Fetch detailed statistics and metadata for a GitHub repository. Returns star count, fork count, open issue count, primary programming language, project description, last updated timestamp, and contributor count. Use for evaluating open-source projects, competitive analysis, or monitoring project health.
| Name | Required | Description | Default |
|---|---|---|---|
| repo | Yes | Repository in format 'owner/repo' (e.g. 'facebook/react', 'kubernetes/kubernetes') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly and openWorld hints. The description adds value by enumerating the specific returned metrics (star count, fork count, etc.) and indicates read-only behavior consistent with annotations. Does not disclose rate limits or error handling, but the bar is lower given 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?
Two sentences, front-loaded with the primary action and resource, then a concise list of return fields and use cases. No redundant or filler content.
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 simple one-parameter schema and no output schema, the description adequately explains what the tool returns and when to use it. It could mention failure behavior (e.g., repo not found) but is otherwise complete for this complexity level.
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?
The single parameter 'repo' is fully documented in the schema with format and examples, providing 100% coverage. The description adds no additional parameter-level meaning beyond what the schema already offers, so baseline of 3 applies.
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?
Clearly states it fetches detailed statistics and metadata for a specific GitHub repository, listing key return fields. This distinguishes it from sibling search tools like search_github and other package-specific tools.
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?
Provides context on when to use (evaluating open-source projects, competitive analysis, monitoring project health) but does not explicitly contrast with alternatives like search_github. Clear usage context, though no exclusions are stated.
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 targets a distinct resource and action: get for specific GitHub repos and npm/PyPI packages, search for GitHub repos, arXiv papers, Google Scholar papers, and Stack Overflow Q&A. No two tools overlap in purpose.
All tool names follow a consistent verb_source pattern using snake_case (get_github_repo, get_npm_package, search_arxiv, etc.). This makes the set predictable and easy to navigate.
With 7 tools, the server covers a focused domain—developer research and resource evaluation—without bloat. Each tool contributes a distinct function, and the count is well-balanced for the scope.
The tool surface covers fetching metadata for known repos/packages and searching multiple external platforms. A minor gap is the lack of direct search for npm or PyPI packages, but GitHub search partially fills this need. Overall, lifecycle coverage is appropriate for a read-only research assistant.