get_user_replies

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 effectively describes the tool's behavior: it's a read-only fetch operation (implied by 'Fetch'), returns paginated results with specific increments of 30, and provides detailed information about the return format (UserAction objects with specific fields). The description doesn't mention rate limits, authentication requirements, or error conditions, but provides substantial behavioral context for a read operation.

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 and appropriately sized. It starts with the core purpose, then provides parameter details, return format, use cases, and pagination guidance. Every sentence adds value: the parameter section clarifies inputs, the return format section explains outputs, and the use cases provide practical guidance. No wasted words or redundant information.

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 that this is a read operation with no annotations, 100% schema coverage, and an output schema (implied by the detailed return format description), the description is complete. It covers purpose, parameters, return values, use cases, and pagination behavior. The output schema information in the description ('Returns a list of UserAction objects with...') compensates for the lack of formal output schema, making this description comprehensive for the tool's complexity.

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 fully documents both parameters. The description repeats the parameter information ('Args: username: The user's handle, offset: Pagination offset') without adding significant semantic value beyond what's in the schema. It does add the pagination increment detail ('in increments of 30'), which provides useful context but doesn't fundamentally change parameter understanding.

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 tool's purpose with specific verb ('Fetch') and resource ('replies/posts made by a user in other topics'), distinguishing it from sibling tools like get_user_topics (which likely gets topics created by a user) and get_user_actions (which might include broader actions beyond replies). The description explicitly mentions what makes this tool unique: focusing on user contributions across different topics.

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 explicit guidance on when to use this tool ('Use this to: See a user's contributions across topics, Find their data points and experiences, Evaluate the quality of their participation'). It distinguishes this from tools that might focus on a single topic (like get_topic_posts) or broader user actions (like get_user_actions), giving clear use cases without needing to explicitly list exclusions.

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