that an agent must infer or learn from elsewhere.

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 the specific verb 'Count' against a well-defined resource ('issues') and clearly scopes it with 'matching a YouTrack query.' This makes it immediately distinguishable from sibling tools like search_issues or get_issues, whose purpose would be to retrieve lists or individual issues. The phrase leaves zero ambiguity about the intended operation.

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 provides no explicit guidance on when to use this tool versus alternatives. It does not mention that you should use this instead of search_issues or get_issues when only a count is needed, nor does it note any situations where the tool would be inappropriate. The intended usage is implied by the verb 'Count,' but no direct or alternative-routing guidance is given.

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