render_creator_profileUse this when the user wants a visual deep dive on one canonical creator ID. It fetches the current creator record plus up to six recent posts and may consume Influship account or API usage.
| Name | Required | Description | Default |
|---|---|---|---|
| creator_id | Yes | Canonical Influship creator ID. |
| Name | Required | Description | Default |
|---|---|---|---|
| view | Yes | ||
| posts | Yes | ||
| creator | Yes | ||
| postsUnavailable | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With all annotation hints false, the description carries the burden and discloses key behavior: it fetches the current creator record and up to six recent posts, and it 'may consume Influship account or API usage.' This cost/resource warning is valuable beyond what annotations provide. It does not describe side effects or reversibility, but the operation is presented as a fetch/read, and the consumption warning 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?
Two sentences, front-loaded with the usage trigger ('Use this when...'). Every phrase adds value: the scope ('one canonical creator ID'), the data retrieved, and the cost warning. No fluff or repetition.
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 parameter and an existing output schema, the description explains the data scope (record + up to six posts) and the usage cost caveat. It gives enough context for an agent to decide appropriately. It could have mentioned how this differs from get_creator/get_profile, but the 'visual deep dive' phrasing and cost warning provide sufficient differentiation.
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 input schema has 100% coverage with a clear description for creator_id ('Canonical Influship creator ID.'). The tool description merely repeats 'canonical creator ID' without adding format details or constraints. Since the schema does the heavy lifting, a baseline of 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 clearly states a specific action: 'visual deep dive on one canonical creator ID' and describes what it fetches ('current creator record plus up to six recent posts'). It distinguishes itself from siblings like render_creator_comparison and render_creator_shortlist by focusing on a single creator and a visual/deep-dive format.
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 provides a clear when-to-use directive: 'Use this when the user wants a visual deep dive on one canonical creator ID.' It also implicitly differentiates from simpler lookup tools by flagging that it 'may consume Influship account or API usage,' suggesting it's for heavier, explicit requests. It does not name alternative tools or exclusions, but the context is clear.
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.
Multiple tools have unclear boundaries: `autocomplete_creators` and `search_creators` both claim the exact same example query ('Who is that fitness coach called Jane?') as their primary use case, creating direct routing conflicts. `get_creator` and `get_profile` also overlap heavily for exact platform+username lookups, with descriptions admitting the choice depends on whether 'profile metrics are the main need' — a thin distinction. `search_creators` further muddies things by dual-routing to legacy semantic search, making it a hybrid that competes with both `autocomplete_creators` and `semantic_search_creators`.
The naming follows a mostly consistent verb_noun snake_case pattern: `get_*` covers record fetching, with clear singular/batch pairs like `get_instagram_post`/`get_instagram_posts` and transcript variants. Minor deviations exist (`semantic_search_creators` prefixes a modifier, and `autocomplete_`, `find_`, `match_`, `lookup_`, `render_` each introduce different verbs), but the style is uniform and the verb typically reflects the operation type.
At 28 tools the server is heavy, but the scope is genuinely broad — three platform-specific data surfaces (Instagram, TikTok, YouTube), each requiring profile/video/transcript/listing operations, plus creator search, matching, and rendering. The count is inflated by redundancy, though: four `render_*` tools that could collapse into one parameterized tool, and batch variants of the Instagram raw-data endpoints. It is borderline acceptable for the platform-multiplied domain rather than chaotic bloat.
The tool surface covers the full read-only creator workflow: fuzzy lookup (autocomplete/search), exact profile fetch (get_profile/lookup_profiles), discovery (semantic_search/find_lookalike), fit scoring (match_creators), content evidence (get_posts), and presentation (render_*). Notable gaps include no Instagram-specific profile endpoint (odd given TikTok/YouTube have dedicated ones), no YouTube comments, and no audience-demographic data, but agents can complete realistic workflows without dead ends.