get_tweet_retweetersFetch users who retweeted a specific tweet (the simple 'retweet' action, not quote-tweets — for those use get_tweet_quotes). Returns user profiles with metadata. Paginates via cursor (~100 per page).
| Name | Required | Description | Default |
|---|---|---|---|
| cursor | No | Pagination cursor; omit for first page (~100 per page). | |
| tweetId | Yes | Numeric ID of the tweet to fetch retweeters for. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only and open-world; the description adds useful behavioral context by stating that it returns user profiles with metadata and paginates via cursor at roughly 100 per page. It does not cover every edge case, but it meaningfully supplements the structured annotations.
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?
Three short sentences, all informative: core purpose, key exclusion/alternative, output shape, and pagination. No redundant phrasing; the most important behavioral distinction is front-loaded.
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 simple two-parameter read-only tool with full schema coverage, the description is nearly complete: it names the resource, output, pagination, and main alternative. The lack of an output schema is partially offset by 'Returns user profiles with metadata', though exact response fields are not specified.
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 tweetId and cursor. The description echoes the cursor pagination behavior but does not add significant new semantic detail beyond what is already present in the schema.
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?
Description uses a specific verb and resource ('Fetch users who retweeted a specific tweet') and explicitly distinguishes itself from the quote-tweet tool by name. An agent can immediately see what this tool does and how it differs from get_tweet_quotes.
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 clearly states the scope (simple retweet action), explicitly excludes quote-tweets, and names the alternative tool to use for that case. This gives the agent direct routing guidance with no ambiguity.
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.