About Glanser Guidelines MCP Server
Glanser Guidelines MCP Server is an MCP server in the RAG category: provides semantic search over a team's coding guidelines corpus using FastMCP, ChromaDB, and sentence-transformers. Enables fully offline operation with tools for searching, browsing, and filtering guidelines by scope. It has been installed 0 times through Conduid.
Install
git clone https://github.com/Vbridge7/MCP-serverThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
Ask AI
Ask AI about Glanser Guidelines MCP Server
Powered by Claude · Grounded in docs
Security checks
- ·README presentNot checked yet.
- ·License declaredNot checked yet.
- ·Tests presentNot checked yet.
- ·Dependencies pinnedNot checked yet.
- ·No dynamic code executionNot checked yet.
- ·Scoped permissionsNot checked yet.
README
Glanser Guidelines MCP Server
Semantic search over the team's coding guidelines corpus. Powered by FastMCP + ChromaDB + sentence-transformers (all-MiniLM-L6-v2). 100% free — no API keys, no external services, runs fully offline after setup.
Folder Structure
mcp-server/
├── server.py ← MCP server (run this on the host)
├── ingest.py ← One-time ingestion script
├── requirements.txt ← Python dependencies
├── documents/ ← Drop your .md guideline files here
│ └── CODING_GUIDELINES.md
└── chroma_db/ ← Created automatically by ingest.py (do not edit)
Setup (run once on the host machine)
1. Install dependencies
pip install -r requirements.txt
sentence-transformerswill download theall-MiniLM-L6-v2model (~80 MB) on first run and cache it. Subsequent runs are fully offline.
2. Add your documents
Copy markdown files into the documents/ folder:
cp /path/to/CODING_GUIDELINES.md documents/
3. Ingest (embed once, saved to disk)
python ingest.py
This reads every .md file in documents/, embeds each section, and
persists the vectors to chroma_db/. You only re-run this when adding
a new document.
Useful flags:
python ingest.py --file documents/NEW_DOC.md # add a single new doc
python ingest.py --reset # wipe and re-ingest everything
python ingest.py --list # see what is currently indexed
4. Start the server
python server.py
Server starts on http://0.0.0.0:8000.
Hosting (team access)
Deploy to Railway or Render (both have free tiers):
- Push this
mcp-server/folder to a git repo - Create a new service pointing to that repo
- Set start command:
python server.py - Mount a persistent volume at
/app/chroma_db(so embeddings survive deploys) - Run
python ingest.pyonce via the host console after deploy
Railway/Render automatically provision an HTTPS URL like:
https://glanser-guidelines-mcp.railway.app
Team .mcp.json entry
Each team member adds this to their .mcp.json:
{
"mcpServers": {
"coding-guidelines": {
"type": "http",
"url": "https://your-hosted-domain.com/mcp"
}
}
}
Available Tools
| Tool | What it does |
|---|---|
search_guidelines |
Semantic search across all docs — use this first |
get_section |
Fetch full content of a specific section |
list_sections |
Browse all section titles across the corpus |
get_by_scope |
Filter rules by library, client, or both |
list_documents |
See all indexed documents and their section counts |
Adding a New Document
# 1. Copy the new doc
cp NEW_GUIDELINES.md documents/
# 2. Ingest only the new file (does not re-embed existing docs)
python ingest.py --file documents/NEW_GUIDELINES.md
# 3. No server restart needed — ChromaDB is queried live
Local dev / testing (without hosting)
{
"mcpServers": {
"coding-guidelines": {
"type": "http",
"url": "http://localhost:8000/mcp"
}
}
}
README mirrored from the source repository 4 months ago. The original is authoritative.