trending_termsTrending Tenor GIF and sticker keywords — the reaction, meme, emotion and pop-culture phrases people are currently typing into Tenor to find GIFs to send in chats and messaging apps. Returns Tenor's current trending GIF keyword list for a locale.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| locale | No |
| Name | Required | Description | Default |
|---|---|---|---|
| results | No | Trending search terms |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is clear. The description adds context about the content (reaction, meme, pop-culture phrases) but does not disclose any additional behavioral traits like pagination or rate limits. No contradiction with 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?
Two sentences, front-loaded with the key information, and no wasted words. It earns its place by explaining both the domain context and the return type.
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 read-only list tool with a rich output schema and good annotations, the description is mostly sufficient. However, it lacks explicit usage guidance and fails to explain the 'limit' parameter, leaving some contextual gaps.
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 0%, so the description must compensate. It mentions 'for a locale' which clarifies the locale parameter, but the 'limit' parameter is not explained at all. With only two optional parameters, this is a noticeable gap.
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 ('Returns') and names the exact resource: Tenor's current trending GIF keyword list. It clearly distinguishes itself from siblings like 'featured' or 'categories' by focusing on trending search terms.
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 when to use this tool (when you need trending Tenor keyword lists) but does not explicitly discuss alternatives or when not to use it. Sibling tools like 'featured' or 'categories' exist, but no exclusion criteria are given.
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.