What can you do with this server?

The Pexafy MCP Server lets AI assistants search and discover royalty-free stock photos inside conversations. It provides three read-only tools: * search_photos – semantic text search using natural-language descriptions, with filters for color (name/hex + tolerance), orientation, source/provider (Unsplash, Pexels, Pixabay, Kaboompics, Burst, StockSnap, Picjumbo, Skitterphoto, NegativeSpace), license type (free, CC0), photographer, publication date, and cursor pagination. * search_photos_by_image – visual search from a reference image supplied as a public URL, uploaded file, or base64 data, optionally refined with text (e.g., “like this but at night”) and adjustable text/image weighting. * photo_similar – “more like this” discovery using a photo ID from previous results. Results include rich metadata: multiple image URLs, dimensions, dominant color, orientation, source, license, photographer, AI-generated captions, and a ready-to-use attribution string. In supported MCP clients, photos render as an inline numbered thumbnail grid for easy reference and clickable links. The server supports OAuth 2.1 for the hosted service, API key authentication for self-hosted deployments, cursor-based pagination, and in-chat plan-limit notifications when usage limits are reached. All operations are read-only.

Which integrations are available for this server?

Allows searching and retrieving royalty-free stock photos from Pexels as a photo source. Allows searching and retrieving royalty-free stock photos from Pixabay as a photo source. Allows searching and retrieving royalty-free stock photos from Unsplash as a photo source.

How do I use Pexafy MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Pexafy MCP Server find an aerial photo of a tropical beach with turquoise water" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

de en es ja ko ru zh

Pexafy MCP Server

Official

by Pexafy

Overview Schema Related Servers Score Discussions

Python

Hybrid

pexafy-mcp

License: MIT

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.

The product page, with the same steps in twenty-three languages, is at pexafy.com/mcp.

The Pexafy result grid, rendered inline in a Claude conversation

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)

Open Settings → Connectors (on Team/Enterprise, an owner adds it once under Organization settings → Connectors).
Click Add custom connector.
Paste https://mcp.pexafy.com/mcp and confirm.
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).

Also listed in the official MCP registry as com.pexafy/pexafy-mcp, and on Smithery — 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. 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.

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

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:

The detail panel: photographer, source, resolution, licence, dominant colour, orientation and description

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

The image itself defaults to stdio, the transport an MCP client uses to drive a container, so it also works directly:

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: 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

./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.

Available Tools

3 tools

get_similar_photosFind similar photosA

Read-onlyIdempotent

Inspect

Use this tool when the user says 'find something similar', 'show me more like this', or 'I need a visually consistent set'. Requires a photo_id obtained from a previous search result. A person normally refers to a photo by the rank shown on the result grid rather than by its identifier; each search result carries both, in the same object. Each result carries: rank, its position on this page (1, 2, 3, …), which is also the number drawn on the inline grid and the handle a person naturally uses to refer to one photo among several; photo_id, the identifier the similar-photos tool takes, present in the same object as the rank; attribution, the credit line to display with the photo; and urls, the image at several sizes, urls.regular being the one to link to. Inline thumbnails are attached to this tool's result as an MCP App resource. Some clients, claude.ai on the web among them, render that resource only inside an expandable tool panel rather than in the reply itself; where it is not rendered, the photos remain reachable through their URLs.

ParametersJSON Schema

Name	Required	Description	Default
`cursor`	No	Token to fetch the next page. Take `pagination.next_cursor` from a previous response and pass it back here. See the Pagination guide.
`photo_id`	Yes	The reference photo's unique Pexafy identifier (a UUID).

Output Schema

ParametersJSON Schema

Name	Required	Description
`data`	No
`meta`	No
`error`	No
`success`	No
`pagination`	No

TDQS

A4.3/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior, so the safety profile is covered. The description adds useful behavioral context: the result includes inline thumbnails as an MCP App resource, and warns that some clients (like claude.ai on web) render them only in an expandable panel, with URLs still available. This goes beyond annotations and is valuable for the agent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is relatively lengthy but every paragraph serves a purpose: it explains when to use, prerequisites, how users refer to photos (critical for agent understanding), and display behavior. It is front-loaded with the primary usage trigger and then provides necessary details. Slightly verbose but justified by the need to explain the rank vs. photo_id distinction.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has an output schema, so return values are covered there. The description compensates for the complexity of the tool by explaining the relationship between rank and photo_id, which is not obvious from the schema. It also addresses pagination and resource rendering behavior. Given the moderate complexity and presence of output schema, this is adequately complete, though more details on what 'similar' entails could be added.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, with both parameters (photo_id and cursor) described in the schema. The description reinforces the use of photo_id (requires it from a prior search) and explains the cursor's role (pass pagination.next_cursor), but adds minimal additional semantics beyond the schema. Baseline 3 is appropriate given full schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it finds similar photos based on a photo_id, distinct from sibling search tools by focusing on similarity rather than keywords or image upload. It explicitly ties to user phrases like 'find something similar', making its purpose actionable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly explains when to use the tool ('when the user says...'), specifies the prerequisite (photo_id from a previous search), and details how a person refers to photos (by rank) versus the identifier, which prevents misuse. It also clarifies how to use the cursor for pagination.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_photosSearch photos by descriptionA

Read-onlyIdempotent

Inspect

Use this tool whenever the user needs an image, photo, or visual — for a presentation, blog, website, social-media post, mood board, or any creative project. Pexafy is a SEMANTIC search engine: describe the scene in full natural-language sentences, not keywords. Rich descriptions return far better results than tag-like queries. Good queries: 'a melancholy portrait of an old person sitting under a soft light'; 'two people sharing a bench in comfortable silence'; 'the last sunlight of the day hitting a dusty windowsill'; 'a child discovering snow for the first time'. Prefer this tool over search_photos_by_image when the user describes what they want in words. BUT if they want photos LIKE a specific image that has a URL — a photo from a previous result, or a public URL they gave — use search_photos_by_image instead (pass that URL, plus a q for any change like 'but with hands raised'). Only use THIS text tool for a reference image with NO URL (a file pasted/uploaded in the chat): describe what you see in rich detail — Pexafy is semantic, so a good description finds visually similar photos. Each result carries: rank, its position on this page (1, 2, 3, …), which is also the number drawn on the inline grid and the handle a person naturally uses to refer to one photo among several; photo_id, the identifier the similar-photos tool takes, present in the same object as the rank; attribution, the credit line to display with the photo; and urls, the image at several sizes, urls.regular being the one to link to. Inline thumbnails are attached to this tool's result as an MCP App resource. Some clients, claude.ai on the web among them, render that resource only inside an expandable tool panel rather than in the reply itself; where it is not rendered, the photos remain reachable through their URLs.

ParametersJSON Schema

Name	Required	Description
`q`	No	Your search query as a full natural-language sentence describing the scene you want — Pexafy is semantic, so sentences beat keywords. Up to 500 characters. Optional if you provide at least one filter instead. Example: 'an old man sitting at a café table he has visited every morning for thirty years'.
`cursor`	No	Token used to fetch the next page. Take the `pagination.next_cursor` value from a previous response and pass it back here. See the [Pagination](/pagination) guide.
`source`	No	Keep only photos from these providers: Unsplash, Pexels, Pixabay, Kaboompics, Burst, StockSnap, Picjumbo, Skitterphoto, NegativeSpace. Repeat the parameter to pass several.
`color_hex`	No	Keep only photos close to this hex color (e.g. `#1E90FF`). Cannot be combined with `color_name`. Use `color_tolerance` to widen or tighten the match.
`after_date`	No	Only return photos published on or after this date, formatted `YYYY-MM-DD`.
`color_name`	No	Keep only photos whose dominant color matches one of: red, orange, yellow, green, blue, purple, pink, brown, black, white, gray, teal, beige, gold, navy. Cannot be combined with color_hex.
`orientation`	No	Keep only photos with these shapes: landscape, portrait, square. Repeat the parameter to pass several.
`license_type`	No	Keep only photos with these license types: free, cc0. 'free' means the photo can be used freely and attribution is appreciated. Repeat the parameter to pass several.
`photographer`	No	Only return photos from this photographer's username. Use `GET /api/v1/facets/photographers/suggest` to find usernames.
`color_tolerance`	No	How far a photo's color may be from `color_hex` and still match, from `0` (exact match) to `255` (very loose). Defaults to `20`. Only applies when `color_hex` is set.

Output Schema

ParametersJSON Schema

Name	Required	Description
`data`	No
`meta`	No
`error`	No
`success`	No
`pagination`	No

TDQS

A4.8/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The annotations already declare a safe, read-only, idempotent operation, so the description's job is to add context beyond that. It does: semantic search behavior, result-field semantics (rank, photo_id, attribution, urls), the inline-thumbnail MCP resource, and the rendering caveat on claude.ai. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long, but it is front-loaded with the primary use case and every section earns its place: query style, examples, sibling distinction, result fields, and rendering behavior. A few example queries could be trimmed without losing meaning, which keeps it from a 5.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 10 optional parameters, an output schema, and two siblings, the description is complete: it explains semantic querying, when to use each sibling, what each result field means, and how the inline resource may render. The output schema covers return values, so the description correctly focuses on selection and invocation behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3 and the schema already documents every parameter. The description adds real value by teaching the core q semantics, showing strong example queries, and explaining how photo_id connects to the similar-photos sibling, but it does not need to repeat the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific use case ('user needs an image, photo, or visual') and names the resource being searched. It clearly differentiates this text-query tool from search_photos_by_image, which is the main sibling it could be confused with.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit when-to-use guidance ('Prefer this tool over search_photos_by_image when the user describes what they want in words') and names the alternative with the exact input it needs. It also handles the edge case of a reference image with no URL, telling the agent to describe it in rich detail instead.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_photos_by_imageSearch photos by example imageA

Read-onlyIdempotent

Inspect

Find visually similar stock photos from an EXAMPLE IMAGE, optionally TWEAKED with words. This is the right tool for 'find photos LIKE THIS but ' (e.g. 'like this but with their hands raised', 'the same scene but at night'). Give the reference image one of three ways: (1) image_url — a public http(s) link: a photo from a PREVIOUS search result (reuse its image_url/urls.regular), or any public URL the user provides; (2) image_file — auto-filled by the host when the user UPLOADS an image (e.g. ChatGPT) — it is populated by the host, not by the caller; (3) image_base64 — raw base64 image bytes, for a programmatic client that already holds the file. A chat assistant has no access to the exact bytes of an image it was shown, so image_base64 is not available to it. Put any change in q; raise text_alpha to weight the text more. If the reference image has no URL and the host did not auto-provide image_file (e.g. a file pasted into a chat that can't be forwarded), you cannot send it — describe what you see and use search_photos instead. Every result carries an attribution you show.

ParametersJSON Schema

Name	Required	Description
`q`	No	Your search query as a full natural-language sentence describing the scene you want — Pexafy is semantic, so sentences beat keywords. Up to 500 characters. Optional if you provide at least one filter instead. Example: 'an old man sitting at a café table he has visited every morning for thirty years'.
`cursor`	No	Token to fetch the next page. Take `pagination.next_cursor` from a previous response and pass it back here — no need to re-upload the image. See the Pagination guide.
`source`	No	Keep only photos from these providers: Unsplash, Pexels, Pixabay, Kaboompics, Burst, StockSnap, Picjumbo, Skitterphoto, NegativeSpace. Repeat the parameter to pass several.
`image_url`	No	Public http(s) URL of the reference image. Reuse the `image_url` of a photo from a previous search result, or any public URL the user provides.
`after_date`	No	Only return photos published on or after this date, formatted YYYY-MM-DD.
`color_name`	No	Keep only photos whose dominant color matches one of: red, orange, yellow, green, blue, purple, pink, brown, black, white, gray, teal, beige, gold, navy. Cannot be combined with color_hex.
`image_file`	No	Filled in by the host when the user uploads an image, not by the caller. Carries the upload's `download_url` and `file_id`.
`text_alpha`	No	Balance between your text and the image when both are provided, from `0` to `10`. `0` ignores the text (pure visual search), `1.7` (the default) is balanced, and higher values give your words more weight. Has no effect without `q`.
`orientation`	No	Keep only photos with these shapes: landscape, portrait, square. Repeat the parameter to pass several.
`image_base64`	No	The reference image as base64 bytes, optionally as a `data:` URL. For a client that already holds the bytes; prefer `image_url` when a link exists.
`license_type`	No	Keep only photos with these license types: free, cc0. 'free' means the photo can be used freely and attribution is appreciated. Repeat the parameter to pass several.
`photographer`	No	Only return photos from this photographer's exact username.

Output Schema

ParametersJSON Schema

Name	Required	Description
`data`	No
`meta`	No
`error`	No
`success`	No
`pagination`	No

TDQS

A4.6/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly and idempotent, so the description doesn't need to repeat that. It adds meaningful context beyond annotations: the image_file is host-populated rather than caller-set, image_base64 is unavailable to chat assistants, and every result carries an attribution to display. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but every section earns its place—it covers usage, input methods, edge cases, and attribution. The numbered list of image-providing options is clear and well-structured. It could be slightly trimmed, but the density is justified by the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the 12 parameters, 100% schema coverage, and an output schema, the description provides all necessary behavioral context: how to provide the reference image, the host-filling behavior of image_file, the text weighting mechanism, and the fallback to search_photos. It also mentions the attribution requirement from results, which is not in the schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents each parameter. The description adds practical nuance beyond the schema, such as how text_alpha weights text against image, and the guidance to put any modification in q. This exceeds the baseline for fully covered schemas.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool finds visually similar stock photos from an example image, optionally tweaked with words. It explicitly distinguishes from siblings by providing a usage scenario ('find photos LIKE THIS but <change>') and names the alternative (search_photos) when the image can't be sent.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use guidance with examples and a concrete fallback: when the image has no URL and no auto-provided file, use search_photos instead. It also explains the three ways to supply the reference image and which is appropriate for different clients.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

1 tool updatev0.4.9Aug 22, 2026

Changedsearch_photos_by_image19 fields changed

addedInput schema / properties / after_date / description

Added value: +"Only return photos published on or after this date, formatted YYYY-MM-DD."

addedInput schema / properties / color_name / description

Added value: +"Keep only photos whose dominant color matches one of: red, orange, yellow, green, blue, purple, pink, brown, black, white, gray, teal, beige, gold, navy. Cannot be combined with color_hex."

addedInput schema / properties / cursor / description

Added value: +"Token to fetch the next page. Take `pagination.next_cursor` from a previous response and pass it back here — no need to re-upload the image. See the Pagination guide."

addedInput schema / properties / image_base64 / description

Added value: +"The reference image as base64 bytes, optionally as a `data:` URL. For a client that already holds the bytes; prefer `image_url` when a link exists."

addedInput schema / properties / image_file / additionalProperties
```
Added value: +false
```

removedInput schema / properties / image_file / anyOf

Removed value: -[
-  {
-    "additionalProperties": true,
-    "type": "object"
-  },
-  {
-    "type": "null"
-  }
-]

removedInput schema / properties / image_file / default
```
Removed value: -null
```

addedInput schema / properties / image_file / description

Added value: +"Filled in by the host when the user uploads an image, not by the caller. Carries the upload's `download_url` and `file_id`."

addedInput schema / properties / image_file / properties

Added value: +{
+  "download_url": {
+    "type": "string"
+  },
+  "file_id": {
+    "type": "string"
+  },
+  "file_name": {
+    "type": "string"
+  },
+  "mime_type": {
+    "type": "string"
+  }
+}

addedInput schema / properties / image_file / required
```
Added value: +[
+  "download_url",
+  "file_id"
+]
```
addedInput schema / properties / image_file / type
```
Added value: +"object"
```

addedInput schema / properties / image_url / description

Added value: +"Public http(s) URL of the reference image. Reuse the `image_url` of a photo from a previous search result, or any public URL the user provides."

addedInput schema / properties / license_type / description

Added value: +"Keep only photos with these license types: free, cc0. 'free' means the photo can be used freely and attribution is appreciated. Repeat the parameter to pass several."

addedInput schema / properties / orientation / description

Added value: +"Keep only photos with these shapes: landscape, portrait, square. Repeat the parameter to pass several."

addedInput schema / properties / photographer / description

Added value: +"Only return photos from this photographer's exact username."

addedInput schema / properties / q / description

Added value: +"Your search query as a full natural-language sentence describing the scene you want — Pexafy is semantic, so sentences beat keywords. Up to 500 characters. Optional if you provide at least one filter instead. Example: 'an old man sitting at a café table he has visited every morning for thirty years'."

addedInput schema / properties / source / description

Added value: +"Keep only photos from these providers: Unsplash, Pexels, Pixabay, Kaboompics, Burst, StockSnap, Picjumbo, Skitterphoto, NegativeSpace. Repeat the parameter to pass several."

addedInput schema / properties / text_alpha / description

Added value: +"Balance between your text and the image when both are provided, from `0` to `10`. `0` ignores the text (pure visual search), `1.7` (the default) is balanced, and higher values give your words more weight. Has no effect without `q`."

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "data": {
+      "items": {
+        "description": "A photo result. Fields returned can be narrowed with the `fields` parameter and may depend on your plan.",
+        "properties": {
+          "alt_description": {
+            "description": "Accessibility-friendly text.",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "attribution": {
+            "description": "Ready-to-display credit for the photographer/source.",
+            "properties": {
+              "html": {
+                "description": "HTML attribution snippet.",
+                "type": "string"
+              },
+              "plain": {
+                "description": "Plain-text attribution.",
+                "type": "string"
+              }
+            },
+            "type": "object"
+          },
+          "blur_hash": {
+            "description": "BlurHash placeholder string.",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "color_hex": {
+            "description": "Dominant color hex code.",
+            "type": "string"
+          },
+          "color_name": {
+            "description": "Dominant color name.",
+            "type": "string"
+          },
+          "description": {
+            "description": "AI-generated caption.",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "height": {
+            "type": [
+              "integer",
+              "null"
+            ]
+          },
+          "image_url": {
+            "description": "Canonical source image URL.",
+            "format": "uri",
+            "type": "string"
+          },
+          "license_type": {
+            "description": "License type (e.g. `free`).",
+            "type": "string"
+          },
+          "orientation": {
+            "enum": [
+              "landscape",
+              "portrait",
+              "square"
+            ],
+            "type": "string"
+          },
+          "photo_id": {
+            "description": "Unique Pexafy identifier (UUID).",
+            "type": "string"
+          },
+          "photographer_full_name": {
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "photographer_url": {
+            "format": "uri",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "photographer_username": {
+            "type": "string"
+          },
+          "relevance_score": {
+            "description": "Match score 0–1 (higher is better). Only on search results.",
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "source": {
+            "description": "Provider (e.g. `Pexels`, `Unsplash`, `Pixabay`).",
+            "type": "string"
+          },
+          "source_description": {
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "source_image_url": {
+            "description": "URL of the photo's page on the provider.",
+            "format": "uri",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "uploaded_on": {
+            "description": "Publication date (YYYY-MM-DD).",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "urls": {
+            "description": "Ready-to-use image links in five sizes.",
+            "properties": {
+              "full": {
+                "format": "uri",
+                "type": "string"
+              },
+              "large": {
+                "format": "uri",
+                "type": "string"
+              },
+              "regular": {
+                "format": "uri",
+                "type": "string"
+              },
+              "small": {
+                "format": "uri",
+                "type": "string"
+              },
+              "thumb": {
+                "format": "uri",
+                "type": "string"
+              }
+            },
+            "type": "object"
+          },
+          "width": {
+            "type": [
+              "integer",
+              "null"
+            ]
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "error": {
+      "anyOf": [
+        {
+          "properties": {
+            "code": {
+              "description": "Machine-readable error code (e.g. `MISSING_PARAMS`, `PHOTO_NOT_FOUND`).",
+              "type": "string"
+            },
+            "message": {
+              "description": "Human-readable error message.",
+              "type": "string"
+            },
+            "request_id": {
+              "type": "string"
+            }
+          },
+          "required": [
+            "code",
+            "message"
+          ],
+          "type": "object"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "meta": {
+      "properties": {
+        "request_id": {
+          "description": "Unique id for this request (quote it in support tickets).",
+          "type": "string"
+        },
+        "took_ms": {
+          "description": "Server processing time in milliseconds.",
+          "type": "number"
+        }
+      },
+      "type": "object"
+    },
+    "pagination": {
+      "anyOf": [
+        {
+          "properties": {
+            "has_more": {
+              "description": "Whether another page exists.",
+              "type": "boolean"
+            },
+            "next_cursor": {
+              "description": "Pass back as `cursor` for the next page; `null` when `has_more` is false.",
+              "type": [
+                "string",
+                "null"
+              ]
+            },
+            "per_page": {
+              "description": "Number of items per page.",
+              "type": "integer"
+            }
+          },
+          "type": "object"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "success": {
+      "type": "boolean"
+    }
+  },
+  "type": "object",
+  "x-fastmcp-top-level-schema": "PhotoListResponse"
+}

2 tool updatesv0.4.0Aug 19, 2026
- Addedget_similar_photos
- Removedphoto_similar
3 tool updatesv0.2.0Aug 17, 2026
- First observedphoto_similar
- First observedsearch_photos
- First observedsearch_photos_by_image

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A4.4/5.0

Disambiguation4/5

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.

Naming Consistency4/5

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.

Tool Count4/5

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.

Completeness4/5

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.

Maintenance

ActivityMaintained

ResponsivenessNo issues

Resources

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Tools

search_photos — semantic text search

search_photos_by_image — visual search from an example

get_similar_photos — more like this

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Self-host

Configuration

How it works

Development

License

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Output Schema

Output Schema

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Tool Schema Changelog

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MCP directory API

`search_photos` — semantic text search

`search_photos_by_image` — visual search from an example

`get_similar_photos` — more like this