mcp-canon
MCP server providing architectural patterns and best practices to LLM agents via RAG.
Installation
Standard (with bundled guides)
pip install mcp-canon
Includes pre-populated database with best practices for Python, Docker, Kubernetes, etc.
With indexing support
pip install "mcp-canon[indexing]"
Required for creating your own knowledge base from Markdown files.
With HTTP server support
pip install "mcp-canon[http]"
Development (all dependencies)
pip install "mcp-canon[dev]"
Quick Start
Option 1: Using bundled guides (zero configuration)
Just install and configure your MCP client — the bundled database works immediately.
{
"mcpServers": {
"canon": {
"command": "uvx",
"args": ["mcp-canon"]
}
}
}
Option 2: Create and index your own guides
Complete workflow from installation to running with custom guides.
Step 1: Install with indexing support
pip install "mcp-canon[indexing]"
Step 2: Create library structure
my-library/
├── python/
│ └── fastapi-guide/
│ ├── INDEX.md # Required: metadata
│ └── GUIDE.md # Content
└── docker/
└── best-practices/
└── INDEX.md # Can reference external URL
Step 3: Create INDEX.md with frontmatter
mkdir -p my-library/python/fastapi-guide
Create my-library/python/fastapi-guide/INDEX.md:
---
name: fastapi-guide
description: "Production-ready FastAPI patterns and best practices"
metadata:
tags:
- python
- fastapi
- api
- production
type: local
---
Step 4: Add guide content
Create my-library/python/fastapi-guide/GUIDE.md:
# FastAPI Production Guide
## Project Structure
...
## Error Handling
...
Step 5: Index your library
# Index to custom location
canon index --library ./my-library --output /path/to/my-db
# Validate frontmatter before indexing (optional)
canon validate --library ./my-library
Step 6: Configure MCP client
Local mcp
{
"mcpServers": {
"canon": {
"command": "uvx",
"args": ["mcp-canon"],
"env": {
"CANON_DB_PATH": "/path/to/my-db"
}
}
}
}
Option 3: Running as HTTP server
For remote access or multi-client scenarios, run Canon as an HTTP server.
Step 1: Install with HTTP support
pip install "mcp-canon[http]"
Step 2: Start the server
# Default port 8080
canon serve
# Custom port and host
canon serve --port 3000 --host 0.0.0.0
# With custom database
CANON_DB_PATH=/path/to/db canon serve --port 8080
Step 3: Configure MCP client
{
"mcpServers": {
"canon": {
"url": "http://localhost:8080/mcp"
}
}
}
Step 4: Verify connection
# Health check
curl http://localhost:8080/health
MCP Client Configuration
Configuration examples for popular MCP clients. Replace CANON_DB_PATH with your database path if using a custom database.
Claude Desktop
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"canon": {
"command": "uvx",
"args": ["mcp-canon"]
}
}
}
With custom database:
{
"mcpServers": {
"canon": {
"command": "uvx",
"args": ["mcp-canon"],
"env": {
"CANON_DB_PATH": "/path/to/your/db"
}
}
}
}
Cursor
Settings: Cursor Settings > Features > MCP Servers > Add new MCP server
{
"mcpServers": {
"canon": {
"command": "uvx",
"args": ["mcp-canon"]
}
}
}
VS Code (GitHub Copilot)
File: .vscode/mcp.json (project) or user settings
{
"mcpServers": {
"canon": {
"command": "uvx",
"args": ["mcp-canon"]
}
}
}
Windsurf
File: ~/.codeium/windsurf/mcp_config.json
{
"mcpServers": {
"canon": {
"command": "uvx",
"args": ["mcp-canon"]
}
}
}
JetBrains AI Assistant
Settings: Settings > Tools > AI Assistant > Model Context Protocol (MCP) > + Add
{
"mcpServers": {
"canon": {
"command": "uvx",
"args": ["mcp-canon"]
}
}
}
Gemini CLI
File: ~/.gemini/settings.json
{
"mcpServers": {
"canon": {
"command": "uvx",
"args": ["mcp-canon"]
}
}
}
Claude Code CLI
claude mcp add canon -- uvx mcp-canon
Roo Code / Kilo Code
File: .roo/mcp.json or .kilocode/mcp.json
{
"mcpServers": {
"canon": {
"command": "uvx",
"args": ["mcp-canon"]
}
}
}
Augment Code
File: VS Code settings under augment.advanced
{
"augment.advanced": {
"mcpServers": [
{
"name": "canon",
"command": "uvx",
"args": ["mcp-canon"]
}
]
}
}
Warp
Settings: Settings > AI > Manage MCP servers > + Add
{
"canon": {
"command": "uvx",
"args": ["mcp-canon"],
"env": {},
"start_on_launch": true
}
}
OpenAI Codex CLI
File: ~/.codex/config.toml
[mcp_servers.canon]
command = "uvx"
args = ["mcp-canon"]
HTTP Server Connection
For clients supporting remote MCP servers (after running canon serve):
{
"mcpServers": {
"canon": {
"url": "http://localhost:8080/mcp"
}
}
}
Using pip instead of uvx
If you installed via pip instead of uvx:
{
"mcpServers": {
"canon": {
"command": "python",
"args": ["-m", "mcp_canon"]
}
}
}
Environment Variables
| Variable | Description | Default |
|---|---|---|
CANON_DB_PATH |
Path to custom database | Bundled DB |
CANON_EMBEDDING_MODEL |
Fastembed model name (supported models) | nomic-ai/nomic-embed-text-v1.5-Q |
CANON_EMBEDDING_DIM |
Embedding vector dimensions (must match model) | 768 |
CANON_FASTEMBED_THREADS |
ONNX runtime threads for FastEmbed (lower = less RAM, slower) | auto |
CANON_FASTEMBED_BATCH_SIZE |
Embedding batch size during indexing (lower = less RAM, slower) | 256 |
CANON_FASTEMBED_PARALLEL |
FastEmbed data-parallel workers (>1 increases RAM usage) |
disabled |
CANON_LOG_LEVEL |
Log level (DEBUG, INFO, WARNING, ERROR) | INFO |
CANON_LOG_JSON |
Output logs in JSON format | false |
Note: Changing
CANON_EMBEDDING_MODELorCANON_EMBEDDING_DIMrequires a full reindex:canon index --library ./library
Documentation
| Document | Description |
|---|---|
| Writing Guides | How to write guides that index well — frontmatter schema, content structure best practices, and search optimization |
MCP Tools
| Tool | Description |
|---|---|
search_best_practices |
Semantic search for best practices (within a guide if guide_id is provided) |
search_suitable_guides |
Find guides matching a task description |
read_full_guide |
Get complete guide content |
CLI Commands
# Indexing
canon index --library ./library # Index guides (creates new DB)
canon index --library ./lib --append # Add to existing database
canon validate --library ./library # Validate frontmatter
# Server
canon serve --port 8080 # Start HTTP server (requires [http])
# Info
canon list # List indexed guides
canon info # Show database info
Frontmatter Schema
Required fields in INDEX.md:
---
name: guide-name # Must match folder name
description: "Guide description for semantic search"
metadata:
tags: # From controlled vocabulary
- python
- fastapi
type: local # "local" for GUIDE.md, "link" for URL
url: https://... # Required if type: link
---
License
MIT
Release files for mcp-canon 0.2.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| mcp_canon-0.2.0.tar.gz | 39.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mcp_canon-0.2.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 74.9 kB
Release files / mcp_canon-0.2.0.tar.gz
| Download URL | mcp_canon-0.2.0.tar.gz |
|---|---|
| Size | 39.0 kB |
| Tags | Source |
|
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Mar 11, 2026.
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