Help the user discover hidden connections between their documents, find related content, and build a knowledge network with bidirectional links across their personal Yuque knowledge base.
When to Use
- User wants to find documents related to a specific topic
- User says "What related documents are there", "find related docs", "help me build knowledge connections"
- User wants to build a knowledge map or graph for a topic
- User says "What else is related to this topic", "help me link up the knowledge", "build a knowledge graph"
Required MCP Tools
All tools are from the yuque-mcp server:
yuque_search— Search for related documents by keywordyuque_get_doc— Read document content to analyze connectionsyuque_list_repos— List personal repos to scanyuque_list_docs— List documents in repos for broader discoveryyuque_update_doc— Add cross-reference links to documentsyuque_create_doc— Create knowledge map documents
Workflow
Step 1: Identify the Starting Point
The user may provide:
- A specific document to find connections for
- A topic or keyword to explore
- A request to map an entire knowledge area
If starting from a document:
Tool: yuque_get_doc
Parameters:
repo_id: "<namespace>"
doc_id: "<slug>"
Extract key concepts, terms, and themes from the document.
Step 2: Discover Related Documents
Search for related content using extracted keywords:
Tool: yuque_search
Parameters:
query: "<keyword 1>"
type: "doc"
Repeat with different keywords to cast a wider net. Use:
- Direct topic keywords
- Synonyms and related terms
- Key people or project names mentioned
- Technical terms and concepts
Also scan repos for broader discovery:
Tool: yuque_list_docs
Parameters:
namespace: "<repo_namespace>"
Step 3: Read and Analyze Connections
For each potentially related document (top 5-10):
Tool: yuque_get_doc
Parameters:
repo_id: "<namespace>"
doc_id: "<slug>"
Analyze the relationship type:
| Relationship | Description | Example |
|---|---|---|
| 🔗 Directly related | Same topic, different angle | Both discuss microservice architecture |
| 🧩 Complementary | Fills gaps in each other | One discusses design, one discusses implementation |
| 📚 Prerequisite/Follow-up | Sequential knowledge | Introductory article → Advanced article |
| 🔀 Cross-reference | Shared concepts across topics | Both mention Redis caching strategies |
| ⚡ Contradiction/Comparison | Conflicting viewpoints | The two have different solutions to the same problem |
Step 4: Build the Knowledge Map
Present the discovered connections:
# 🗺️ 知识关联图:[主题/文档标题]
> 基于「[起始文档]」发现的知识网络
> 扫描范围:X 个知识库,XX 篇文档
> 生成时间:YYYY-MM-DD
---
## 🎯 中心节点
**[起始文档标题](链接)**
- 知识库:[库名]
- 核心概念:[概念1]、[概念2]、[概念3]
---
## 🔗 关联文档
### 直接相关
| 文档 | 知识库 | 关联类型 | 关联说明 |
|------|--------|----------|----------|
| [标题](链接) | [库名] | 🔗 直接相关 | [为什么相关] |
| [标题](链接) | [库名] | 🧩 互补 | [互补点说明] |
### 延伸阅读
| 文档 | 知识库 | 关联类型 | 关联说明 |
|------|--------|----------|----------|
| [标题](链接) | [库名] | 📚 前置知识 | [说明] |
| [标题](链接) | [库名] | 🔀 交叉引用 | [共同概念] |
---
## 🧠 知识网络
[Central document] ├── 🔗 [Directly related document 1] │ └── 🔀 [Cross-reference document A] ├── 🧩 [Complementary document 2] ├── 📚 [Prerequisite document 3] │ └── 📚 [Earlier prerequisite document B] └── ⚡ [Comparison document 4]
---
## 💡 发现与建议
- **知识聚类**:[发现的知识聚类模式]
- **知识缺口**:[发现缺少的关联文档或主题]
- **建议行动**:
1. [建议创建的文档或补充的内容]
2. [建议建立的新关联]
---
> 本知识图谱由 AI 助手自动生成,关联关系基于内容分析。
Step 5: (Optional) Add Cross-References
If the user agrees, add "相关文档" sections to the connected documents:
Tool: yuque_update_doc
Parameters:
repo_id: "<namespace>"
doc_id: "<slug>"
body: "<original content>\n\n---\n\n## 🔗 相关文档\n\n- [相关文档 1](链接) — [关联说明]\n- [相关文档 2](链接) — [关联说明]\n"
Ask before modifying any existing document:
- "Do you want to add cross-reference links to these documents?"
Step 6: (Optional) Save Knowledge Map
Tool: yuque_create_doc
Parameters:
repo_id: "<namespace>"
title: "🗺️ 知识图谱:[主题]"
body: "<knowledge map content>"
format: "markdown"
Step 7: Confirm
✅ 知识关联分析完成!
🗺️ **发现 X 篇相关文档,建立了 X 个关联**
### 关联概览
- 🔗 直接相关:X 篇
- 🧩 互补文档:X 篇
- 📚 前置/后续:X 篇
- 🔀 交叉引用:X 篇
💡 建议:[最重要的一条建议]
Guidelines
- Start broad, then narrow — search with multiple keywords to find unexpected connections
- Quality over quantity — 5 strong connections are better than 20 weak ones
- Explain why documents are related, not just that they are
- Always ask before modifying existing documents (adding cross-references)
- The knowledge map should be actionable — include specific suggestions for strengthening the knowledge network
- Identify knowledge gaps — what's missing is as valuable as what's connected
- For large knowledge bases, focus on one topic area at a time
- Default language is Chinese
Error Handling
| Situation | Action |
|---|---|
yuque_search returns few results |
Broaden keywords; try synonyms and related terms |
| Starting document has no clear connections | Suggest the document may be on a new topic; offer to search broader |
| Too many connections found (>15) | Prioritize by relevance strength; group into clusters |
yuque_update_doc fails when adding links |
Skip that document; note it in the report |
| User's knowledge base is very small | Acknowledge limited scope; suggest topics to write about to build the network |