Pexafy MCP Server
Official# pexafy-mcp
[](https://github.com/Pexafy/pexafy-mcp/actions/workflows/ci.yml)
[](LICENSE)
**Stock photo search for AI assistants.** An [MCP](https://modelcontextprotocol.io)
server that lets Claude, ChatGPT or any MCP client search a library of royalty-free
images — by describing a scene in plain language, from an example image, or "more
like this" — and render the results as a thumbnail grid **inside the conversation**.
> Remote MCP, OAuth, no API key to paste, 3 tools, images rendered inline.
The product page, with the same steps in twenty-three languages, is at
[pexafy.com/mcp](https://pexafy.com/mcp/).

---
## Use it (nothing to install)
A hosted server runs at:
```
https://mcp.pexafy.com/mcp
```
It speaks Streamable HTTP and authenticates with **OAuth 2.1** — you sign in to
Pexafy in a browser window and the connector receives its own credentials. There is
no API key to generate, paste into a JSON file, or rotate later.
### Claude (web and desktop)
1. Open **Settings → Connectors** (on Team/Enterprise, an owner adds it once under
**Organization settings → Connectors**).
2. Click **Add custom connector**.
3. Paste `https://mcp.pexafy.com/mcp` and confirm.
4. Sign in to Pexafy in the window that opens. Done — ask Claude for a photo.
### Claude Code
```bash
claude mcp add --transport http pexafy https://mcp.pexafy.com/mcp
```
### Any other MCP client
Point it at the same URL with the `streamable-http` transport. Clients that don't
implement OAuth can authenticate instead with a Pexafy API key sent as
`Authorization: Bearer <key>` or `x-api-key: <key>` — get one from the
[dashboard](https://pexafy.com/dashboard/api-keys/).
Liveness: [`GET /health`](https://mcp.pexafy.com/health) (public, no auth).
Also listed in the [official MCP registry](https://registry.modelcontextprotocol.io)
as `com.pexafy/pexafy-mcp`, and on
[Smithery](https://smithery.ai/servers/pexafy/pexafy-mcp) — where a hosted
gateway URL is available for clients that prefer it.
### What it costs
The Free plan covers 5,000 searches a month with one connector — enough for regular
use, no card required. Higher tiers are on the [pricing page](https://pexafy.com/pricing/).
When you hit a limit, the assistant tells you in-chat instead of failing with an
opaque error.
---
## Tools
Three read-only tools. No write scope, no account mutation.
### `search_photos` — semantic text search
Describe the scene in a full sentence; Pexafy is semantic, so sentences beat
keywords. All parameters are optional, but pass either `q` or at least one filter.
| Parameter | Type | Notes |
|---|---|---|
| `q` | string | The scene, in natural language. Max 500 characters. |
| `color_name` | string | One of: red, orange, yellow, green, blue, purple, pink, brown, black, white, gray, teal, beige, gold, navy. Excludes `color_hex`. |
| `color_hex` | string | e.g. `#1E90FF`. Excludes `color_name`. |
| `color_tolerance` | integer | 0 (exact) to 255 (loose). Default 20. Only with `color_hex`. |
| `orientation` | string[] | `landscape`, `portrait`, `square`. |
| `source` | string[] | Unsplash, Pexels, Pixabay, Kaboompics, Burst, StockSnap, Picjumbo, Skitterphoto, NegativeSpace. |
| `license_type` | string[] | `free`, `cc0`. |
| `photographer` | string | Exact username. |
| `after_date` | string | `YYYY-MM-DD`. Published on or after. |
| `cursor` | string | `pagination.next_cursor` from a previous response. |
### `search_photos_by_image` — visual search from an example
Finds photos that look like a reference image, optionally tweaked in words
("like this, but at night").
| Parameter | Type | Notes |
|---|---|---|
| `image_url` | string | Public http(s) URL of the reference image. |
| `image_file` | object | Auto-filled by hosts that support uploads (e.g. ChatGPT). |
| `image_base64` | string | Raw base64 bytes, for programmatic clients. |
| `q` | string | Text to combine with the image ("but with hands raised"). |
| `text_alpha` | number | Weight of `q` against the image. |
| `orientation`, `source`, `color_name`, `license_type`, `photographer`, `after_date` | string | Same filters as above. |
| `cursor` | string | Pagination token. |
One of `image_url`, `image_file` or `image_base64` is required. Images are fetched
server-side; max 20 MB.
### `get_similar_photos` — more like this
| Parameter | Type | Notes |
|---|---|---|
| `photo_id` | string | **Required.** A photo's UUID, taken from a previous result. |
| `cursor` | string | Pagination token. |
### What comes back
Every photo carries its id, URLs at several sizes, dimensions, dominant colour,
orientation, source, licence, photographer, and an `attribution` string to display
as credit — enough for the assistant to reason about the results rather than just
list them.
Results are numbered `#1, #2, …`, so you refer to a photo the way you would in
conversation. No ids to copy around:

In clients that support [MCP Apps](https://modelcontextprotocol.io), clicking a
thumbnail opens a detail panel with the full metadata — no extra call, it is all in
the tool result already:

---
## Self-host
You don't need to — the hosted server above is the intended way in. But the server
is a thin, plain client of the [Pexafy API](https://api.pexafy.com/schema.json), so
you can run your own against your own key.
Requires Python 3.12+.
```bash
git clone https://github.com/Pexafy/pexafy-mcp.git && cd pexafy-mcp
./run.sh setup # venv + editable install + seed .env
# edit .env — set PEXAFY_API_KEY
./run.sh dev # stdio, for Claude Desktop / Claude Code
```
With the installed console script (`pip install .`):
```bash
pexafy-mcp # stdio (default)
PEXAFY_MCP_TRANSPORT=http pexafy-mcp # remote Streamable HTTP
```
Claude Desktop / Claude Code, over stdio:
```json
{
"mcpServers": {
"pexafy": {
"command": "pexafy-mcp",
"env": { "PEXAFY_API_KEY": "pexafy_api_…" }
}
}
}
```
Docker, over HTTP — see [`docker-compose.example.yml`](docker-compose.example.yml):
```bash
docker compose -f docker-compose.example.yml up -d
curl localhost:8765/health
```
The image itself defaults to **stdio**, the transport an MCP client uses to drive a
container, so it also works directly:
```bash
docker run -i --rm pexafy-mcp
```
That answers `initialize` and `tools/list` with no API key and no network — the
tools come from the vendored OpenAPI snapshot. A key is only needed to run a search.
Serving over HTTP is a matter of setting the transport, which both compose files do.
### Configuration
Every setting is an environment variable, and every one of them is optional: with
none set, `pexafy-mcp` starts on stdio and answers `initialize` and `tools/list`
offline. Two are worth knowing about.
| Variable | Default | Purpose |
|---|---|---|
| `PEXAFY_MCP_TRANSPORT` | `stdio` | `stdio` for a local client, `http` to serve remotely |
| `PEXAFY_API_BASE_URL` | `http://localhost:8000` | Pexafy API root — point it at `https://api.pexafy.com`, or at your own deployment |
The rest belongs to a deployment rather than to someone running the container, and
lives in [`.env.example`](.env.example): a fallback `PEXAFY_API_KEY` for stdio use
when the client sends no key of its own, `PEXAFY_THUMB_BASE_URL` and
`PEXAFY_THUMB_HMAC_SECRET` to sign the thumbnails behind the inline grid, and
`PEXAFY_OAUTH_*` with `MCP_RESOLVE_SECRET` to run the HTTP transport as an OAuth
resource server. None of them is needed to start the server.
---
## How it works
```
src/pexafy_mcp/
├── server.py # entry point: builds the server, wires hooks, custom tools, /health
├── tooling.py # tunes the OpenAPI-derived tools for an LLM (descriptions, value sets)
├── widget.py # MCP Apps UI resource — the inline result grid (self-contained HTML)
├── previews.py # signs the thumbnail URLs injected into each result
├── limits.py # turns plan-limit (429) responses into in-chat upgrade nudges
├── auth.py # per-user auth: OAuth Resource Server or forwarded API key
└── assets/ # vendored, shipped with the package:
├── openapi.json # OpenAPI snapshot the tools are generated from
├── facets.json # evolving source/license value sets
└── ext_apps_bundle.js # @modelcontextprotocol/ext-apps SDK (inlined in the widget)
```
- The tools are **generated** from the Pexafy OpenAPI spec via `FastMCP.from_openapi()`,
so the API stays the single source of truth; `tooling.py` then reshapes them for an
LLM — narrowing the surface to the search core, dropping parameters that mislead a
model, and inlining the closed value sets so no facet lookup is ever needed.
- `build_server()` assembles everything. **Importing the package has no side effects and
does no network I/O**: it reads the vendored `assets/openapi.json` and `assets/facets.json`.
`prepare.sh` regenerates those.
- `search_photos_by_image` is hand-written: a chat assistant cannot upload a binary file
to an MCP tool, so the tool takes an image URL and fetches it server-side.
- The inline grid is an **MCP Apps** UI resource. The ext-apps client is bundled and
inlined, because the host's sandboxed iframe cannot fetch external scripts at runtime.
## Development
```bash
./run.sh test # offline test suite (pytest)
./run.sh inspect # MCP Inspector
./prepare.sh # maintainers: regenerate the vendored assets/
```
Contributions welcome — see [CONTRIBUTING.md](CONTRIBUTING.md).
## License
MIT — see [LICENSE](LICENSE).
The package also redistributes third-party assets (the Inter typeface, the
`@modelcontextprotocol/ext-apps` browser bundle and the libraries bundled into it),
each under its own licence — see [THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md).
TDQS
Scored across 3 tools
Each tool has a clearly documented input type (text query vs. image/file vs. previous photo_id), and the descriptions are explicit about which phrase or condition triggers each tool. However, search_photos_by_image and get_similar_photos both produce visually similar photos, and their boundary (one tweaks by text, the other just fetches similar) could occasionally mislead an agent even with the detailed guidance.
All names follow a snake_case verb_noun pattern (search_photos, search_photos_by_image, get_similar_photos), and the shared 'search_photos' prefix on two tools is helpful. The slight deviation is 'get' in get_similar_photos versus 'search' elsewhere for the same core concept, but the pattern is otherwise uniform and predictable.
Three tools is a lean but sensible footprint for a dedicated photo-search server, covering the natural query modalities (text, image, similar-by-id). While each tool does earn its place, the set feels slightly minimal—no dedicated tool for fetching individual photo details, but results already carry URLs and attribution, so it works.
The core workflow is complete: text query → results → similar-by-photo_id, and image query → results with tweakable text, covering the main stock-photo search use cases with no dead ends. Minor gaps exist (no downloadable/collections/curated feed support, no orientation/filter parameters), but agents can work around these with richer natural-language calls to search_photos.