search_suggestionsReturn Tenor search query suggestions related to a partial query string (q); useful for building GIF search autocomplete UIs.
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | ||
| limit | No | ||
| locale | No | ||
| client_key | No |
| Name | Required | Description | Default |
|---|---|---|---|
| results | No | Search suggestions |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint as false, so the description doesn't need to repeat those. The description adds behavioral context (returns suggestions for partial query, Tenor-specific) beyond annotations. No contradictions.
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 sentence that is clear, front-loaded, and concise. Every word adds value, no redundancy.
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 tool's complexity (4 parameters, output schema exists), the description provides sufficient context for usage. The output schema likely details return format, so the description doesn't need to explain that. It could hint at the return type but is complete enough.
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 explains 'q' as a partial query string, but does not mention 'limit', 'locale', or 'client_key'. The examples in the schema provide some context but not in the description. Baseline is 3 due to low coverage, and the description only partially improves it.
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 tool returns Tenor search query suggestions for partial query strings, specifically for building GIF autocomplete UIs. It uses a specific verb and resource, distinguishing it from sibling tools like 'search' and 'autocomplete'.
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 explicitly mentions the use case (building autocomplete UIs), providing clear context for when to use. However, it does not explicitly state when not to use it or name alternatives, though the sibling list includes 'autocomplete' and 'trending_terms' which are related.
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.