get_remix_resultReturns the finished remix for a remix_post job: adapted title, description, script segments, why-it-went-viral analysis (whyItWorks) and checklist. Free to call. Poll get_job_status until the job reports completed, then fetch here. The result is also viewable in the web app under Content Ideas. Cost: free.
| Name | Required | Description | Default |
|---|---|---|---|
| jobRef | Yes | The pipeline jobRef returned by remix_post |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the bar is lower. The description goes further by disclosing the job-lifecycle requirement, the exact result contents, an alternate access path, and cost. This adds meaningful behavioral context beyond the annotations without contradiction.
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 concise and front-loaded, with the core result and contents in the first sentence. The only minor issue is redundancy: 'Free to call' and 'Cost: free' communicate the same fact twice, preventing a perfect score.
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 read-only tool with no output schema and strong annotations, the description is complete: it explains what is returned, the polling prerequisite, the web app alternative, and cost. No critical usage context is missing for an agent to correctly select and invoke this 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 coverage is 100% and the sole parameter, jobRef, is already described as 'The pipeline jobRef returned by remix_post.' The description restates that it is for a remix_post job but does not add meaningful new semantics or format details beyond the schema. Baseline 3 is appropriate.
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 uses a specific verb+resource pattern: 'Returns the finished remix for a remix_post job' and enumerates the exact output contents (adapted title, description, script segments, whyItWorks, checklist). This clearly distinguishes it from sibling tools like get_job_status, which tracks job progress rather than the finished result.
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?
It explicitly names the prerequisite workflow: 'Poll get_job_status until the job reports completed, then fetch here.' This gives the agent a clear when-to-use instruction and identifies the alternative tool by name. It also notes the result is viewable in the web app, adding useful context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Most tools have clearly distinct purposes (search, profile management, post retrieval, async jobs, billing). Some overlap exists among get_trending_outliers, niche_trends, and search_outliers, but their descriptions differentiate free/unauthenticated, niche-specific, and filtered search, so an agent can usually pick correctly.
The vast majority of tool names follow a verb_noun pattern (crawl_profile, get_post, remix_post, track_profile). The main exception is 'niche_trends', which is a noun phrase, and the minor spelling of 'topup' instead of 'top_up'. Otherwise, naming is consistent enough to predict tool behavior.
At 21 tools, this is slightly above the typical 3-15 range, but the broad scope (search, crawling, tracking, media handling, transcripts, remixing, billing) justifies the number. Each tool serves a distinct function, and none feel redundant, so the count is reasonable for the domain.
The tool set covers the core workflow well: searching outliers, crawling/tracking profiles, fetching posts/media/transcripts, and generating remixes. However, there is no way to list all posts for a specific profile (only 'recent tracked posts' via get_profile and an incremental feed via get_tracked_updates), which is a notable gap for deep creator analysis.