write-gatekeeper

# Writing a Gatekeeper A Gatekeeper is a Cloudflare Worker that mediates all access between a Gadget and an external service. It implements a three-tier hierarchy: - **Vendor** (`GatekeeperVendor`, a `WorkerEntrypoint`) — top-level entry for the service. One per service. - **User** (`GatekeeperUser`, a `WorkerEntrypoint` with `ctx.props`) — a human user's authenticated connection. - **Instance** (`Gatekeeper<Session>`, a DO facet of the Overseer) — per-resource, per-Gadget binding that provides the Session API. Read `packages/workshop-shared/src/gatekeeper.ts` for the canonical interfaces and detailed JSDoc. ## Seven responsibilities 1. **Auth management** — Manage authorization to the external service via OAuth (or similar), on behalf of the human end user. This means managing "connected accounts" — token storage, refresh, and revocation in a `UserAccount` Durable Object. 2. **API design** — Provide a TypeScript API wrapper around the service's API, compatible with Cap'n Web RPC. The interface should be designed around capability-based security: object-oriented, with separate interfaces representing logical resources. For example, the Google Docs gatekeeper provides an interface to a *specific* document, rather than a coarse-grained interface where you pass the doc ID to every method. **IMPORTANT:** When writing a new gatekeeper, design a proposed API and then STOP to let the operator review and make changes before proceeding with the rest of the implementation. Getting the API right is the most important and delicate part of creating a new gatekeeper. 3. **Fine-grained resource granting** — Enable the end user to grant access to agents at fine granularities, in addition to coarse-grained access. For example, a user may want to give an agent access to a specific Google Doc or GitHub repo, rather than granting broad access to everything they can do. This should be straightforward given a capability-based API. That said, broad access should also be allowed when it makes sense. Consider carefully which granularities are meaningful — a Jira gatekeeper might support "whole service", "project", and "issue" granularities, but it would be silly to support granting access to a single field of an issue separately. 4. **Logging & approvals** — Every action the agent or gadget performs must be logged via the `ApprovalQueue` API. Every action with an externally-visible side effect must be submitted via `submitAction()`, and must not actually be performed until `applyAction()` has been called. Read-only observations must call `authorizeObservation()` before returning data to the caller. Reads the gatekeeper makes for its own purposes, returning nothing to the caller, are not observations (see [What counts as an observation](#what-counts-as-an-observation)). 5. **Caching** — When it makes sense, cache remote content in the gatekeeper's DO storage to improve performance when agents or gadgets repeatedly read the same data. Caching also enables a better TypeScript API when the service's underlying API has an inconvenient data shape. For example, Gmail's API for listing threads returns only thread IDs without metadata, requiring a callback for each thread; with caching, the gatekeeper can provide an API that returns rich thread summaries directly, reading from local content synchronized with Gmail as needed. See Phase 2 for implementation guidance. 6. **Simulation** — Actions submitted but not yet applied should be simulated as if they already occurred, to the maximum extent reasonable. If the caller reads back data, it should observe the data as if pending actions had been applied, even though they haven't yet. This allows the agent to continue working without waiting for each approval, and allows the end user to batch-approve a lot of work at once. Simulation may leverage caching (updating the cache on submit, clearing or repopulating it on reject), or it may work by storing pending actions separately and adjusting read results at query time — the latter is arguably cleaner but trickier to implement correctly. See Phase 2 for implementation guidance. 7. **Observer verification** — When a Gadget is shared, collaborators may "observe" data the Gadget previously read through the gatekeeper. The gatekeeper must ensure each collaborator could access that data themselves, via `getVerifier()` / `addObserver()` / `removeObserver()`. The interface methods are mandatory — a gatekeeper won't type-check without them — so include at least minimal versions in Phase 1; but *choosing and implementing the right strategy* is a Phase 2 security concern, like logging/approvals. See [Observer verification](#observer-verification). ## Phase 1: Core implementation In the first phase, focus only on responsibilities 1 - 3, though keeping in mind that 4 - 7 will need to be implemented later. Note the observer methods of responsibility 7 (`getVerifier`/`addObserver`/`removeObserver`) are required for the code to type-check, so the skeleton includes minimal versions; you flesh out the actual strategy in Phase 2. ### Step 1: Understand the external service Study the service's API docs. Identify: - Auth model (OAuth 2.0, API keys, etc.) - Resources to expose and what access granularities make sense - Which operations are observations (read-only) vs. actions (side effects) ### Step 2: Design the Session types Create `src/types.d.ts` defining the Session interface (and Hook interface if the service pushes events — see [Hooks](#hooks-push-notifications)). Before designing, read `packages/workshop-shared/node_modules/capnweb/README.md` to understand what Cap'n Web RPC supports — this determines what types and patterns are expressible in the Session interface. Design principles: - One interface per logical resource type, not a god-object - Methods return structured data, not raw API responses - Use capability-based design principles: make it easy to limit authority in useful ways by simply limiting access to specific objects or allowing/blocking specific methods - Simplify API complexities that are not likely to matter to agents and gadgets; design for a more novice user and common use cases - Consider what URL patterns `getGatekeeperClassFor()` should match — each pattern maps to a resource granularity - Include JSDoc comments; these types serve as the agent's API documentation. See [Documenting the API](#documenting-the-api-typesdts) below. #### Documenting the API (`types.d.ts`) This JSDoc is the agent's sole documentation for the API, so keep it **narrowly focused on what the agent needs to use each method**: what it does, its parameters, the returned data shape, and any errors the caller must handle. Do NOT leak details the caller doesn't need to use the API — the approval queue (never mention `submitAction`/`applyAction`/approvals; correct simulation keeps this invisible), or gatekeeper internals (caching, DO storage, OAuth, syncing). Document those in the `.ts` implementation or PR, never in the agent-facing `.d.ts`. The agent receives the `.d.ts` text **verbatim** (via the `types.txt` symlink), and nothing resolves its imports — so it must be **self-contained**. Don't import types from other packages or files (e.g. `Cursor` from `@gadgets/workshop-shared/gatekeeper`); copy the definition into the `.d.ts` instead. TypeScript's structural typing means the implementation can still return the shared type. The only exception is `cloudflare:workers` (e.g. `import type { RpcTarget } from "cloudflare:workers"`), which the agent's environment provides. `pnpm lint` enforces this with the `gadgets/self-contained-agent-types` rule on every `packages/gatekeeper-*/src/*types.d.ts`. Also keep the file free of comments aimed at maintainers (such as "keep in sync with X"), since the agent reads those too. ### Step 3: STOP — Present API for review **Do not proceed without operator approval.** Present the proposed `types.d.ts` and explain the design: what resource granularities are supported, what Session methods do, and what trade-offs were made. The API is the most important and delicate part of a gatekeeper — getting it wrong means rebuilding. Wait for the operator to review and approve (or request changes) before continuing. Also confirm the JSDoc follows [Documenting the API](#documenting-the-api-typesdts) — no approval-queue or implementation details leaked in. ### Step 4: Implement See [SKELETON.md](SKELETON.md) for a complete implementation template. Package structure: ``` packages/gatekeeper-<name>/ ├── src/ │ ├── configurator/ # Optional resource-picker UI modules and UI-facing types │ ├── <name>.ts # Vendor, UserAccount, UserImpl, GatekeeperImpl, SessionImpl │ ├── types.d.ts # Session/Hook types (compile-time) │ ├── types.txt -> types.d.ts # Symlink (runtime, for getTypeScriptTypes()) │ └── <name>-api.ts # (optional) Helper wrapping the service's HTTP API ├── cloudflare.config.ts # wrangler.jsonc is generated from it ├── package.json └── tsconfig.json ``` ### Step 5: Register There is no binding to add. `run-dev-server.ts` binds every `packages/gatekeeper-*` that has a `wrangler.jsonc` to the backend as `GATEKEEPER_<NAME>`, and the deploy service does the same for installed gatekeepers. The backend auto-discovers vendors from `GATEKEEPER_`-prefixed bindings (see `packages/workshop-backend/src/user.ts`). ### Step 6: Add resource selection UI Add a resource selection UI for each resource type returned in `getSupportedResources()`. This will be used by users to select the specific resource. - Workshop calls `GatekeeperUser.startResourceConfigurator(resourceUrlPattern)` with the selected resource's `urlPattern`. - Return `iframeHtml` of the selection UI and `ui` for any RPCs that UI needs. - When the user selects "Add connection", Workshop asks the iframe for the selected resource URL. Keep the iframe-facing capability narrow, only what's necessary to provide desired interface to help user find and select the resource. #### Optional helper: `@gadgets/configurator-ui` For simple configuration UIs, consider using `@gadgets/configurator-ui`. It provides the basic form components that look consistent to the Gadget Workshop and a build script that turns `src/configurator/*-ui.tsx` into `iframeHtml`. Gatekeepers with more specialized UI needs can produce their own `iframeHtml`. If you use this: - UI modules live in `src/configurator/*-ui.tsx`. - `resourceUrl()` returns the selected resource URL. - `src/configurator/*-types.d.ts` describes the iframe-facing `ui` API. - `scripts/build-gatekeeper-configurator.ts` generates `src/generated/*.txt`. - Nothing invokes `build-gatekeeper-configurator.ts` by hand. `vite.config.ts` re-exports the shared `build` and `build:configurator` Vite+ tasks from `scripts/gatekeeper-configurator-vite-config.ts`; `build` is just `tsc` and depends on `build:configurator`, which carries `VITE_FRONTEND_ERROR_REPORTING` in its fingerprint, and `deploy` runs `vp run --no-cache build:configurator && wrangler deploy` — deploys never replay a cached artifact. There is no `build` script and no direct builder call, because a script running the builder gets vp's stripped environment and bakes the wrong flag into the shipped HTML. ##### Pre-filling the form from a known resource URL When something already knows the exact resource — most importantly an AI agent's `requestConnection` (which passes a concrete `resourceUrl`) — the configurator should open **pre-filled and editable**, not blank. The runtime handles this for you: - If your value keys already match the `urlPattern`'s named groups (e.g. pattern `.../area/:areaId` with a value key `areaId`), prefill works automatically — no code needed. - Otherwise, implement the optional `initialValuesFromResourceUrl({ resourceUrl, resourceUrlPattern, ui })` on your spec to map a concrete URL back to your form values. Keep it pure where possible (parse the URL); it may use `ui` and may be async. Example (GitHub, whose value is `repoFullName` but pattern is `:owner/:repo`): ```ts initialValuesFromResourceUrl({ resourceUrl }) { const [owner, repo] = new URL(resourceUrl).pathname.split("/").filter(Boolean); return owner && repo ? { repoFullName: `${owner}/${repo}` } : {}; }, ``` The runtime seeds these values before first render and reflects them in `Autocomplete`/`TextInput`/`RadioCards` inputs. Make sure your `resourceUrl(values)` and `initialValuesFromResourceUrl(url)` are inverses so a prefilled form round-trips to the same URL. **Every gatekeeper with selectable resources should support this** so agents can fully pre-configure a connection. ### Step 7: STOP — Ask operator whether to proceed to phase 2 The operator may prefer to implement phase 2 later, perhaps in a new context. Stop here and ask the operator whether to proceed. ## Phase 2: Logging, approvals, caching, simulation, and observers In this phase, we focus on responsibilities 4-7. These are typically added as a second pass, after the core gatekeeper works. They may be implemented in a separate session. ### Logging and approvals Go through all the API methods and decide where to insert calls to the `ApprovalQueue`. - Any operation which reads external data and returns it to the caller (but with no side effects) must call authorizeObservation(). - Any operation which has visible side effects on the world must call submitAction(), and must not actually apply the action until approved. Study the `ApprovalQueue` API in `gatekeeper.ts` for details. #### What counts as an observation The observation log records what the **caller** (agent or gadget) learned, not what the gatekeeper read. Deciding this correctly keeps the log readable for the user. Ask: *what information did this call hand back to the caller?* If the answer is "nothing", don't log an observation, however much the gatekeeper fetched internally. Do **not** call `authorizeObservation()` for: - **Reads that only prepare an action.** A mutation that fetches current state to build the action description, bind an expected old value, capture revert information, or validate the request, and then calls `submitAction()`, returns `void` (or a provisional stub or a freshly generated ID). The action description the user approves already shows what was read. Don't log "Read X before mutation". - **Session setup.** `startSession()` resolving the account, a bound label, and so on returns only a session stub. - **Creating a cursor or stub that returns no data itself**, provided its later reads (cursor pages, `getDetails()`, …) log their own observations. Log wherever the data actually crosses to the caller; if a cursor logs once up front and not per page, that up-front log is the real one and must stay. - **Information that leaks only through a validation error** ("label already exists", "branch is not a fast-forward"). **Existence checks are a judgment call, but usually not an observation.** A method like `getIssue(id)` that confirms an item exists and returns a stub tells the caller one bit. When the ID is an unguessable, high-entropy value (a Gmail message ID, a commit SHA, a UUID), that bit is no information at all: a caller that already holds the ID knows the item exists, and the ID itself encodes nothing. Sequential numbers and human-readable names are more ambiguous, but an agent leaking information by probing whether titles exist is far-fetched, so the default is not to log. Log the stub's real reads instead. Don't put fetched details (such as the item's title) into a log entry for data the caller never received. Returning a boolean or count *is* data when that is the answer the caller asked for (`isFollowing()`, `areTracksSaved()`, a `null` from `getArea()` meaning "no area"). Log those. **Exception: observer tracking.** Under observer strategy C (below), `authorizeObservation()` does more than log. Its `excludeObservers` and set-tracking side effects gate which collaborators may see the workspace, and `containsRestrictedData` / `ownerInvitesOnly` change workspace state. A call that carries any of these is a security control, and removing it is a security decision, not a logging cleanup (see the Drive session's "Check Google Drive folder" fences). It's critically important that you add `ApprovalQueue` to all API operations that interact with the outside world, otherwise the gatekeeper security model is broken. ### Caching Store fetched data in the gatekeeper's DO storage (`this.ctx.storage`) to avoid redundant API calls. The cache also enables a better API shape when the service's native data model is awkward — e.g., Gmail's list API returns thread IDs without metadata, but with caching the Session can return richer summaries directly. - Use TTLs or revision IDs to keep the cache fresh. - Cache transformed data (e.g., Markdown) rather than raw API responses when the transformation is expensive. PROTIP: The relatively new API `this.ctx.storage.kv` provides synchronous versions of the traditional Durable Object storage API, e.g. `get` and `put`. Use these instead of the old asynchronous methods. (Note that the synchronous API does not provide "batch" versions of `get()` and `put()`, but you don't really need them since simply making multiple calls is efficient.) PROTIP: `this.ctx.storage.sql` gives you access to a full, private SQLite database. Use this when the full power of SQL is useful, but prefer KV for simple things. The full Durable Objects storage API (including synchronous KV and SQLite) is documented at: https://developers.cloudflare.com/durable-objects/api/sqlite-storage-api/ ### Simulation When `submitAction()` has been called but `applyAction()` hasn't, reads should reflect the pending action. This allows the calling agent or gadget to be unaware of the approvals mechanism, and proceed with follow-on work immediately. The end user is able to approve a whole batch of changes at once, later on. Two possible implementation approaches include: 1. **Mutate the cache** — Apply the action's effects to cached data on submit. On `rejectAction()`, invalidate or rebuild the cache. Simple; works well when the cache is already a transformed view. Don't forget to re-apply any queued actions when updating the cache. 2. **Overlay at read time** — Store pending actions separately; merge them into read results on demand. Cleaner separation; better when the overlay logic is straightforward. Choose based on the service's data model and the complexity of simulating each action type. Keep in mind that the agent calling the API (or the agent writing a gadget to call it) is generally not aware that actions do not take place immediately. If the simulation is correct, the agent doesn't need to be aware. If the simulation has gaps, you may want to mention it in your API's doc comments, so that the calling agent knows to work around them — but ideally there are no gaps and the calling agent does not need to think about it. For concrete examples, see the Google gatekeeper's Google Docs simulation/cache handling and BigQuery dry-run scope enforcement. ### Observer verification This is responsibility 7. When a Gadget is shared, each non-owner collaborator becomes an **observer** of every gatekeeper bound to the Gadget, and may see data the Gadget previously read. The gatekeeper's job is to refuse — or forward-restrict — observers who couldn't access that data themselves. Three methods implement this (full JSDoc in `gatekeeper.ts`): - `GatekeeperUser.getVerifier()` — mints a `GatekeeperUserVerifier` (a persistent service stub) representing *this* user's account. The overseer mints one per open and **only ever passes it back to a gatekeeper of the same vendor**, so the gatekeeper may trust whatever it learns from it. - `Gatekeeper.addObserver(id, verifier)` — must **throw** if the user represented by `verifier` is not allowed to observe everything read through this gatekeeper so far. The overseer calls it on **every open by every authorized observer** (re-verification, so revoked access is caught at the next open); cache as needed if the check is expensive. `id` is an opaque, stable per-(user,gadget) string. - `Gatekeeper.removeObserver(id)` — idempotent; drop a tracked observer. #### The verifier "non-standard method" pattern `GatekeeperUserVerifier` has no methods of its own — it's an opaque token. To actually answer "can this observer access X?", define a vendor-specific interface that **extends `GatekeeperUserVerifier`** with your own methods, implement it on a `WorkerEntrypoint` that queries the service using the **observer's own token**, and cast the `Fetcher` back to that interface inside `addObserver`. The overseer's same-vendor guarantee is what makes the cast safe. ```typescript // In types/impl: a verifier interface with non-standard methods. export interface MyVerifierApi extends GatekeeperUserVerifier { hasResourceAccess(resourceId: string): Promise<boolean>; } type MyVerifierProps = { userObjectId: string }; export class MyVerifier extends WorkerEntrypoint<Env, MyVerifierProps> implements MyVerifierApi { async hasResourceAccess(resourceId: string): Promise<boolean> { // Query the service with the OBSERVER's own token (this.ctx.props.userObjectId). try { await myApiForObserver(this.ctx).getResource(resourceId); return true; } catch (error) { // Distinguish "no access" from "transient failure": // - auth/permission/not-found (401/403/404) → false (they can't see it) // - anything else → rethrow, so the open fails loudly rather than silently denying if (isNoAccessStatus(statusOf(error))) return false; throw error; } } } // In GatekeeperUser: async getVerifier(): Promise<Fetcher<GatekeeperUserVerifier>> { return this.ctx.exports.MyVerifier({ props: { userObjectId: this.ctx.props.userObjectId } }); } ``` `MyVerifier` is a `WorkerEntrypoint`, so it needs **no migration entry**, but (like all entrypoints) it must be `export`ed from the worker's main module so `ctx.exports.MyVerifier(...)` resolves. #### Choosing a strategy (per resource type / binding) Strategy is chosen **per `Gatekeeper` DO class / binding**, not per package — one package may use several (e.g. Google: Gmail=A, Doc=B, BigQuery=C). - **A — Private-only.** `addObserver()` always throws; `removeObserver()` is a no-op. `getVerifier()` must still exist (the overseer mints it) but is never consulted. Use when the resource is too sensitive to share and there is no per-observer access oracle (e.g. a personal Gmail mailbox). For truly sensitive data, also mark each observation with `ObservationDescription.containsRestrictedData: true`: because nobody can open the workspace without passing `addObserver()`, and with strategy A nobody ever does, the first such observation makes the workspace effectively unshareable — and it permanently sets the workspace's `containsRestrictedData` flag, whose restricted mode blocks all actions and public web fetches, so the data cannot leak back out through other gatekeepers. - **B — ACL check (single unit).** The binding is one atomic resource; sub-resources inherit its ACL. `addObserver()` calls a verifier method to confirm the observer can access it and throws otherwise; `removeObserver()` is a no-op; nothing is tracked and no `excludeObservers` is ever needed. Use for repo / document / page / team / single-project bindings. - **C — Data-set tracking.** The binding spans sub-resources with **distinct ACLs**, and there is a **per-observer access oracle** for each. The DO logs the data sets actually observed and the current observers; `addObserver()` verifies the observer against **every** logged set (plus a coarse membership baseline) and **stores their verifier**; each later observation that first touches a **new** set re-checks all stored observers and sets `excludeObservers` for any who fail. Use for workspace / organization / dataset-spanning bindings.