linkedin_post_transcript_getGet the transcript for a LinkedIn post video by URL.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Link to the LinkedIn post whose video transcript should be returned. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds minimal behavioral context beyond that—it doesn't describe response format, potential errors (e.g., if the video has no transcript), or any restrictions. For a read-only tool with strong annotation coverage, the bar is lower, but the description provides little extra behavioral transparency, so a 3 is appropriate.
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 a single, compact sentence that front-loads the core action and resource. Every word is meaningful; there is no filler or repetition. It efficiently conveys the tool's purpose without unnecessary detail.
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 tool with one well-documented parameter, no output schema, and readOnly/openWorld annotations, the description is nearly sufficient. It specifies the input (URL) and the expected result (transcript). The only minor omission is clarifying limitations (e.g., only works for posts with video transcripts), but this is inferable from the description and schema. Overall, it is complete enough for an agent to correctly select and invoke the tool.
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 100%: the 'url' parameter already has an explanatory description ('Link to the LinkedIn post whose video transcript should be returned'). The tool description adds only the phrase 'by URL', which is redundant with the schema. Since the schema fully documents the parameter, the description contributes no additional semantic value, matching the baseline of 3.
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 states a specific verb ('Get'), a specific resource ('transcript for a LinkedIn post video'), and the input mechanism ('by URL'). This clearly distinguishes it from the many sibling transcript tools for other platforms (e.g., facebook_post_transcript_get, youtube_video_transcript_get) since the platform is explicitly named. No ambiguity about what the tool does.
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 implies the use case: retrieving a transcript from a LinkedIn post video via its URL. It doesn't explicitly mention alternatives or exclusion scenarios, but the platform-specific nature of the tool (as reflected in the name and description) makes the context clear. Since there are many sibling transcript tools, an explicit 'use for LinkedIn posts' could be added, but it's already implicit. Slight gap in not stating that it only works for posts with video, but the description already includes 'video'.
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.