Vectree

Search and read ~95,000 interactive concept diagrams that explain how things work — visually.

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品質與安全性

A
說明品質
100%
結構描述完整度
98%
命名品質
100%
汙染風險
100%
權限相符程度
100%
協定合規性
100%

根據工具定義與協定合規性的自動化分析。

上下文成本

~1,515Token(工具定義)
~763 B典型回應大小
中等的注意力影響(128k 上下文的 1.18%)

這是每次將伺服器的工具載入模型上下文時所消耗的約略 token 數量。數量越高,可用於其他工作的注意力就越少。

安裝

一鍵安裝

將以下內容加入你的 `claude_desktop_config.json` 檔案:

{
  "mcpServers": {
    "diagrams": {
      "url": "https://vectree.io/mcp"
    }
  }
}

遠端端點

https://vectree.io/mcpstreamable-http

它能做什麼

工具清單

工具(6)

🟢 唯讀🟡 寫入🔴 刪除⚪ 未知
🟡get_collection(slug)

Fetch one Vectree collection: its description and the diagrams it contains, in curated order. A collection is an editor-assembled group of Vectree's labelled schematics around one theme — a book shelf, a system map, a skill tree or a set of deep lore. This tool returns the whole group, so you do not need to fetch the page. Use it after `list_collections`, or whenever you already know a collection slug. Each entry gives a diagram slug you can pass to `get_diagram` to read it in full; an entry marked "still generating" has no readable diagram yet, so skip it. Only published collections can be fetched. Nothing is generated on demand: an unknown slug simply has no collection.

輸入結構描述

{
  "type": "object",
  "properties": {
    "slug": {
      "description": "Collection slug, e.g. the `slug:` of a list_collections result or the last segment of a vectree.io/explore/... URL.",
      "type": "string"
    }
  },
  "required": [
    "slug"
  ]
}
🟢get_diagram(slug)

Fetch the full content of one Vectree diagram: every labelled node with its explanation, plus image URLs and links to the diagrams its nodes lead to. Vectree explains how things work as zoomable, labelled schematics; this tool returns the complete text of one of them, so you do not need to fetch the page. Use it after `search_diagrams` to read a result in depth, or whenever you already know a Vectree slug. The `Linked diagrams` and `Related diagrams` sections carry slugs you can pass straight back to this tool to walk the graph. Only public, already-generated diagrams can be fetched. Nothing is generated on demand: an unknown slug simply has no diagram.

輸入結構描述

{
  "type": "object",
  "properties": {
    "slug": {
      "description": "Diagram slug, e.g. the `slug:` of a search_diagrams result or the last path segment of a vectree.io/c/... URL.",
      "type": "string"
    }
  },
  "required": [
    "slug"
  ]
}
🟢get_learning_path(slug)

Fetch one Vectree learning path: its ordered steps, each naming the diagram that step teaches. A learning path is a curated sequence of Vectree's labelled schematics that works through a subject from the fundamentals upward. This tool returns the whole sequence in order, so you do not need to fetch the page. Use it after `search_learning_paths`, or whenever you already know a path slug. Every step gives a diagram slug you can pass to `get_diagram` to read that step in full; a step marked "still generating" has no readable diagram yet, so skip it. Only published paths can be fetched. Nothing is generated on demand: an unknown slug simply has no path.

輸入結構描述

{
  "type": "object",
  "properties": {
    "slug": {
      "description": "Learning path slug, e.g. the `slug:` of a search_learning_paths result or the last segment of a vectree.io/path/... URL.",
      "type": "string"
    }
  },
  "required": [
    "slug"
  ]
}
🟡list_collections(type)

List Vectree's curated collections — hand-assembled groups of diagrams: book shelves, system maps, skill trees and deep lore. Vectree explains how things work as zoomable, labelled schematics. A collection is an editor-curated set of them around one theme, so it is the best way to see what Vectree covers when you have no specific query yet. Use this to browse, to answer "what is on Vectree?", or to find a themed set of diagrams. When the user already has a topic in mind, use `search_diagrams` instead; when they want an ordered syllabus, use `search_learning_paths`. Every entry carries a slug — pass it to `get_collection` for the diagrams inside. Only published collections are listed, and nothing is generated on demand.

輸入結構描述

{
  "type": "object",
  "properties": {
    "type": {
      "description": "Optional filter to one kind of collection: book_shelf, system_map, skill_tree, deep_lore or curated. Omit it to list them all.",
      "enum": [
        "book_shelf",
        "system_map",
        "skill_tree",
        "deep_lore",
        "curated"
      ],
      "type": "string"
    }
  }
}
🟢search_diagrams(limit, query)

Search Vectree's library of ~95,000 interactive concept diagrams by meaning, not keywords. Vectree explains how things work as zoomable, labelled schematics — each diagram breaks a topic into nodes you can read or drill into. Use this when the user wants a diagram, a visual explanation, a systems overview, or a map of how the parts of something fit together. Describe the topic in natural language; the search is semantic, so a full question works better than a bare keyword. Results are ranked by how closely they match and by the quality of the model that generated them. Each result carries a slug — pass it to `get_diagram` for the full content of one diagram. Only public, already-generated diagrams are searched. Nothing is generated on demand, so a topic with no match simply has no diagram yet.

輸入結構描述

{
  "type": "object",
  "properties": {
    "limit": {
      "description": "How many diagrams to return, 1-10. Defaults to 5.",
      "maximum": 10,
      "minimum": 1,
      "type": "integer"
    },
    "query": {
      "description": "What to explain, in natural language — e.g. \"how TCP congestion control works\".",
      "type": "string"
    }
  },
  "required": [
    "query"
  ]
}
🟡search_learning_paths(limit, query)

Search Vectree's curated learning paths — ordered sequences of diagrams that teach a subject from the ground up, one step at a time. Vectree explains how things work as zoomable, labelled schematics. A learning path strings a set of those diagrams into a syllabus, so a reader moves from the fundamentals of a subject to its harder parts in a deliberate order. Use this when the user wants to *learn*, *study* or *get started with* a whole subject. When they want one specific topic explained instead, use `search_diagrams` — that searches individual diagrams rather than sequences. Describe the subject in natural language; the search is semantic, so a full sentence works better than a bare keyword. Each result carries a slug — pass it to `get_learning_path` for the full ordered sequence. Only published paths are searched. Nothing is generated on demand, so a subject with no match simply has no path yet.

輸入結構描述

{
  "type": "object",
  "properties": {
    "limit": {
      "description": "How many learning paths to return, 1-10. Defaults to 5.",
      "maximum": 10,
      "minimum": 1,
      "type": "integer"
    },
    "query": {
      "description": "The subject the user wants to learn, in natural language — e.g. \"learn digital signal processing from scratch\".",
      "type": "string"
    }
  },
  "required": [
    "query"
  ]
}

建議的提示詞

search_research
Search for information about [topic] using Vectree
預期的工具: search_diagrams
find_specific
Find [specific item] using Vectree
預期的工具: search_diagrams
retrieve_data
Get details about [item] from Vectree
預期的工具: get_collection
fetch_info
Fetch [information type] using Vectree
預期的工具: get_collection
list_items
List all [items] available in Vectree
預期的工具: list_collections

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已驗證未記錄版本6 個工具
已驗證未記錄版本6 個工具