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Stock photo search for AI assistants — remote MCP server with OAuth, three tools, results rendered inline.

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Last scanned: 8/21/2026
Install in Claude Code / Claude Desktop
Method: pip / Python · .
Claude Code CLI
claude mcp add pexafy-mcp -- python -m .
claude_desktop_config.json (Claude Desktop)
{
  "mcpServers": {
    "pexafy-mcp": {
      "command": "python",
      "args": ["-m", "."]
    }
  }
}
1. Run the command above in your terminal (Claude Code), or paste the JSON config into claude_desktop_config.json (Claude Desktop).
2. Replace any <placeholder> values with your API keys or paths.
3. Restart Claude. The MCP server and its tools appear automatically.
💡 Install first: pip install .
Use cases

MCP Servers overview

# pexafy-mcp

[![CI](https://github.com/Pexafy/pexafy-mcp/actions/workflows/ci.yml/badge.svg)](https://github.com/Pexafy/pexafy-mcp/actions/workflows/ci.yml)
[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](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 Pexafy result grid, rendered inline in a Claude conversation](docs/screenshot-grid.jpg)

---

## 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:

![Asking for more photos like #1, and the assistant reasoning over the new set](docs/screenshot-similar.jpg)

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:

![The detail panel: photographer, source, resolution, licence, dominant colour, orientation and description](docs/screenshot-detail.jpg)

---

## 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

Everything is environment variables — see [`.env.example`](.env.example).

| Variable | Default | Purpose |
|---|---|---|
| `PEXAFY_API_BASE_URL` | `http://localhost:8000` | Pexafy API root |
| `PEXAFY_API_KEY` | — | Fallback key (stdio/dev) |
| `PEXAFY_MCP_TRANSPORT` | `stdio` | `stdio` or `http` |
| `PEXAFY_THUMB_BASE_URL` / `PEXAFY_THUMB_HMAC_SECRET` | — | Enable the inline grid (signed thumbnails) |
| `PEXAFY_OAUTH_*` / `MCP_RESOLVE_SECRET` | — | Per-user OAuth (HTTP only) |

---

## 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).
ai-agentsclaudeimage-searchmcpmcp-servermodel-context-protocoloauthpythonsemantic-searchstock-photos

What people ask about pexafy-mcp

What is Pexafy/pexafy-mcp?

+

Pexafy/pexafy-mcp is mcp servers for the Claude AI ecosystem. Stock photo search for AI assistants — remote MCP server with OAuth, three tools, results rendered inline. It has 0 GitHub stars and its last recorded update is dated 2026-08-20.

How do I install pexafy-mcp?

+

You can install pexafy-mcp by cloning the repository (https://github.com/Pexafy/pexafy-mcp) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.

Is Pexafy/pexafy-mcp safe to use?

+

Our security agent has analyzed Pexafy/pexafy-mcp and assigned a Trust Score of 95/100 (tier: Verified). See the full breakdown of passed checks and flags on this page.

Who maintains Pexafy/pexafy-mcp?

+

Pexafy/pexafy-mcp is maintained by Pexafy. The last recorded GitHub activity is dated 2026-08-20, with 0 open issues.

Are there alternatives to pexafy-mcp?

+

Yes. On ClaudeWave you can browse similar mcp servers at /categories/mcp, sorted by popularity or recent activity.

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