EdgeContext

EdgeContext is a small proxy for MCP servers that runs on Cloudflare Workers.

You put it between your AI client (like Claude Desktop) and your MCP servers. It forwards every request, and also gives you:

• Memory per project. Notes and recent activity are saved and survive restarts.

• API keys. Each team gets its own key and can only reach the servers it's allowed to use.

• Rate limiting. Each team gets a requests-per-minute limit.

• Caching. Results of slow tools can be reused (only for tools you choose).

• Logs. Tool calls are saved to Cloudflare R2.

It runs only on Cloudflare services, so you don't need your own server or database.

How it works

AI client  ──►  EdgeContext (Cloudflare Worker)  ──►  Your MCP server(s)
                  checks key, rate limit, cache
                  saves project memory and logs

Requests EdgeContext doesn't recognize are passed through unchanged, so your existing MCP setup keeps working.

Related MCP server: remote-mcp-server-authless

Built-in tools

EdgeContext adds three tools of its own:

Run it locally

You'll need Node.js 20+ and pnpm.

1. Install

pnpm install

2. Configure. Copy .dev.vars.example to .dev.vars and set your MCP server URL:

UPSTREAM_MCP_SERVERS={"default":"http://127.0.0.1:8902/mcp"}

3. Create a test API key. Only the key's hash is stored:

# Hash the key "demo-key-123"
node -e "console.log(require('crypto').createHash('sha256').update('demo-key-123').digest('hex'))"

# Save it (replace <hash> with the output above)
pnpm exec wrangler kv key put --local --preview --binding TENANTS \
  "tenant:<hash>" '{"name":"demo","allowedServers":["default"],"rateLimitRpm":60}'

4. Start it

pnpm dev

5. Try it

curl -X POST http://127.0.0.1:8787/mcp/default \
  -H 'Authorization: Bearer demo-key-123' \
  -H 'X-EdgeContext-Project: my-project' \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc":"2.0","method":"tools/list","id":1}'

Deploy to Cloudflare

1. Create storage

pnpm exec wrangler kv namespace create TENANTS
pnpm exec wrangler kv namespace create TENANTS --preview
pnpm exec wrangler kv namespace create CACHE
pnpm exec wrangler kv namespace create CACHE --preview
pnpm exec wrangler r2 bucket create edgecontext-logs
pnpm exec wrangler r2 bucket create edgecontext-logs-preview

2. Paste the IDs. Each kv namespace create command prints an ID. Paste them into wrangler.toml, replacing the placeholder id and preview_id values.

3. Set your MCP servers and deploy

pnpm exec wrangler secret put UPSTREAM_MCP_SERVERS
pnpm exec wrangler deploy

Add a real API key the same way as in step 3, using --remote instead of --local --preview.

Connect your AI client

Point your client to https://<your-worker>/mcp/<serverName> and send two headers:

• Authorization: Bearer <your-api-key>

• X-EdgeContext-Project: <project-name>, which picks the project's memory

For an example, see examples/claude-desktop-config.json.

Settings

Design choices

• Rate limiting uses Durable Objects, not KV. KV can't count requests safely when many arrive at once, so it would let too many through.

• API keys are stored only as SHA-256 hashes. The real key can't be recovered from storage.

• Each project has its own Durable Object. Projects can't read each other's memory.

• Batches can't bypass the rate limit. Every request inside a batch uses one slot.

• Errors are never cached. If the cache fails, the request goes to the MCP server as normal.

Tests

pnpm test
pnpm typecheck

The 147 tests run inside the real Cloudflare Workers runtime, with real KV, Durable Objects and R2. Only the upstream MCP server is mocked.

Known limitations

• There's no dashboard for logs. They are only stored in R2.

• Requests in a batch run one at a time. This keeps them in order but is slower.

• Cached results of streamed calls come back as plain JSON, not as a stream.

License

MIT. See LICENSE.