Development Automation Server
About
Provides a development automation server with modular architecture for fetching web content, accessing documentation, analyzing project structure, and performing web searches through internet and Perplexity APIs.
Details
- Author
- alexsmirnov
- Repository
- alexsmirnov/obsidian-second-brain-mcp
- Categories
- Productivity, Developer Tools, Design, Workplace, File Management, AI, Search, Frontend, Infrastructure, Project Management
- Tags
- #web
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Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Development Automation ServerCommand (node, npx, python, etc.)dockerArguments-
Argument 1
run -
Argument 2
--rm -
Argument 3
--env-file -
Argument 4
.env -
Argument 5
-e -
Argument 6
VAULT=/vault -
Argument 7
-v -
Argument 8
<host-vault-path>:/vault -
Argument 9
-p -
Argument 10
8000:8000 -
Argument 11
mcps:local
Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
-
Argument 1
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
The server uses only a single LLM API provider. I do have LiteLLM AI Gateway (LLM Proxy)
, but it can work with Openrouter
Clone repository, create .env file from env.example , and run
```sh
web_research
An AI agent that answers questions based on public information on the internet, optimized for technical or academic queries.
obsidian_search
A hybrid vector + BM25 search engine optimized for searching within the Obsidian vault organization.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"development automation server": {
"cwd": ".",
"env": {},
"args": [
"run",
"--rm",
"--env-file",
".env",
"-e",
"VAULT=/vault",
"-v",
"<host-vault-path>:/vault",
"-p",
"8000:8000",
"mcps:local"
],
"shell": false,
"command": "docker"
}
}
}Linux
{
"cwd": ".",
"env": [],
"args": [
"run",
"--rm",
"--env-file",
".env",
"-e",
"VAULT=/vault",
"-v",
"<host-vault-path>:/vault",
"-p",
"8000:8000",
"mcps:local"
],
"shell": false,
"command": "docker"
}Macos
{
"cwd": ".",
"env": [],
"args": [
"run",
"--rm",
"--env-file",
".env",
"-e",
"VAULT=/vault",
"-v",
"<host-vault-path>:/vault",
"-p",
"8000:8000",
"mcps:local"
],
"shell": false,
"command": "docker"
}Windows
{
"cwd": ".",
"env": [],
"args": [
"run",
"--rm",
"--env-file",
".env",
"-e",
"VAULT=/vault",
"-v",
"<host-vault-path>:/vault",
"-p",
"8000:8000",
"mcps:local"
],
"shell": false,
"command": "docker"
}Model Context Protocol ( MCP ) server for Obsidian vault
This is side research and learning project, mostly focused on AI Agents and information retrivial, as well as evaluation of AI tools.
The server allows access to Obsidian.md Vault with search and read tools. Obsidian is the plain text Markdown editor, and keeps all note files in the single folder. The format makes it perfect companion and knowledge storage for AI Agents.
I do use combination of AI coding agent ( Claude Code, Cursor ) with Obsidian.md Vault since 2024, similar to Andrej Karpathy LLM Wiki, but a little bit more complicated with his proposal.
With > 1500 notes, agents often miss important information, so I decided to create search tool that aware of knowledge organization that I use
Provided functionality
Web deep research
web_research tool is an AI agent similar by the functionality to Perplaxity.ai to answer questions based on the public information in Internet. It's optimised to answer technical or academic questions.
The core loop:
1. generate web search queries
2. fetch content from search results URLs. No attempts to bypass bot protections, but supports wide range of public sources: Arxiv.org articles, Github repositories, Reddit forums, Wikipedia, pdf documents - special cases to extract information from them, like switch to reddit API instead of read web pages, or use github raw format instead of html pages.
3. Use LLM to extract relevant information from fetch results, reduces main agent context
4. Reflection step that analyses result and decides to finish research or repeat loop to fill knowledge gaps
5. Final answer generator, creates short answer, long explanation how it was concluded, and relevant links to support answer
Evaluation results
Evaluation were performed with small models like Gamini Flash lite or GPT 5.4 nano to save costs, with fraction of questions. GAIA dataset - 60% on 20 questions DRACO dataset - around 40%, mostly because answers are less detailed than expected in evaluation criteriaEvaluation code not in this repository, it is part of internal project for team wide AI Software development tools, created to optimize process for my startup Jobsflow.ai. The tool ported from that project.
Obsidian Vault RAG
obsidian_search is hybrid vector + BM25 search engine, mostly classical RAG. It optimized to Vault organization that I use
Note format assumption
I do keep all notes with the same pattern, and indexing tool uses expected format to split note into chunks and generate additional metadata. The format enforced by special Claude Code skill that used to process all new nortes. 1. Frontmatter properties: 1.title - short sentence what is it about
2. description - 3-4 sentences that describe the note content. Used as a summary similar to RAPTOR combined summary of several chunks
3. tags - I have pre-defined set of tags, that slice notes by 3 dimensions: knowledge area ( ai, programming, finances, ...), note type ( article, tutorial, action item, ...) and narrow subject ( programming language, tool type, activity ). Tool enables retrieve of tags taxonomy and filtering them
2. Note content - all notes follow scientific essay format, each section separated by first or second level header. Semantic chunking split notes by sections. Links between notes use [[Wikilinks]] format and also extracted and stored in database. It allows graph like navigation
3. Note size - keep them no longer than 200 lines
Indexing
Tool crawls vault folders by pattern, and extracts all markdown files. Each file parsed to extract frontmatter properties, splitted by headers ( no more than 500 tokens ) and saved to database with meta information. Chunk content and description also stored as vector embeddings. Chunks created without overlaps, positive search results combined with neibhours instead.
Additional summary chunk created by LLM from whole note content, to increase chances to get into search results.
Reindexing triggered by vault content change, or explicitly from command line.
Search
The query passed through LLM to create Hypotetical abswer . Query itself used for full text search, and generated answer for vector search. This is similar to qmd markdown search
Database search results filtered by Reranker API call ( Cohere or Voiage.ai ), or Reciprocal Rank Fusion from LanceDB. RRFReranker fuses results by rank position instead of raw score, so it sidesteps having to make vector and full-text scores comparable.
The second filtering uses LLM to select chunks relevated to query. Result chunks combined with their neihbors ( so if 5th chunk from note selected, search return combination of 4-6th chunks)
Additional filters
To narrow search, optional parameters:
- tags list of tags that must be present in result
- path file path pattern
I do have 2 special notes, Tags.md with taxonomy, and Folders.md that describe vault organization, MCP server provides tool and instructions to read them
Evaluation
I do use a simple evaluation tool, that performs a query on My Vault snapshot, and counts number of expected words ( precision ), and unwanted words ( recall ). 25 questions total, F1 score ~0.9
Usage
The server uses only a single LLM API provider. I do have LiteLLM AI Gateway (LLM Proxy)
, but it can work with Openrouter
Clone repository, create
.env file from env.example , and run
# Create vault index
uv run --project <local copy> mcps --vault <Vault Folder> --reindex
run as HTTP MCP Server
uv run --project <local copy> mcps --vault <Vault Folder> --port 1234
With http protocol, a single server available to all AI tools. I do use it as shared Knowledge Base and memory across all projects.
Docker
Build and run the server as a standalone container with streamable HTTP transport:
# Build image
docker build -t mcps:local .
Run container (vault and secrets are mounted/injected explicitly)
docker run --rm --env-file .env -e VAULT=/vault -v <host-vault-path>:/vault -p 8000:8000 mcps:local
The server is reachable at
http://localhost:8000/mcp`.Sign in to leave a review
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