github_trending_repositories_listList trending GitHub repositories for a language and time window. Returns a list (use cursor when paginated).
| Name | Required | Description | Default |
|---|---|---|---|
| since | No | Trending time window. Default: `daily`. | |
| language | No | Optional coding language filter. | |
| spokenLanguageCode | No | Optional spoken language code filter. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true and openWorldHint=true, covering the safety profile. The description adds value by stating that the tool returns a list and advises using a cursor when the result is paginated. This gives the agent practical invocation guidance beyond what the annotations alone provide. No contradictions 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?
The description is extremely concise: two sentences, both informative. The main action and filters are front-loaded, and the return type and pagination hint follow naturally. There is no filler 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?
Given the tool's simplicity, the description covers everything an agent needs: it states what it does, the filters, the return type (list), and pagination handling. Since there is no output schema, the description appropriately explains the return value. Annotations cover the safety profile, so nothing else is missing for a filtered-list read operation.
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 100%, so all three parameters (since, language, spokenLanguageCode) are already documented in the schema. The description briefly mentions 'language and time window' which maps to existing parameters but adds no additional syntax or behavioral details. Baseline 3 is appropriate because the schema carries the full parameter documentation.
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 action (List), the resource (trending GitHub repositories), and the primary filters (language and time window). This is specific enough to distinguish it from sibling tools like github_trending_developers_list (which lists developers) and github_profile_repositories_list (which lists a specific user's repositories), so an agent can immediately understand its purpose.
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: when you need trending repositories, you use this tool. However, it does not explicitly mention alternatives or when not to use it. Since the sibling list includes similar trending tools, a note like 'use for trending repositories; for trending developers use github_trending_developers_list' would improve clarity. This is implied usage, not explicit guidance.
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.
Each tool is clearly scoped to a specific platform and action (e.g., facebook_post_get vs instagram_post_get). Descriptions explicitly differentiate similar tools across platforms, and within-a-platform tools like tiktok_search_videos_list vs tiktok_search_hashtag_list have clear disambiguation notes.
All 167 tools follow a strict `platform_resource_action` pattern (e.g., youtube_video_comments_list). No mixing of styles—snake_case throughout, with consistent verb ordering (get, list, search, etc.).
The server has 167 tools, which is far beyond the typical well-scoped range of 3-15. While the broad multi-platform scope justifies many tools, this extreme number makes the tool surface overwhelming and difficult for an agent to navigate efficiently.
The tool set covers a wide range of platforms and operations including profile retrieval, post/video fetching, comments, search, transcripts, and ad library access. Minor gaps exist (e.g., no Facebook events or LinkedIn messaging), but the surface is comprehensive for a read-only data aggregation use case.