github_profile_contributions_getGet the contribution graph for a GitHub profile for a given year. Accepts a username.
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | Calendar year for the contribution graph. Default: current calendar year. | |
| handle | Yes | GitHub username to look up, with or without a leading @. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the description doesn't need to cover safety. It adds that it accepts a username and year, but does not describe return format or other behavioral traits. Given the annotations, this is adequate but not rich.
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 two concise sentences, front-loaded with the resource and purpose. No wasted words, all information is relevant and efficiently delivered.
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?
There is no output schema, and the description does not explain what the returned contribution graph contains (e.g., daily contributions, total count). However, the concept of a contribution graph is widely known, and the openWorldHint suggests the agent should expect flexible data. Still, a note on response structure would improve completeness.
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%, with both parameters fully described (handle with length constraints and default, year with min/max and default). The description adds no additional meaning beyond what the schema already provides, so it meets the baseline without enhancing 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 action ('Get') and the specific resource ('contribution graph') for a GitHub profile, with the optional year context. It is specific enough to differentiate from sibling tools like github_profile_get (profile info) and github_profile_activity_list (activity feed).
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 retrieving contribution graphs for a specific year, which is clear context. However, it does not explicitly mention alternative tools or when not to use this tool, so it lacks exclusions but provides enough directional 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.