query_on_demand_data

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

With no annotations, the description carries the full burden and excels at it. It discloses default date behavior (last 24 hours), limit range and default, keyword_mode semantics, return envelope contents, UTC timestamps, and platform-specific constraints such as username filtering being unavailable for Reddit. No behavioral contradictions or hidden surprises remain.

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-organized and front-loaded with the most important constraint, but it is somewhat repetitive: the 'Default Behavior' section restates date defaults already given under start_date and end_date, and the examples are more numerous than strictly necessary. Still, each section has a clear purpose and the overall structure aids comprehension.

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 moderate complexity, 7 parameters, no annotations, and 0% schema coverage, the description provides everything needed for correct invocation: required values, supported platforms, parameter semantics, default behavior, return format, and platform-specific limitations. The presence of an output schema further reduces the need to document return values in detail.

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 0%, but the description fully compensates by explaining every parameter: accepted values for source, username limits and @ handling, keyword interpretation per platform, date formats and defaults, limit range, and keyword_mode options. This goes far beyond the bare schema definitions.

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 identifies a specific action ('Fetch real-time social media data') and a specific resource ('X and Reddit through the Macrocosmos SN13 network'), and clarifies the exact data platforms involved. It is clearly distinguishable from the sibling tools, which all concern task/dataset creation and cancellation.

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 gives strong usage context: required source values, case sensitivity, platform-specific capabilities, and multiple concrete invocation examples. It does not explicitly contrast this tool with alternatives, but the sibling tools are unrelated in function, so this is not a significant gap.

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