Translate Tweet
get_v1_1_TranslateTweetTranslate Tweet Group: Tweet. Billing per call: 1 Credits.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | ||
| language | No | Language code |
get_v1_1_TranslateTweetTranslate Tweet Group: Tweet. Billing per call: 1 Credits.
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | ||
| language | No | Language code |
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, but it only mentions billing credits. It does not describe output format, error behavior, rate limits, or any other observable behavior.
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 (two sentences), but one sentence is redundant ('Translate Tweet Group: Tweet') and the other provides only billing information. This is under-specification rather than purposeful conciseness.
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 annotations, no output schema, and a sparse description, the tool is undercontextualized. The core action is inferable from the title, but input/output behavior, language handling, and limitations are missing.
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 tool description adds no meaning beyond the schema's titles/examples for 'id' or 'language'. With schema description coverage at 50%, the description does nothing to clarify that 'id' is the tweet to translate or how language codes should be specified.
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 essentially restates the title ('Translate Tweet') and adds only a group label ('Group: Tweet') and billing info. It does not provide a specific verb+resource statement or differentiate from sibling tools like get_v1_1_TranslateProfile.
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 on when to use this tool versus alternatives. The description does not mention that this tool targets a specific tweet (vs a profile), nor does it state prerequisites or preferred context.
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.