twitter_tweet_getGet a single tweet by URL. Author includes profile metrics when not trimmed. Warning: trim=true omits author. Credits: 1.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Tweet permalink or identifier. | |
| trim | No | Optional: omit author profile and tweet `core` for a smaller response. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds behavioral details beyond the annotations: it notes that 'Author includes profile metrics when not trimmed' and warns that 'trim=true omits author.' It also discloses the credit cost ('Credits: 1'). These are useful caveats that help the agent set expectations about the response and resource usage, complementing the read-only and open-world hints.
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 brief and to the point, with the core purpose front-loaded in the first phrase. The additional details (author metrics, trim warning, credits) are appended concisely, ensuring each sentence adds value without redundancy.
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 simplicity of the tool (fetching a single tweet) and full schema coverage, the description covers the essential aspects: input via URL, conditional author inclusion, and credit cost. It does not detail the full response structure, but with no output schema and a straightforward operation, this level of detail is sufficient for an agent to invoke it correctly.
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 schema already describes both parameters, but the description adds nuance: it clarifies that setting trim=true omits the author entirely, which is more specific than the schema's 'omit author profile.' This extra detail helps the agent understand the impact of the trim parameter, enhancing the schema coverage.
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's function: 'Get a single tweet by URL.' It specifies the verb (Get), resource (single tweet), and the input method (by URL). This distinguishes it from sibling tweet tools like twitter_tweet_replies_list or twitter_tweet_transcript_get, which have different scopes.
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 provides clear context for when to use this tool—retrieving a single tweet via its URL. However, it does not explicitly point to alternative tools or state when not to use it, such as when needing replies or transcripts. The purpose is clear enough for an agent to select it by name and description, but the absence of explicit alternative guidance leaves some inference to the agent.
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.
Each tool is clearly scoped to a specific platform and action (e.g., facebook_post_get vs instagram_post_get). Descriptions explicitly differentiate similar tools across platforms, and within-a-platform tools like tiktok_search_videos_list vs tiktok_search_hashtag_list have clear disambiguation notes.
All 167 tools follow a strict `platform_resource_action` pattern (e.g., youtube_video_comments_list). No mixing of styles—snake_case throughout, with consistent verb ordering (get, list, search, etc.).
The server has 167 tools, which is far beyond the typical well-scoped range of 3-15. While the broad multi-platform scope justifies many tools, this extreme number makes the tool surface overwhelming and difficult for an agent to navigate efficiently.
The tool set covers a wide range of platforms and operations including profile retrieval, post/video fetching, comments, search, transcripts, and ad library access. Minor gaps exist (e.g., no Facebook events or LinkedIn messaging), but the surface is comprehensive for a read-only data aggregation use case.