get_user_userId_mediaReceive all media items ordered by most recent. You can use the cursor and count to navigate around. Group: User. Billing per call: 1 Credits.
| Name | Required | Description | Default |
|---|---|---|---|
| userId | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It does disclose useful behavior: ordering by recency, pagination/cursor navigation, user group, and billing cost. However, it does not clearly state read-only semantics, return shape, or what happens on invalid usernames/missing media, and the cursor/count reference is not reflected in the schema.
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 core description is short and front-loaded: the main purpose is stated first, navigation is captured in one sentence, and the group/billing metadata is compact. No words are wasted.
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?
The tool is simple, with only one official parameter, so the description does not need to be long. Still, because there is no output schema and no details on cursor/count, returned media structure, or edge cases, the description leaves meaningful gaps for an agent invoking the tool.
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%, and the description does not add detail about the only schema parameter, userId. It even introduces 'cursor' and 'count' as navigable concepts although they are absent from the input schema with additionalProperties false, which creates ambiguity for correct invocation.
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 states the resource plainly ('all media items') and the ordering rule ('most recent'); the tool name and title make the user scoping explicit. It is clearly distinct from sibling tools such as get_user_userId_tweets or get_user_userId_followings because it is the media-specific option.
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 implies when to use the tool: when media items are needed. It also explains cursor/count navigation, but it does not explicitly say when to choose this over siblings such as get_user_userId_tweets or get_user_userId_likes, nor does it give when-not-to-use guidance.
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 target distinct data types, but there is notable overlap: get_search_searchTerm vs post_search_searchTerm and get_tweet_tweetId vs post_tweet_tweetId are functional duplicates, and the three followings variants (get_user_userId_followings, _basic, _ids) have unclear boundaries. This creates ambiguity for an agent trying to select the right tool.
Tool names mix HTTP method prefixes (get_, post_) with resource_parameter structures, and camelCase is embedded in parameters (searchTerm, tweetId, tweetsAndReplies). The pattern is not uniform (e.g., get_user_by_username_username vs get_user_usernames_to_ids), making the naming feel ad hoc and hard to predict.
With 23 tools, the set is on the heavier side. Many tools are redundant (GET/POST duplicates) or overly granular (three followings endpoints), padding the count without adding distinct capabilities. A leaner set of 15-18 tools would be more appropriate for the read-only Twitter data scope.
The server covers the core read-only Twitter data surface well: user info, tweets, media, followers/followings, likes, search, trends, and miscellaneous utilities. The only notable gap is the lack of a direct endpoint for listing a user's followers' details (though that could be composed), and no ability to post or interact, which aligns with the apparent purpose.