github_search_reposSearch GitHub repositories by keyword, sort results by stars, forks, or updated date, and limit the count to translate tech trends into numeric metrics.
搜索 GitHub 仓库趋势，把技术趋势翻译成数字（stars/forks/活跃度）。
| Name | Required | Description | Default |
|---|---|---|---|
| sort | No | 排序，stars（默认）/ forks / updated | stars |
| limit | No | 返回数量，默认 10 | |
| query | Yes | 搜索关键词，如 "AI agent framework" |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses a behavioral trait: the tool focuses on quantitative metrics (stars/forks/activity) and trend analysis, which goes beyond a generic search. However, it doesn't mention rate limits, authentication, or return format, but given the output schema existshareholders, the core behavior is transparent enough.
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 that front-loads the purpose (trend search) and outcomes (quantitative metrics). It's efficient with no filler, earning a high score for its brevity and clarity.
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 has an output schema (filling in return values), 3 well-documented parameters, and a straightforward search use case, the description suffices. It covers the core purpose and result orientation. Minor gaps like not mentioning comparison with siblings are offset by the existence of an output schema and simple parameter semantics.
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 the schema documents all three parameters (query, sort, limit) with clear descriptions. The description adds a trend-oriented framing but doesn't add new parameter-level meaning beyond what's in the schema. Baseline 3 is appropriate since the schema carries the load.
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 ('搜索' - search) and resource ('GitHub 仓库趋势' - GitHub repository trends), and clarifies it translates trends into metrics (stars/forks/activity). It distinguishes from siblings by focusing on trend metrics rather than generic search or comparison, though it doesn't name a sibling explicitly.
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 exploring trends and quantifying popularity, but doesn't explicitly state when to use it vs. alternatives like github_compare_repos (for comparison) or web_search (for general web results). The trend-focused angle gives some guidance, but no exclusions or alternative names are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.