Company Docs MCP

Turn any documentation into an AI-searchable knowledge base. Feed it markdown, HTML, PDFs, web pages, or plain text — publish to a database and let anyone on your team query it through AI tools like Claude, Cursor, or Slack. Powered by the Model Context Protocol.

What This Does

1. Ingest — Point the CLI at your content. It accepts markdown files, HTML pages, PDFs, live URLs, or even an entire website via crawl.
2. Publish — Run a command that converts your content into searchable vectors and stores it in a database.
3. Query — Connect any MCP-compatible AI tool to your server. Ask questions in plain English and get answers sourced directly from your documentation.

Two Ways to Use This

There are two distinct roles when working with Company Docs MCP. Most people on your team only need the first one.

If someone already set up the server for your team

You just need a URL. No accounts, no installation, no terminal commands.

1. Get the server URL from whoever set it up (it looks like https://company-docs-mcp.example.workers.dev/mcp)
2. Add it to your AI tool:
   • Claude: Settings > Connectors > Add custom connector > paste the URL
   • Cursor / Windsurf: Add the URL as a remote MCP server in settings
3. Start asking questions about your documentation

That's it. Cloudflare, Supabase, and the CLI are only needed by the person who sets up and maintains the server.

If you're setting up the server (admin/maintainer)

The rest of this README is for you. Follow the setup guide below to get everything running.

How the Pieces Fit Together

The system uses three services. All three offer free tiers that are sufficient for most teams.

flowchart TD
    A["Your Content<br/>Markdown, HTML, PDFs, URLs"]
    B["Cloudflare Workers AI"]
    C[("Supabase")]
    D["Your Team<br/>Claude, Cursor, Slack, Chat UI"]
    E["Cloudflare Worker"]

    A -- "ingest + publish" --> B
    B -- "store vectors" --> C

    D -- "ask a question" --> E
    E -- "vector search" --> C
    C -. "matching docs" .-> E
    E -. "answers" .-> D

    style A fill:#f9f9f9,stroke:#333,color:#333
    style B fill:#dbeafe,stroke:#1d4ed8,color:#333
    style C fill:#d4edda,stroke:#155724,color:#333
    style D fill:#f0fdf4,stroke:#15803d,color:#333
    style E fill:#dbeafe,stroke:#1d4ed8,color:#333

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Service	What it does	Why it's needed
Cloudflare	Hosts your server and converts text into searchable vectors using its built-in AI	This is where your server runs 24/7 so your team can query docs at any time. It also handles the AI processing that makes semantic search possible — no separate AI subscription needed.
Supabase	Stores your documentation in a PostgreSQL database with vector search	Powers "smart" search — asking "how do I deploy?" will find documents about releases, CI/CD, and shipping, not just pages containing the word "deploy."
npm package	A command-line tool that ingests your content (markdown, HTML, PDFs, URLs) and publishes it to the database	You run this on your computer whenever you add or update documentation.

No third-party AI API keys are required. Cloudflare provides the AI capabilities through its Workers AI service, which is included with every Cloudflare account at no extra cost.

What You'll Need

Before starting, create free accounts on these two services:

• Node.js 18 or later — the runtime that powers the CLI tool (download here)
• A Cloudflare account — for hosting and AI (sign up here, free tier works)
• A Supabase account — for the database (sign up here, free tier works)

That's it. No OpenAI, Anthropic, or Google API keys needed.

Setup Guide

Follow these steps in order. Each one builds on the previous.

Step 1: Install the Package

Open your terminal in the project where your documentation lives and run:

npm install company-docs-mcp

This downloads the CLI tool to your project. No external services are contacted yet.

Step 2: Create Your Database (Supabase)

Your documentation needs a database to store content and make it searchable.

1. Go to supabase.com and create a new project
2. Go to Settings > API and copy three values (you'll need these in Step 4):
   • Project URL (looks like https://abc123.supabase.co)
   • anon key (a long string starting with eyJ)
   • service_role key (another long string starting with eyJ — keep this private)
3. Open the SQL Editor in the left sidebar, paste the contents of database/schema.sql, and click Run

This creates the database tables and search functions the system uses.

The schema file is included in the npm package at node_modules/company-docs-mcp/database/schema.sql.

Step 3: Log In to Cloudflare

The CLI needs access to Cloudflare's AI service to convert your documentation into searchable vectors. The simplest way to connect is through the Wrangler CLI (Cloudflare's command-line tool, included with this package).

Run this command:

npx wrangler login

A browser window will open asking you to log in to your Cloudflare account and grant permission. Click Allow and return to your terminal.

You also need your Cloudflare Account ID:

1. Go to dash.cloudflare.com
2. Your Account ID is shown on the right side of the overview page — copy it

That's the only Cloudflare setup needed for publishing. The CLI automatically detects the login credentials that wrangler login saved to your computer.

Token expiration: The login session expires periodically. If you see an authentication error when publishing, just run npx wrangler login again.

Step 4: Configure Your Environment

Create a file called .env in your project root with these values:

# Supabase — where your documentation is stored
SUPABASE_URL=https://your-project.supabase.co
SUPABASE_ANON_KEY=eyJ...
SUPABASE_SERVICE_KEY=eyJ...

# Cloudflare — your Account ID (from Step 3)
CLOUDFLARE_ACCOUNT_ID=your-account-id

Replace the placeholder values with the ones you copied from Supabase (Step 2) and Cloudflare (Step 3).

Keep this file private. Never commit .env to version control — it contains credentials. Add .env to your .gitignore file.

Step 5: Prepare Your Content

The system accepts multiple content formats. Use whichever fits your workflow:

Format	How to ingest	Best for
Markdown (.md)	npx company-docs ingest markdown --dir=./docs	Documentation you write and maintain as files
HTML (.html)	npm run ingest -- html ./file.html	Exported web pages, saved articles
PDF (.pdf)	npm run ingest -- pdf ./document.pdf	Policies, reports, specs in PDF format
URL (any web page)	npm run ingest -- url https://example.com/page	Live web content you want to index
Website crawl	npm run ingest:web -- --url=https://docs.example.com	Entire documentation sites
CSV of URLs	npm run ingest:csv -- urls.csv	Batch-importing many web pages at once

Note: The npx company-docs CLI (from the npm package) directly supports markdown ingestion and publishing. The other formats (HTML, PDF, URL, website crawl, CSV) are available when running from the cloned repository.

Markdown is the most common starting point. Create files in a directory — any folder structure works:

docs/
├── onboarding/
│   ├── new-hire-checklist.md
│   └── tools-and-access.md
├── engineering/
│   ├── deployment-guide.md
│   └── code-review-process.md
└── policies/
    ├── pto-policy.md
    └── expense-guidelines.md

You can optionally add YAML frontmatter to control how each document is categorized:

---
title: Deployment Guide
category: engineering
tags: [deploy, ci-cd, release]
description: How to deploy to production
---

# Deployment Guide

Your content here...

If you don't include frontmatter, the system will auto-detect a category and extract tags from the content.

Step 6: Ingest and Publish

Two steps turn your content into a searchable knowledge base:

Step 1 — Ingest (converts your content into structured entries):

# Markdown (most common)
npx company-docs ingest markdown --dir=./docs

# Or from the cloned repo, for other formats:
npm run ingest -- html ./exported-page.html
npm run ingest -- pdf ./policy-document.pdf
npm run ingest -- url https://wiki.example.com/important-page

Step 2 — Publish (pushes entries to the database with search vectors):

npx company-docs publish

What happens:

1. The ingest step reads your content, extracts titles and sections, and saves structured entries as JSON files in a content/entries/ folder in your project.
2. publish sends each entry to Cloudflare's AI to generate search vectors, then stores everything in your Supabase database. A content hash automatically skips entries that haven't changed, so re-running is fast.

To preview what would be published without actually writing to the database:

npx company-docs publish --dry-run

Updating content: Whenever you change your source files, run both steps again. Only changed entries are re-processed.

Step 7: Deploy the Server (Cloudflare Worker)

The server is what runs 24/7 and handles search queries from your team's AI tools. It's deployed as a Cloudflare Worker.

Clone the repository

git clone https://github.com/southleft/company-docs-mcp.git
cd company-docs-mcp
npm install

Configure the Worker

Edit wrangler.toml with your organization name:

name = "company-docs-mcp"
main = "src/index.ts"
compatibility_date = "2024-01-01"
compatibility_flags = ["nodejs_compat"]

[ai]
binding = "AI"

[vars]
ORGANIZATION_NAME = "Your Organization"
VECTOR_SEARCH_ENABLED = "true"
VECTOR_SEARCH_MODE = "vector"

Create a search cache

The Worker caches recent search results to keep things fast. Run this command to create the cache:

npx wrangler kv namespace create CONTENT_CACHE

It will print an ID. Add it to wrangler.toml:

[[kv_namespaces]]
binding = "CONTENT_CACHE"
id = "the-id-that-was-printed"

Add your database credentials to the Worker

These are stored securely as encrypted secrets — they never appear in plain text in the dashboard or config files.

echo "your-supabase-url" | npx wrangler secret put SUPABASE_URL
echo "your-anon-key" | npx wrangler secret put SUPABASE_ANON_KEY
echo "your-service-key" | npx wrangler secret put SUPABASE_SERVICE_KEY

Deploy

Make sure you're logged in (you should be from Step 3 — if not, run npx wrangler login again), then:

npm run deploy

Your server is now live at https://company-docs-mcp.<your-subdomain>.workers.dev.

Step 8: Connect and Test

Share this URL with your team:

https://company-docs-mcp.<your-subdomain>.workers.dev/mcp

Claude: Settings > Connectors > Add custom connector > paste the URL.

Cursor / Windsurf / Other MCP clients: Add the URL as a remote MCP server in your client's settings.

Once connected, your AI tool will have access to these search tools:

Tool	What it does
search_documentation	Finds documentation that matches your question using semantic search
search_chunks	Searches specific sections within documents
browse_by_category	Lists all documentation in a category (categories come from frontmatter, the --category flag, or auto-detection)
get_all_tags	Lists every tag used across your documentation

Cloudflare's Role — A Quick Summary

Since Cloudflare appears in several steps, here's a plain-language summary of what it does and when:

When	What Cloudflare does	How it's accessed
Publishing docs (Step 6)	Converts your text into numerical vectors that enable semantic search	CLI calls the Cloudflare REST API using your wrangler login credentials
Running the server (Step 7+)	Hosts the always-on server that your team queries; generates vectors for incoming questions	Built-in — no API keys needed at runtime

Is Cloudflare optional? No — it's required for both publishing and hosting. However, the free tier is more than sufficient and no separate AI subscription is needed. The only setup required is creating an account and running npx wrangler login.

CLI Reference

npm Package Commands

These work anywhere via npx company-docs:

company-docs <command> [options]

Command	Description
ingest markdown	Parse markdown files into content/entries/
publish	Push entries to the database with AI-generated vectors
ingest supabase	Same as publish
manifest	Generate content/manifest.json (used during Worker deployment)

Repository Commands

These are available when running from the cloned repository:

Command	Description
npm run ingest -- html <file>	Ingest an HTML file
npm run ingest -- pdf <file>	Ingest a PDF document
npm run ingest -- url <url>	Ingest a single web page
npm run ingest:csv -- <file>	Ingest URLs listed in a CSV file
npm run ingest:web -- --url=<url>	Crawl and ingest an entire website

Ingest Markdown Options

Option	Description	Default
--dir, -d	Folder containing your markdown files	./docs
--category, -c	Category label for the content (overrides frontmatter)	documentation
--recursive	Include files in subfolders	true
--verbose, -v	Show detailed output	false

Publish Options

Option	Description
--clear	Delete all existing data before publishing (start fresh)
--dry-run	Preview what would change without writing to the database
--verbose	Show detailed per-entry progress

Examples

# Ingest markdown from different folders with different categories
npx company-docs ingest markdown --dir=./docs/engineering --category=engineering
npx company-docs ingest markdown --dir=./docs/policies --category=hr
npx company-docs publish

# Ingest a PDF and a web page (from cloned repo)
npm run ingest -- pdf ./policies/employee-handbook.pdf
npm run ingest -- url https://wiki.example.com/onboarding
npx company-docs publish

# Crawl an entire documentation site (from cloned repo)
npm run ingest:web -- --url=https://docs.example.com --max-pages=50

# Full re-publish from scratch
npx company-docs publish --clear

# Preview changes
npx company-docs publish --dry-run --verbose

YAML Frontmatter Reference

Each markdown file can optionally include a YAML frontmatter block at the very top. The system reads these fields:

---
title: Page Title
category: engineering
tags: [deploy, ci-cd, release]
description: A short summary of this page
status: stable
version: 1.0.0
source: src/path/to/source.ts
figma: https://figma.com/...
author: Jane Smith
department: Engineering
---

Field	Effect
title	Used as the document title (overrides the first # Heading)
category	Sets the browseable category for this document
tags	Adds tags for filtering and discovery
description	Stored as metadata, returned in search results
status	Stored as metadata (e.g., draft, stable, deprecated)
version	Stored as metadata
source, figma, author, department	Stored as metadata, available in search results

All fields are optional. If no frontmatter is present, the system auto-detects a category and extracts tags from the content.

Priority order: Frontmatter values take highest priority, followed by CLI flags (like --category), followed by auto-detection.

Incremental Updates

The system is designed for repeated runs — you don't need to start from scratch each time:

• Content hashing — Only entries whose content has actually changed are re-processed
• Deterministic IDs — The same source file or URL always produces the same database ID, preventing duplicates
• Stale cleanup — Entries removed from your source files are automatically cleaned up from the database

# Edit your content, then re-ingest and re-publish — only changes are processed
npx company-docs ingest markdown --dir=./docs
npx company-docs publish

Optional: Slack Integration

The server includes a Slack slash command so team members can search documentation directly from Slack:

/docs deployment process
/docs PTO policy
/docs how to set up staging

See docs/SLACK_SETUP.md for setup instructions.

Optional: Chat Interface

The server includes a web-based chat UI at its root URL (visit the Worker URL in a browser). It has two modes:

• Search mode — Finds relevant documentation using the same vector search as MCP. No additional setup needed.
• AI chat mode — Sends your question to OpenAI GPT-4o, which searches your docs and synthesizes a conversational answer. This is the only feature that requires an OpenAI API key (set as a Worker secret: OPENAI_API_KEY).

Customize the chat UI with environment variables in wrangler.toml:

[vars]
ORGANIZATION_NAME = "Your Organization"
ORGANIZATION_LOGO_URL = "https://example.com/logo.svg"
ORGANIZATION_TAGLINE = "Ask anything about our documentation"

See docs/BRANDING.md for full branding options.

Optional: OpenAI Embeddings

By default, the system uses Cloudflare's Workers AI for embeddings (free, no extra keys). If your organization prefers OpenAI, you can switch:

OPENAI_API_KEY=sk-...
EMBEDDING_PROVIDER=openai

Provider	Model	Dimensions	When to use
Workers AI (default)	@cf/baai/bge-large-en-v1.5	1024	Default. No extra keys. Free on Cloudflare.
OpenAI	text-embedding-3-small	1536	If your organization already standardizes on OpenAI.

Important: The embedding provider must match the database schema. The default schema.sql uses 1024 dimensions (Workers AI). If switching to OpenAI, change all vector(1024) to vector(1536) in the schema before running it.

Troubleshooting

No results from search

• Verify npx company-docs publish completed without errors
• Check that your .env has the correct Supabase credentials
• Run npx company-docs publish --dry-run to see what entries exist

Authentication error when publishing

• Your wrangler login session may have expired — run npx wrangler login again
• Verify CLOUDFLARE_ACCOUNT_ID is set in your .env

Duplicate entries

• Re-run npx company-docs ingest markdown followed by npx company-docs publish — duplicates are cleaned up automatically

MCP client not connecting

• Make sure the Worker is deployed and accessible
• Use the /mcp path in the URL (not just the root URL)
• Restart your MCP client after adding the connector

Wrangler login not working

• If you have a CLOUDFLARE_API_TOKEN set in your environment or .env file, it can interfere with the login flow. Remove or comment it out, then try npx wrangler login again.

Security

• Never commit .env files — they contain credentials
• The SUPABASE_SERVICE_KEY has full database access — keep it private
• The SUPABASE_ANON_KEY is restricted by Row Level Security policies (read-only)
• Review docs/SECURITY_KEY_ROTATION.md if you need to rotate credentials

License

MIT — see LICENSE for details.

Contributing

Issues and pull requests are welcome at github.com/southleft/company-docs-mcp.