Building an MCP Server with n8n to Seamlessly Save Research from Claude Desktop to GitHub
Introduction
As of June 2025, my information gathering methods have undergone a significant transformation. While Google search was once my primary tool, I now use Claude Desktop almost daily for research. This may not yet be a widely adopted approach, but AI/LLM-based information search has become part of my daily routine.
This shift is largely due to the Extended Thinking feature12 introduced with Claude 4. This capability allows Claude to spend more time breaking down and analyzing problems before responding, considering multiple approaches. As a result, the accuracy of responses that combine web search with Claude Desktop has dramatically improved. Furthermore, by leveraging system prompts to implement fact-checking, I can now leverage AI/LLMs for comprehensive tasks ranging from information search to fact verification support.
Recently, I've used Claude Desktop for comparative research between React Native and Flutter, as well as technical investigations for n8n workflow implementations. It collects information from multiple sources and handles organization and analysis, making it significantly more efficient compared to traditional Google searches.
However, this convenience came with a side effect: while efficient research became possible, a new challenge emerged regarding how to manage and utilize those results.
Difficulty in Utilizing Knowledge Accumulated in Chat History
While I became capable of conducting research tasks efficiently, vast amounts of knowledge started to accumulate in Claude Desktop's chat history. Although investigated content remains as analysis results, later attempts to reuse that knowledge become cumbersome. Finding the desired information requires searching through extensive chat history.
Specifically, when thinking "Where was that React Native information I researched weeks ago?" finding the target information from extensive chat history proved difficult. While Claude Desktop has search functionality, it lacks organization through relevance or categorization of research content.
Need for Structured Knowledge Management
Rather than simply leaving research results as history, I wanted to manage them in a structured format that includes the following elements:
- Classification and tagging: Categorization by technical domain and use case
- Reference linking: Source management to ensure information reliability
- Search and filtering: Mechanisms for efficient information discovery later
- Relationship visualization: Understanding connections between different research content
Integration with Daily Workflow Tools
I regularly use Obsidian, a knowledge management tool that uses markdown format, for managing memos and notes. Obsidian is highly compatible with AI/LLMs due to its markdown format that's easily understood by AI/LLMs, link functionality for expressing knowledge relationships, and rich plugin ecosystem3.
Ideally, I wanted to build a workflow that would allow me to manage Claude Desktop research results directly in Obsidian.
Solution
To address these challenges, I employed the following approach.
MCP Server for Seamlessly Utilizing Knowledge Gained Through AI/LLM Dialogue
Specifically, I made it possible to save content researched through AI conversations in markdown format externally. Knowledge gained from AI conversations can now be saved seamlessly using the same chat, complete with references found during research.
I also enabled AI to search saved knowledge. Therefore, even across different chat sessions, I can utilize previously researched knowledge by instructing the AI.
Here's a brief explanation of the technologies used.
Model Context Protocol (MCP)
MCP45, announced by Anthropic in 2024, is a protocol for standardizing collaboration between AI/LLMs and external tools. Claude Desktop's official MCP support was one of the selection criteria.
Simply put, it's a standard for easily accessing external services from AI/LLMs. In this case, I'm using it as a gateway to save chat content directly to external services like GitHub through Claude Desktop conversations.
n8n
To put it simply, n8n is a workflow automation tool that makes it easy to integrate AI.
Since I was already using n8n for automating tasks like news collection and receipt processing, I could leverage existing operational knowledge.
n8n provides a dedicated "MCP Server Trigger node"6. This functionality allows n8n to operate as an MCP server, making workflows available from AI/LLMs.
In other words, you can execute workflows from AI/LLM chats like Claude Desktop and easily integrate with external services like GitHub and Google Cloud.
GitHub
For data storage, I'm using GitHub. The reason is simple: it enables file search via API78.
Initially, I considered cloud storage options, but there was the problem of how to handle indexing for search. Ultimately, I settled on GitHub because it provides file and content search capabilities by default.
Since I was already a heavy GitHub user in my daily work, it was easy to integrate.
Implementation Details and Workflow Explanation
Using the aforementioned technologies, I built an actual MCP server.
Created Functions
1. save_knowledge (Save) Function to save research results directly from Claude Desktop to GitHub in markdown format. Implemented reliability assurance through mandatory references, automatic categorization, tag systems, and automatic metadata assignment via YAML front matter.
2. search_knowledge (Search) Search functionality utilizing GitHub Search API7. Provides flexible search by filename, title, and tags, with results displayed in relevance order.
3. read_knowledge (Read) Function to retrieve content of saved knowledge documents. Uses GitHub Contents API8 and supports Japanese filenames.
4. get_available_categories (Category List) Function to check available categories and support appropriate classification selection.
Innovations and Challenges
Error Handling Innovations
MCP servers have descriptive elements that convey actual usage to AI. However, when AI encounters errors during use, failing to properly communicate error details can confuse the AI.
During actual debugging, I used the AI to interact with the MCP server during debugging, but initially I wasn't properly conveying error details to the AI, causing it to repeatedly retry.
Therefore, I adopted error message formats that AI can understand and appropriately convey to users, implementing feedback in formats that AI can interpret rather than technical errors.
Connection Bridge via mcp-remote
MCP clients like Claude Desktop are originally designed to connect with local MCP servers. However, since n8n's MCP server operates remotely, an adapter tool called mcp-remote9 was necessary.
Since MCP has only been available for about six months, knowledge about connecting from Claude Desktop to remotely hosted MCP servers isn't widely available, which caused some difficulty.
MCP specifications are rapidly evolving, with migration planned from the current HTTP+SSE method to the more efficient Streamable HTTP method9. This change may eliminate the need for adapter tools like mcp-remote in the future.
Actual Operation and Results
In fact, this article itself serves as a proof of concept for the MCP server I built. For article writing, I utilized the following knowledge that I had previously researched and saved using Claude Desktop:
- Technical documentation on n8n's MCP server functionality
- MCP protocol specification research results
- Implementation insights for GitHub API integration
During article writing, I could instantly utilize related saved knowledge by instructing Claude Desktop to "research knowledge using the n8n-knowledge-server MCP." Since I can directly pass data to the LLM in this flow, article writing became much easier.
Conclusion
I've achieved a seamless knowledge preservation workflow where simply asking Claude Desktop to "save this research result" automatically saves it to a structured knowledge base with appropriate metadata.
Since I'm using n8n, adding features like "having Gemini review text before saving," "changing save destinations," or "email notifications" can be implemented relatively easily.
By leveraging MCP and n8n in this way, efficient knowledge collection and aggregation becomes possible.
With AI expanding the scope of what's possible, I've been running short on time lately, so being able to achieve efficiency through AI like this is very helpful.