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Kamisorara

online-search-mcp

by Kamisorara

search_github

Search GitHub repositories via official API using qualifiers like language, stars, topic, and pushed date to find open-source projects, examples, or libraries.

Instructions

搜索 GitHub 仓库(官方 API,找开源项目/案例/轮子用这个)。

支持限定符组合,示例: - "mcp server language:python stars:>1000"(Python 高星 MCP 服务) - "rimworld mod topic:rimworld-mod"(按主题) - "web crawler pushed:>2025-01-01 language:go"(近期活跃的 Go 爬虫) 常用限定符:language: / stars:>N / topic: / pushed:>YYYY-MM-DD / license: / in:name,description

Args: query: 搜索词,可含上面的限定符。 max_results: 返回条数上限,默认 8(1-20)。 sort: "best-match"(相关度)/ "stars" / "forks" / "updated"。

Input Schema

NameRequiredDescriptionDefault
sortNobest-match
queryYes
max_resultsNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description carries the burden. It discloses the result cap range (1–20), the default (8), the sort vocabulary, and the supported qualifier set — real behavioral detail. It says nothing about GitHub API authentication/token requirements or unauthenticated rate limits, which are the main failure modes for this tool, and it doesn't characterize what a result entry contains beyond 'returns N results'.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose is front-loaded in the first line, followed by examples and then a per-argument block — good scan order. The qualifier list partially duplicates what the examples already demonstrate, so there is mild redundancy, but every line is actionable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return-value explanation is not needed, and the query grammar — the hardest part of this tool — is covered thoroughly. The remaining gap is operational: no mention of auth/token or rate limits for the official API, which matters for an agent that may call it repeatedly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% (titles only), yet the description fully compensates: `query` is documented with combinable qualifiers (language:, stars:>N, topic:, pushed:>, license:, in:name,description) plus three concrete examples; `max_results` documents default 8 and the 1–20 bound; `sort` enumerates the four valid string values even though the schema declares none.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource (search GitHub repositories) and adds scope via the official API plus the 'find open-source projects/cases/libs' gloss, which separates it from the generic search_web sibling in spirit. It never names the siblings explicitly, so the differentiation is implied rather than stated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The parenthetical '(official API, use this for finding open-source projects/cases/wheels)' gives a clear use context, and the three worked query examples show realistic invocation patterns. It lacks an explicit 'when not to use this / use search_web instead' clause, so it stops short of full routing guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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