User By Screen Name
get_v2_UserByScreenNameUser By Screen Name Group: User. Billing per call: 1 Credits.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| username | No |
get_v2_UserByScreenNameUser By Screen Name Group: User. Billing per call: 1 Credits.
| Name | Required | Description | Default |
|---|---|---|---|
| username | No |
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, placing the full burden on the description to disclose behavior. The description only mentions a billing cost and a group label—it fails to disclose rate limits, required authentication, error behavior, or the response shape. Nothing here helps an agent predict side effects or operational constraints.
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 rather than concise; it wastes its few words on a tautological phrase ('User By Screen Name') and adds only a billing hint. Like the 'Process' calibration example, this is under-specification, not effective brevity.
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?
Although the tool has only one optional parameter and no nested objects, the description still leaves critical gaps: it never confirms whether this is a read/list operation, what happens if the username is not found, or any output expectations. With no annotations or output schema, the description needed to carry more weight and falls short.
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 adds zero parameter documentation. The only hint is the schema itself ('username' with example 'elonmusk'), which is self-explanatory but still benefits from context (e.g., format, whether it accepts '@' prefix, case sensitivity). The description misses the opportunity to add value beyond 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?
The description merely restates the tool's name ('User By Screen Name' matches the title exactly) without a verb phrase like 'retrieves' or 'looks up.' The additional text 'Group: User' and 'Billing per call: 1 Credits' provides no functional purpose. This qualifies as a tautology under the rubric.
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?
No guidance is given for when to use this tool versus siblings like get_v2_UserByRestId or get_v2_UsersByRestIds. There is no mention of exclusions, prerequisites, or alternative approaches. The description silently implies usage via the name, but never states it.
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.