check_socials

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 key traits: the tool checks availability across 10 platforms, returns a confidence indicator, and warns that some platforms (Instagram, LinkedIn, TikTok) have low confidence due to automated check blocks. It also clarifies default behavior for platforms when not specified. However, it does not mention rate limits, authentication needs, or error handling, leaving some 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 and front-loaded with the core purpose, followed by platform details and examples. Every sentence adds value: the first states the purpose, the second lists platforms with confidence levels, the third explains the return format, and the examples illustrate usage. There is no redundant or unnecessary 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 the complexity of checking multiple platforms with varying confidence levels and no output schema, the description is mostly complete. It covers the tool's purpose, supported platforms, confidence indicators, and usage examples. However, it lacks details on the return format structure, error scenarios, or how confidence levels are determined, which could be important for an agent to interpret results accurately.

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 schema description coverage is 100%, so the baseline is 3. The description adds significant value beyond the schema by explaining the platforms with confidence levels, providing examples of usage, and clarifying default behavior for the platforms parameter. This enhances understanding of how parameters affect the tool's operation, though it does not detail parameter interactions or edge cases.

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: 'Check if a username is available on social media and developer platforms.' It specifies the verb ('check'), resource ('username'), and scope ('social media and developer platforms'), distinguishing it from sibling tools like search_domain or suggest_domains which focus on domains rather than usernames.

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 usage by listing supported platforms with confidence levels, which helps the agent understand when to rely on results. However, it does not explicitly state when to use this tool versus alternatives like bulk_search or compare_registrars, nor does it mention any prerequisites or exclusions beyond the confidence warnings for certain platforms.

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