get_user_infoFetch basic profile info for a Twitter/X user by their screen name (handle). Returns user ID, display name, bio, follower/following counts, verified status, profile picture, banner, location, website, and account creation date. Use this as the starting point for any user analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| userName | Yes | Twitter/X screen name (handle) WITHOUT the leading @ sign. Example: 'elonmusk', not '@elonmusk'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds value by enumerating the exact fields returned (which is important since there is no output schema), but it does not disclose additional behaviors such as rate limits, error cases, or handle normalization.
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?
Two sentences, no waste. The first sentence front-loads the core purpose, the second lists return fields and gives usage direction. Every clause earns its place.
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?
For a single-parameter, read-only tool, the description is complete: it explains the input format via schema, lists return fields in the description, and gives usage context. The readOnly annotation covers safety, so nothing material is missing.
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%: the userName parameter is fully documented with the no-@ convention and an example. The description merely restates 'by their screen name,' adding no new parameter-level meaning. Baseline 3 applies.
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 action ('Fetch basic profile info'), the resource (a Twitter/X user by screen name), and lists the returned fields. It implicitly differentiates from siblings by emphasizing 'basic' profile info as the starting point, but it does not explicitly name a sibling like get_user_about to distinguish against.
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 instruction 'Use this as the starting point for any user analysis' provides explicit when-to-use context. It does not name alternatives or state when not to use it, but the starting-point guidance is a strong usage signal given the sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools cleanly target distinct resources: trends, replies, quotes, retweeters, followers, followings, mentions, and search. However, get_user_info and get_user_about overlap significantly, and get_user_last_tweets vs search_tweets have overlapping capabilities that are only separated by usage warnings.
All tools use a consistent snake_case convention with get_ as the dominant verb, followed by a clear noun target: get_trends, get_tweet_replies, get_user_followers, get_tweets_by_ids. search_tweets is the single intentional exception but follows the same predictable pattern.
12 tools is well-scoped for a read-only Twitter/X data-access server. Each tool covers a distinct major data surface—trends, individual tweet interactions, user profiles, relationships, mentions, and search—without unnecessary bloat.
The set covers the most common Twitter read workflows: user lookup, timelines, search with date ranges, followers/followings, mentions, and engagement metrics like replies, quotes, and retweeters. Obvious gaps like liking users or list-based lookups are missing, but they are not critical to the server's apparent core purpose.