xkcdforthat

Find the obligatory xkcd for whatever you're working on. Article, slide deck, memo, PDF, blog post. Plug it into your AI tool. Your model searches our index of all xkcd comics annotated with applicability, humor type, and themes, picks the right one, and drops it inline.

该来源不提供完整文件导出（国内平台多为平台内托管），仅存元数据与原链

接入信息

传输形态: http
鉴权方式: 鉴权未知
端点: https://xkcdforthat--geertjan.run.tools

鉴权方式未标注，请核对官方文档后再接入——不要直接使用以下片段

{
  "mcpServers": {
    "xkcdforthat": {
      "url": "https://xkcdforthat--geertjan.run.tools"
    }
  }
}

能力清单

工具	说明
pick_xkcd_for	Pick the xkcd that best illustrates a document. Call this first. The server runs hybrid retrieval (BM25 + vector) over the enriched index and hands back the candidate slate plus the picker brief — same philosophy whatxkcd.com uses on its own LLM. Then you (the host LLM) read the brief, reason through the candidates in your own context, and either: - call `render_comic(...)` to draw the rich Prefab UI card (only available on hosts that support the MCP Apps UI extension), or - reply directly with the comic image and your reasoning, using the `image_url` and `page_url` already in the candidate slate. The picker_brief in the response tells you which path to take, based on what your host advertised at MCP `initialize` time.
search_comics	Hybrid (BM25 + vector) search over the enriched xkcd index. Returns up to `k` candidate comics ranked by a fusion of keyword and semantic similarity to the query. Each row contains: - num, title, alt, image_url, page_url - applicability: an LLM-written "you'd cite this when..." description - humor_type: a closed-vocab tag (e.g. 'engineer_relatability') - themes: short topical tags - one_line_summary: short prose description Use this as the retrieval step when your agent picks a comic for a document. Construct a query that captures the document's themes, cultural references, or specific topics. Examples: - "the futility of design-by-committee, yet another standard" - "regex parsing edge cases, ISO 8601, time zones" - "feeling old when others don't recognize cultural references" The text channel matches Randall's exact words (titles, alt text, transcripts) for literal-keyword recall; the vector channel matches abstract themes via the LLM-written applicability prose. Both are fused so you get topical and thematic hits in one slate.
get_comic	Fetch full details for one xkcd comic by its number. Includes everything search_comics returns plus `transcript` and `explanation` (community-written, from explainxkcd). Use this when you want richer context on a specific candidate before deciding to pick it. Always includes the required CC BY-NC attribution. The image_url is safe to hotlink — that's what xkcd's license explicitly permits.
list_humor_types	Return the closed vocabulary of `humor_type` tags used in the index. Useful when you want to filter or reason about candidates by humor mode rather than topic — e.g. a document about endless committees might call for `tech_culture_critique`, while one about feeling outdated might call for `interpersonal_awkwardness`.

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