elfatwitterintelligenceagent_search_account

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

With no annotations, the description carries full burden. It discloses key behavioral traits: data comes from ELFA API, analyzes several weeks of historical activity, and limits to influential users' tweets. However, it omits critical details like rate limits, authentication needs, error handling, or whether it's read-only/destructive.

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 moderately concise but could be more front-loaded. It starts with core purpose but includes some redundancy (e.g., 'both mention search and account statistics' could be streamlined). Every sentence adds value, but structure could better highlight key constraints upfront.

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 and no output schema, the description partially compensates by detailing outputs (metrics, topics, crypto discussions) and data sources. However, it lacks information on return format, pagination, error cases, or completeness of results, leaving gaps for a tool with 3 parameters and complex functionality.

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%, providing clear parameter documentation. The description adds no additional parameter semantics beyond what's in the schema, such as explaining how 'days_ago' interacts with 'influential users' filtering or default value implications.

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 searches for a Twitter account and provides specific outputs: engagement metrics, follower growth, mentions by smart users, topics, and cryptocurrency discussions. It distinguishes from sibling 'elfatwitterintelligenceagent_search_mentions' by focusing on account analysis rather than mentions search, though the distinction could be more explicit.

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?

No explicit guidance on when to use this tool versus alternatives like 'elfatwitterintelligenceagent_search_mentions' or 'elfatwitterintelligenceagent_get_trending_tokens'. The description mentions data sources and scope (influential users, historical weeks), but lacks clear when-to-use or when-not-to-use directives.

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