github_trending_developers_listList trending GitHub developers 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 trending coding language filter. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the return type ('Returns a list') and pagination behavior ('use cursor when paginated'), adding value beyond the readOnlyHint and openWorldHint annotations. It does not contradict annotations and provides useful operational detail for a likely read-only list endpoint.
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, concise sentence with a parenthetical about pagination. It front-loads the core action and scope, contains no redundant words, and efficiently conveys the essential information an agent needs.
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 two optional parameters and no output schema, the description covers the primary usage and pagination. It could benefit from describing the shape of the returned developer objects, but current coverage is adequate for correct invocation given the schema and annotations.
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 both parameters (since and language) are adequately documented in the schema. The description only restates that these parameters exist ('for a language and time window') without adding new syntax, format, or interaction details, making the baseline 3 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 the action ('List'), the resource ('trending GitHub developers'), and the key parameters ('for a language and time window'). It also distinguishes from the sibling github_trending_repositories_list by naming 'developers' explicitly, making the purpose unambiguous.
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 provides no guidance on when to use this tool versus alternatives, such as github_trending_repositories_list. It does not mention any conditions or exclusions, leaving the agent to infer that this is for developers rather than repositories, which is already evident from the name.
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.