get_user_usernames_to_idsThis endpoint receives a list of usernames and converts up to 100 of them at the same time to ids. Group: ✨ Exclusive endpoints. Billing per call: 1 Credits.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the batch limit and billing cost, but omits critical behavioral details such as the input format, return mapping (order vs. object), handling of invalid usernames, and any rate limits. This is a significant gap.
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 a single sentence that is efficient and to the point. It is front-loaded with the primary action and the limit. However, the title contains marketing fluff ('✨ EXCLUSIVE!') and the group/billing info could be separated, but the overall structure is acceptable.
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 minimal schema, no output schema, and no annotations, the description is insufficient for an agent to reliably use the tool. It lacks input specification, output structure, and error semantics, making it incomplete for a batch conversion operation.
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?
Although there are 0 declared parameters in the schema, the description mentions an implicit input (list of usernames) without specifying how the agent should supply it (e.g., parameter name, format, encoding). The description adds conceptual meaning but fails to compensate for the empty schema, leaving invocation ambiguous.
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 converts a list of usernames to IDs, with a specific limit of up to 100 at a time. This distinguishes it from singular sibling tools like get_username_to_id_username, making the purpose unambiguous.
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 usage for batch conversions of multiple usernames but does not explicitly contrast with the singular alternative or specify when not to use it. The context is clear but lacks exclusionary 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.