Campervan-mcp

A production-grade Model Context Protocol (MCP) server for campervan/DIY-van-build technical and electrical planning, built with TypeScript and deployed on Cloudflare Workers.

It gives AI assistants (Claude, etc.) tool access to deterministic electrical calculations, payload/weight capacity checks, a component specs database (Cloudflare D1), and semantic search over van build manuals and documentation (Cloudflare Vectorize).

Features

• Deterministic calculations — All electrical, wire sizing, and weight math is implemented in pure TypeScript (src/utils/formulas.ts), never left to LLM guesswork.

• Component spec lookup — Query a D1-backed relational database of electrical/mechanical component specs (busbars, inverters, heaters, etc.).

• Semantic manual search — Search vectorized PDF/HTML documentation (wiring diagrams, body builder guides, error codes) via Cloudflare Vectorize.

• Stateless HTTP transport — Uses the MCP Streamable HTTP transport, well suited to Cloudflare Workers' request/response model.

Related MCP server: CalcsLive MCP Server

Tools

Architecture

src/
├── index.ts            # Worker entrypoint — wires up McpServer + HTTP transport
├── tools/
│   ├── electrical.ts    # Wire gauge & solar array validation tools
│   ├── payload.ts       # Van payload/weight capacity tools
│   ├── specs.ts         # D1-backed component spec lookup tools
│   └── manuals.ts       # Vectorize-backed semantic manual search tool
└── utils/
    ├── formulas.ts      # Pure TypeScript electrical/weight calculation helpers
    └── db.ts            # D1 database helpers

d1/
├── migrations/          # Sequential, non-destructive D1 schema migrations
└── seeds/               # Seed data for component_specs table

scripts/
└── ingest-docs.ts       # Ingests PDF/HTML docs into Vectorize for RAG search

tests/
├── unit/                # Unit tests for formulas and tools
└── e2e/                 # End-to-end tests against the MCP HTTP endpoints

The server binds to two Cloudflare resources (see wrangler.toml):

• D1 (DB) — relational database of component specs.

• Vectorize (VECTOR_INDEX) — vector index of chunked manual/documentation text, with an optional Workers AI (AI) binding for embeddings.

Prerequisites

• Node.js >= 20

• pnpm

• A Cloudflare account with Workers, D1, and Vectorize enabled (for deployment)

• Wrangler CLI (installed as a dev dependency)

Getting Started

Install dependencies:

pnpm install

Run the worker locally:

pnpm dev

Apply D1 migrations locally:

npx wrangler d1 migrations apply DB --local

Type-check the project:

pnpm tsc --noEmit

Testing

pnpm test          # run all tests (unit + e2e)
pnpm test:unit      # unit tests only
pnpm test:e2e       # end-to-end tests only
pnpm test:coverage  # unit tests with coverage

Server Smoke Test

scripts/smoke-test-server.sh spins up the worker locally with wrangler dev, applies local D1 migrations, and exercises the live MCP JSON-RPC endpoints (initialize, tools/list, and several tools/call requests) to verify the server actually runs end-to-end. Run it locally:

./scripts/smoke-test-server.sh

This same script runs in the MCP Server Smoke Test GitHub Actions workflow, which can be triggered manually (workflow_dispatch) or automatically on pull requests.

Linting

pnpm lint

Uses ESLint 9 flat config (eslint.config.mjs) with typescript-eslint recommended rules.

Deployment

Deployment uses two GitHub Actions pipelines with CLOUDFLARE_ACCOUNT_ID and CLOUDFLARE_API_TOKEN configured as repository secrets:

1. Setup Cloudflare Infrastructure (workflow_dispatch, manual) — a one-time bootstrap pipeline that creates the D1 database, the Vectorize index, and the category/doc_type metadata indexes on that Vectorize index (required for metadata filtering) if they don't already exist. Run it once from the Actions tab, then copy the printed database_id into wrangler.toml, commit, and push to main.

2. Deploy to Production (runs automatically on every push to main) — applies any new D1 migrations in d1/migrations/ and deploys the Worker.

To deploy manually instead, update wrangler.toml with your own D1 database_id, then run:

pnpm deploy

Ingesting Documentation

Automated PDF Manual Ingestion (GitOps)

PDF manuals placed in docs/manuals/*.pdf are automatically parsed, chunked, embedded, and synced to the van_manuals_index Vectorize index by the .github/workflows/ingest-manuals.yml workflow whenever they are added, modified, or deleted on main. PDF parsing and chunking happen in the GitHub Actions runner (not the Cloudflare Worker) to avoid edge CPU limits.

npx wrangler vectorize create van_manuals_index --dimensions=768 --metric=cosine

# Ingest / update manuals
npx tsx scripts/ingest-manuals.ts --added-modified "docs/manuals/velit-heater-manual.pdf"

# Remove manuals that were deleted
npx tsx scripts/ingest-manuals.ts --deleted "docs/manuals/old-manual.pdf"

Each chunk is embedded via the Cloudflare Workers AI REST API (@cf/baai/bge-base-en-v1.5, 768 dimensions) and upserted into Vectorize with a deterministic vector ID ({sanitized_filename}#chunk_{index}), so re-ingesting an updated manual overwrites its previous chunks. Requires CLOUDFLARE_ACCOUNT_ID and CLOUDFLARE_API_TOKEN environment variables.

Metadata Schema

Every vector upserted by ingest-manuals.ts or sync-r2-manuals.ts carries the following metadata, so results can be filtered by document type/category in addition to full-text search:

{
  "filename": "travel/vancouver_island.pdf",
  "manual_title": "Vancouver Island",
  "title": "Vancouver Island",
  "doc_type": "road_trip_guide",
  "category": "travel",
  "chunk_index": 0,
  "total_chunks": 12,
  "text": "Raw extracted text chunk..."
}

doc_type/category are inferred from the file's folder prefix:

Filtering on category/doc_type requires Vectorize metadata indexes, which the setup-infrastructure.yml workflow creates automatically:

npx wrangler vectorize create-metadata-index van_manuals_index --property-name=category --type=string
npx wrangler vectorize create-metadata-index van_manuals_index --property-name=doc_type --type=string

Manual R2 Bucket Sync (Large Manuals)

Large PDF manuals (too big to comfortably commit to the git repo) can instead be uploaded to the campervan-mcp Cloudflare R2 bucket, including nested subfolders (e.g. heaters/velit-2026.pdf or electrical/victron/multiplus.pdf). Run the Manual Sync R2 Manuals to Vectorize workflow (.github/workflows/sync-r2.yml) from the GitHub Actions UI (workflow_dispatch) to recursively list the bucket, diff it against a manifest persisted in R2, and stream/chunk/embed/upsert only the added or modified PDFs while deleting vectors for any PDFs removed from the bucket.

# Sync R2 bucket manuals (uses R2_ACCESS_KEY_ID / R2_SECRET_ACCESS_KEY for the R2
# S3-compatible access, and CLOUDFLARE_ACCOUNT_ID / CLOUDFLARE_API_TOKEN for the
# Workers AI / Vectorize REST APIs)
npx tsx scripts/sync-r2-manuals.ts

# Preview the diff without embedding/upserting anything
npx tsx scripts/sync-r2-manuals.ts --dry-run

# Force re-indexing of every PDF in the bucket, ignoring the manifest cache
npx tsx scripts/sync-r2-manuals.ts --force-reindex

Vector IDs are derived from the full R2 object key (subfolder path included), e.g. electrical_victron_multiplus_pdf#chunk_0, so nested manuals with the same filename in different folders don't collide.

Legacy Ad-Hoc Ingestion Script

Older HTML/PDF sources can still be ingested individually using the legacy script:

npx tsx scripts/ingest-docs.ts --source ./docs/transit-body-builder.pdf --name ford_transit_body_builder

Requires CLOUDFLARE_ACCOUNT_ID and CLOUDFLARE_API_TOKEN environment variables.

Contributing

See AGENTS.md for repository conventions and rules that apply to automated/AI contributors (deterministic calculations, migration policy, schema validation with Zod, and testing thresholds), which are also good practices for human contributors.