featuredFetch Tenor's current editorially featured GIFs; supports locale, content filter, aspect-ratio range, and pagination; returns media URLs and metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| pos | No | ||
| limit | No | ||
| locale | No | ||
| ar_range | No | ||
| media_filter | No | ||
| contentfilter | No |
| Name | Required | Description | Default |
|---|---|---|---|
| next | No | Pagination token for next results | |
| results | No | Featured GIFs |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds context about the return format (media URLs and metadata) and supported features like pagination, going beyond annotations.
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, dense sentence with no fluff. It efficiently conveys the core purpose and capabilities.
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 presence of an output schema, the description adequately covers the return type ('media URLs and metadata'). All parameters are optional, and the tool's read-only nature is clear. Minor gap: no mention of default behaviors for omitted parameters.
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?
With 0% schema description coverage, the description attempts to compensate by listing parameter categories (locale, content filter, aspect-ratio range, pagination). However, it does not provide detailed semantics for each of the 6 parameters, leaving some ambiguity.
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 verb 'Fetch' and the specific resource 'Tenor's current editorially featured GIFs', distinguishing it from sibling tools like 'categories' or 'search'. The scope is well-defined.
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 usage for fetching featured content with various filters, but does not explicitly state when to use this tool versus alternatives or provide 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.
Several tools have nearly identical purposes: autocomplete and search_suggestions both return query completions, ask_pipeworx and ask_pipeworx_beta are currently identical, and ai_visibility_check overlaps with scan_competitor_ai_presence. The detailed descriptions help for some, but the overlapping clusters create real confusion for an agent selecting a tool.
Naming is a mix of single-word nouns (search, featured, posts, categories) and snake_case verb phrases (resolve_entity, compare_entities, ask_pipeworx), with modifier suffixes like _beta and _grounded. While the snake_case is consistent where used, the overall pattern is not uniform across the server.
38 tools is far too many for a server named 'Tenor' whose core GIF API needs only a handful. Much of the surface belongs to Pipeworx data, polymarket analytics, memory, and subscriptions—scope that belongs in a different server or a clearly separated package.
The Pipeworx side is remarkably complete: query (ask_pipeworx), grounded answers, deep research, entity profiles, comparisons, validation, discovery, memory, subscriptions, and specialized polymarket tools all cover their domain thoroughly. The Tenor side has search, browse, categories, trending, suggestions, and post retrieval—no critical dead ends.