Get GitHub repository
github_repositories_getGet a single GitHub repository by URL.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Full public GitHub repository URL. |
github_repositories_getGet a single GitHub repository by URL.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Full public GitHub repository URL. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, covering safety and external data access. The description adds the scoping detail of fetching a single repo by URL, which is not in annotations. It does not contradict annotations and provides sufficient clarity for a read operation.
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, front-loaded sentence with zero filler words. It states the action and target efficiently. No unnecessary elaboration.
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 single-get tool with one parameter and no output schema, the description adequately covers what it does and how to specify the target. The lack of return format details is acceptable given the low complexity and clear purpose. It is complete enough for an agent to invoke correctly.
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%: the 'url' parameter is fully documented as 'Full public GitHub repository URL.' The tool description merely says 'by URL' and adds no additional semantic detail beyond the schema. Baseline 3 is appropriate since the schema already explains the parameter fully.
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 states a specific verb ('get'), a resource ('single GitHub repository'), and a method ('by URL'). This clearly distinguishes it from sibling tools like github_profile_repositories_list (lists a user's repos) and github_trending_repositories_list (trending list). It is precise and 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 implies usage via 'by URL' – if you have a repository URL, use this tool. However, it does not explicitly state when to prefer it over alternatives, nor does it mention exclusions (e.g., not for listing). It lacks direct comparison with sibling tools, so guidance is only implied, not explicit.
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.