get_v2_UserTweetsUser Tweets Group: User. Billing per call: 1 Credits.
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | Use the User By Screen Name endpoint to find the ID from a username. | |
| count | No | Number of results | |
| cursor | No | Cursor for other results |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It does mention a per-call credit cost, which is a small useful operational detail, but it fails to disclose auth requirements, rate limits, pagination behavior, or the exact scope of returned tweets.
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 short but under-specified. The only complete sentence is a billing note, while the main functional sentence merely restates the title. Conciseness at this level sacrifices all useful descriptive content.
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?
With no output schema, no annotations, and a description that omits return value details, whether it returns only authored tweets, and how it differs from the 'Tweets and Replies' sibling, the description is not contextually complete for the tool's ecosystem.
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?
The input schema describes all three parameters with 100% coverage, including usage hints for the user ID parameter. The tool description itself adds nothing about parameters, but baseline 3 is appropriate since the schema already carries the semantic load.
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 repeats the title 'User Tweets' and adds only grouping and billing information. It never explicitly states that the tool retrieves tweets for a user, making it a tautology rather than a clear functional description.
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?
There is no guidance about when to use this tool versus related tools such as get_v2_UserTweetsAndReplies, get_v2_UserMedia, or get_v2_Tweet. The description provides no context for selection or exclusion.
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.
Several tools appear to do the same thing, such as get_v1_1_Followers vs get_v2_Followers and get_v2_Tweet vs get_v2_TweetDetail. The descriptions are too brief to clarify differences, and multiple user lookup tools (get_v1_1_Users, get_v2_UserByRestId, etc.) create confusion.
Naming is inconsistent, mixing camelCase (get_ShortUrl), snake_case (get_email_search_by_username), and version prefixes with varying formats (get_v1_1 vs get_v2). No uniform verb_noun pattern is followed.
With 32 tools, the server is overloaded, especially given many redundant variations across API versions. The count exceeds the 25-tool threshold for too many tools, and many could be consolidated.
The server is entirely read-only (all tools are GET), missing write operations like posting tweets, following users, or sending direct messages. This is a significant gap for a Twitter server, and even read coverage has redundancies rather than comprehensive distinct endpoints.