@sfdxy/sf-documentation-knowledge

Collect, process, and serve Salesforce documentation for LLM agents — using Context Engineering + MCP, not RAG.

Overview

This system programmatically collects all Salesforce documentation from developer.salesforce.com (121 domains, 31,000+ pages), processes it into structured, curated knowledge files, and serves them to LLM agents via:

1. Context Engineering — Pre-compiled Markdown files with _index.md routing tables
2. MCP Server — 5 tools + 3 prompt templates via Model Context Protocol
3. Knowledge Graph — 53,000+ nodes and 450,000+ edges connecting SF concepts, namespaces, services, and cross-references

No embeddings. No vector stores. No blind chunking.

Quick Start

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "sf-docs": {
      "command": "npx",
      "args": ["-y", "@sfdxy/sf-documentation-knowledge"]
    }
  }
}

Restart Claude Desktop.

VS Code (GitHub Copilot)

Add to .vscode/mcp.json in your workspace (or globally in VS Code settings):

{
  "servers": {
    "sf-docs": {
      "command": "npx",
      "args": ["-y", "@sfdxy/sf-documentation-knowledge"]
    }
  }
}

Then use @sf-docs in Copilot Chat to query Salesforce documentation.

Gemini Code Assist / Gemini CLI

Add to your MCP config (~/.gemini/settings.json or project .gemini/settings.json):

{
  "mcpServers": {
    "sf-docs": {
      "command": "npx",
      "args": ["-y", "@sfdxy/sf-documentation-knowledge"]
    }
  }
}

Cursor

Add in Settings → MCP Servers → Add Server:

• Name: sf-docs
• Command: npx -y @sfdxy/sf-documentation-knowledge
• Transport: stdio

Windsurf

Add to ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "sf-docs": {
      "command": "npx",
      "args": ["-y", "@sfdxy/sf-documentation-knowledge"]
    }
  }
}

Any MCP Client (Generic)

Point your MCP client to:

npx -y @sfdxy/sf-documentation-knowledge

The server uses stdio transport and is compatible with any MCP client.

Use from Source

git clone https://github.com/Avinava/sf-documentation-knowledge.git
cd sf-documentation-knowledge
npm install
npm run build
npm run mcp:start

MCP Server

The MCP server loads the full 53k-node knowledge graph and 18,000+ code snippets into memory on startup (~5s) and serves all queries instantly.

Tools (9)

Prompt Templates (4)

Resources (4)

Agents can read these without making a tool call:

Knowledge Base

The repository comes pre-loaded with 31,000+ curated markdown files and a Knowledge Graph (53,000+ nodes, 450,000+ edges) covering 121 domains of Salesforce documentation.

Option A: Context Engineering (File-based)

Point your AI agent to the _index.md file in any domain folder. The index acts as a routing table telling the AI which files contain which topics:

knowledge/current/<domain-name>/_index.md

Each domain folder also has a SKILL.md in skills/<domain-name>/SKILL.md that teaches AI agents how to navigate the knowledge.

Option B: Knowledge Graph

The graph at knowledge/current/graph.json connects all documentation with semantic relationships:

Inspect it with:

npm run graph:stats

See Graph Schema Documentation for the full schema with node/edge types, ID conventions, and a visual diagram.

Data Pipeline

To update the knowledge base with the latest Salesforce releases, run the pipeline in order:

Step 1: Discover Available Deliverables

npm run discover

Lists all documentation deliverables available from the Salesforce Index API (~127 deliverables).

Step 2: Collect Raw Data

# Collect a specific domain
npm run collect -- --domain cli-commands

# Collect all configured (P0) domains
npm run collect

# Collect ALL deliverables from the SF index API (121 domains, ~31k pages)
npm run collect -- --discover

Step 3: Process HTML to Markdown

# Process a specific domain
npm run process -- --domain cli-commands

# Process ALL collected domains
npm run process -- --discover

Automatically cleans HTML, strips noise, parses tables, formats code blocks, creates clean Markdown, and redacts any Salesforce tokens or secrets.

Step 4: Generate Knowledge Files & Graph

# Generate ALL collected domains and rebuild the full Knowledge Graph
npm run generate -- --discover

Builds the knowledge graph (cross-references, namespaces, service categories, doctype clustering), generates context files, and updates inventory docs.

Step 5: Inspect the Graph

npm run graph:stats

Full Pipeline (One-liner)

npm run collect -- --discover && npm run process -- --discover && npm run generate -- --discover

CLI Reference

All pipeline commands support --domain <name> for single-domain processing and --discover for all-domain processing.

CI/CD

Documentation

License

MIT © Avinava

Inventory

121 domains | 33,188 knowledge files

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