twitter_profile_getGet an X (Twitter) profile by handle. Returns lookupStatus plus follower/tweet metrics and account age signals.
| Name | Required | Description | Default |
|---|---|---|---|
| handle | Yes | X handle to look up, with or without a leading @. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds value by disclosing that the response includes lookupStatus, metrics, and age signals, which gives the agent expectations about the return structure. It does not go beyond annotations with details like rate limits or authentication, but given the simple read-only nature, this is sufficient.
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 two short sentences with no wasted words. The primary action is front-loaded, and the return details are added succinctly. It is concise and well-structured, allowing quick scanning by an agent.
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?
For a simple one-parameter tool with no output schema, the description adequately explains what it does and what the response contains (lookupStatus, metrics, age signals). There is no missing essential information for an agent to decide whether to invoke it and interpret the result. The openness hint and readonly hint cover behavioral aspects.
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 coverage is 100% for the handle parameter, and the schema already describes it as 'X handle to look up, with or without a leading @.' The description only repeats 'by handle' without adding further nuance, so it does not enhance the schema. Baseline 3 applies because the schema fully documents the parameter.
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 specific action (get), the resource (X/Twitter profile), and the lookup method (by handle). It also distinguishes itself from sibling profile tools on other platforms (e.g., bluesky_profile_get, instagram_profile_get) by naming the platform and specifying the return content (metrics and age signals). This is precise and 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 provides clear context about when to use the tool: when you need an X/Twitter profile by handle and want follower/tweet metrics and account age signals. However, it does not explicitly mention alternatives or when not to use it, such as pointing to a different tool for tweets or transcripts. This is adequate context without exclusions.
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.