mcp-canon 0.2.0

MCP server providing architectural patterns and best practices to LLM agents via RAG

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_MODEL or CANON_EMBEDDING_DIM requires 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