gitlab_get_user_resolved_threads

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what the tool returns (resolved thread information with details like timeline, impact, etc.), which is helpful. However, it lacks critical behavioral information such as whether this is a read-only operation (implied but not stated), pagination behavior (mentioned in parameters but not explained in description), rate limits, authentication requirements, or error conditions. The description adds value but leaves significant gaps.

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 well-structured with sections for purpose, returns, use cases, parameters, and an example. Most sentences earn their place by adding value. However, it could be more front-loaded—the core purpose is clear early, but the detailed return list and use cases could be trimmed or integrated more tightly. The parameter list is somewhat redundant with the schema but serves as a quick reference.

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 no annotations, no output schema, and 6 parameters with full schema coverage, the description is moderately complete. It explains what the tool does and what it returns, but lacks behavioral details like pagination handling, error responses, or performance considerations. The use cases help contextualize, but for a tool with multiple parameters and no structured output, more guidance on result interpretation or limitations would improve completeness.

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 all parameters thoroughly. The description lists parameters with brief explanations (e.g., 'user_id: Numeric user ID'), but these add minimal value beyond what's in the schema. It does clarify that 'username' and 'user_id' are alternatives ('use either user_id or username'), which is useful context not in the schema. However, parameters like 'resolution_type' and 'context_type' are listed without enum values or detailed semantics, so the description doesn't fully compensate for schema limitations.

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 specific action ('Get threads resolved by a user in reviews') and resource ('discussion threads'), distinguishing it from siblings like gitlab_get_user_discussion_threads (which likely gets all threads) or gitlab_get_user_resolved_issues (which focuses on issues rather than threads). The verb 'Get' combined with the qualifier 'resolved by a user' makes the purpose unambiguous.

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 clear context for when to use this tool ('code review effectiveness tracking', 'collaboration quality assessment', etc.), but does not explicitly state when NOT to use it or name specific alternatives among the sibling tools. For example, it doesn't contrast with gitlab_get_user_discussion_threads or gitlab_get_user_resolved_issues, which could help the agent choose more precisely.

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