astrbot-mcp

github_search_repos

Find GitHub repositories by query syntax; sort matches by stars, forks, or updated date and control result count.

Instructions

搜索 GitHub 仓库。

Args: query: GitHub 搜索语法，例如 "language:python mcp"。 sort: 排序字段，可选 stars、forks、updated。 per_page: 返回数量，1-100；0 表示使用配置值。

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "github_search_repos"
  }
}

Input Schema

Name	Required	Default
`sort`	No	stars
`query`	Yes
`per_page`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 29, 2026

TDQS

B3.1/5.0

Behavior2/5

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

No annotations exist, so the description carries the full behavioral burden, but it discloses nothing about authentication requirements, rate limits, or pagination. It says nothing about how results are ordered beyond the sort field or that search is subject to GitHub's API quotas — material facts for an agent deciding whether to retry or narrow a query.

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?

A one-line purpose statement followed by a compact Args block — front-loaded and free of filler. The Args formatting is slightly redundant with the schema, but the added semantics (enum values, sentinel) justify the space.

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

Completeness3/5

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

An output schema exists, so return values need not be explained, and all three parameters are documented despite zero schema coverage. What is missing for a network-backed search tool is operational context: auth, rate limits, and whether results are paginated or capped.

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

Parameters4/5

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

Schema coverage is 0%, so the description must compensate, and it does: it gives a concrete query syntax example ("language:python mcp"), enumerates the valid sort values (stars, forks, updated) that the schema only types as a bare string, and specifies the per_page range 1-100 plus the special sentinel meaning of 0. The only gap is that the 100-cap behavior when exceeding the range is not stated.

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 and resource ("搜索 GitHub 仓库" / search GitHub repositories), which is unambiguous and clearly distinct from siblings like github_search_code (code search) and github_get_repo (single repo fetch). It stops short of naming those siblings, so the differentiation is implicit rather than explicit.

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

Usage Guidelines2/5

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

There is no when-to-use or when-not-to-use guidance. The sibling set includes both github_search_code and web_search, yet the description never says when to prefer repository search over code search or general web search, leaving the agent to infer scope from the name.

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