pexafy-mcp

Stock photo search for AI assistants. An MCP 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.

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

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.

Liveness: GET /health (public, no auth).

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. When you hit a limit, the assistant tells you in-chat instead of failing with an opaque error.

Related MCP server: brave-image-mcp

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.

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").

One of image_url, image_file or image_base64 is required. Images are fetched server-side; max 20 MB.

photo_similar — more like this

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, 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, so you can run your own against your own key.

Requires Python 3.12+.

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 .):

pexafy-mcp                             # stdio (default)
PEXAFY_MCP_TRANSPORT=http pexafy-mcp   # remote Streamable HTTP

Claude Desktop / Claude Code, over stdio:

{
  "mcpServers": {
    "pexafy": {
      "command": "pexafy-mcp",
      "env": { "PEXAFY_API_KEY": "pexafy_api_…" }
    }
  }
}

Docker, over HTTP — see docker-compose.example.yml:

docker compose -f docker-compose.example.yml up -d
curl localhost:8765/health

Configuration

Everything is environment variables — see .env.example.

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

./run.sh test         # offline test suite (pytest)
./run.sh inspect      # MCP Inspector
./prepare.sh          # maintainers: regenerate the vendored assets/

Contributions welcome — see CONTRIBUTING.md.

License

MIT — see 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.