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Pexafy MCP Server

Name: Pexafy MCP Server
Author: Pexafy

Official

by Pexafy

Overview Schema Related Servers Score Discussions

Python

Hybrid

pexafy-mcp

License: MIT

Búsqueda de fotos de stock para asistentes de IA. Un servidor MCP que permite a Claude, ChatGPT o cualquier cliente MCP buscar en una biblioteca de imágenes libres de derechos — describiendo una escena en lenguaje natural, desde una imagen de ejemplo o con «más como esta» — y mostrar los resultados como una cuadrícula de miniaturas dentro de la conversación.

MCP remoto, OAuth, sin clave de API que pegar, 3 herramientas, imágenes renderizadas en línea.

La cuadrícula de resultados de Pexafy, renderizada en línea en una conversación de Claude

Úsalo (nada que instalar)

Un servidor alojado está disponible en:

https://mcp.pexafy.com/mcp

Usa Streamable HTTP y se autentica con OAuth 2.1: inicias sesión en Pexafy en una ventana del navegador y el conector recibe sus propias credenciales. No hay ninguna clave de API que generar, pegar en un archivo JSON o rotar más adelante.

Claude (web y escritorio)

Abre Configuración → Conectores (en Team/Enterprise, un propietario lo añade una vez desde Configuración de la organización → Conectores).
Haz clic en Añadir conector personalizado.
Pega https://mcp.pexafy.com/mcp y confirma.
Inicia sesión en Pexafy en la ventana que se abre. Listo: pídele a Claude una foto.

Claude Code

claude mcp add --transport http pexafy https://mcp.pexafy.com/mcp

Cualquier otro cliente MCP

Apunta a la misma URL con el transporte streamable-http. Los clientes que no implementan OAuth pueden autenticarse en su lugar con una clave de API de Pexafy enviada como Authorization: Bearer <key> o x-api-key: <key> — consíguela en el panel de control.

Disponibilidad: GET /health (público, sin autenticación).

También aparece en el registro oficial de MCP como com.pexafy/pexafy-mcp, y en Smithery — donde hay una URL de pasarela alojada disponible para los clientes que la prefieran.

Cuánto cuesta

El plan gratuito cubre 5 000 búsquedas al mes con un conector — suficiente para un uso regular, sin necesidad de tarjeta. Los niveles superiores están en la página de precios. Cuando alcanzas un límite, es el asistente quien te lo indica en el chat, en lugar de fallar con un error opaco.

Related MCP server: brave-image-mcp

Herramientas

Tres herramientas de solo lectura. Sin alcance de escritura ni modificación de la cuenta.

`search_photos` — búsqueda semántica por texto

Descríbe la escena con una frase completa; Pexafy es semántico, por lo que las frases funcionan mejor que las palabras clave. Todos los parámetros son opcionales, pero hay que pasar q o al menos un filtro.

Parámetro	Tipo	Notas
`q`	string	La escena, en lenguaje natural. Máximo 500 caracteres.
`color_name`	string	Uno de: red, orange, yellow, green, blue, purple, pink, brown, black, white, gray, teal, beige, gold, navy. Excluye `color_hex`.
`color_hex`	string	Por ejemplo, `#1E90FF`. Excluye `color_name`.
`color_tolerance`	integer	De 0 (exacto) a 255 (flexible). Por defecto 20. Solo con `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	Nombre de usuario exacto.
`after_date`	string	`YYYY-MM-DD`. Publicado en esa fecha o después.
`cursor`	string	Contenido de `pagination.next_cursor` de una respuesta anterior.

`search_photos_by_image` — búsqueda visual a partir de un ejemplo

Encuentra fotos que se parecen a una imagen de referencia, con la opción de ajustarlas con palabras («así, pero de noche»).

Parámetro	Tipo	Notas
`image_url`	string	URL http(s) pública de la imagen de referencia.
`image_file`	object	Se rellena automáticamente en clientes que admiten cargas (p. ej., ChatGPT).
`image_base64`	string	Bytes Base64 sin procesar, para clientes programáticos.
`q`	string	Texto para combinar con la imagen («pero con las manos levantadas»).
`text_alpha`	number	Peso de `q` frente a la imagen.
`orientation`, `source`, `color_name`, `license_type`, `photographer`, `after_date`	string	Los mismos filtros que arriba.
`cursor`	string	Token de paginación.

Se requiere uno de image_url, image_file o image_base64. Las imágenes se descargan en el servidor; máximo 20 MB.

`get_similar_photos` — más álbumes de este

Parámetro	Tipo	Notas
`photo_id`	string	Obligatorio. El UUID de una foto, extraído de un resultado anterior.
`cursor`	string	Token de paginación.

Qué devuelve

Cada foto incluye su id, URLs en varios tamaños, dimensiones, color dominante, orientación, fuente, licencia, fotógrafo y una cadena attribution para mostrar como crédito — suficiente para que la IA razone sobre los resultados en lugar de simplemente enumerarlos.

Los resultados se numeran #1, #2, …, así que puedes referirte a una foto con la misma naturalidad con la que conversas. Sin ids que copiar:

Pidiendo más fotos como la #1, con la IA razonando sobre el nuevo conjunto

En clientes que soportan MCP Apps, al hacer clic en una miniatura se abre un panel de detalle con los metadatos completos — sin una llamada adicional; ya aparecen todos en el resultado de la herramienta:

El panel de detalle: fotógrafo, fuente, resolución, licencia, color dominante, orientación y descripción

Alojamiento propio

No hace falta alojarlo tú — ya está el alojado de arriba como vía de entrada prevista. Pero el servidor es un cliente sencillo y desnudo de la API de Pexafy, de modo que puedes ejecutar el tuyo con tu propia clave.

Requiere Python 3.12 o superior.

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

Con el script de consola instalado (pip install .):

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

Claude Desktop / Claude Code, sobre stdio:

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

Docker, sobre HTTP — consulta docker-compose.example.yml:

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

La imagen por sí misma usa stdio por defecto, el transporte que usa un cliente MCP para manejar un contenedor, de modo que también funciona directamente:

docker run -i --rm pexafy-mcp

Eso responde a initialize y tools/list sin clave de API y sin red: las herramientas provienen de la instantánea OpenAPI incluida. Solo se necesita una clave para ejecutar una búsqueda. Servirlo por HTTP es cuestión de cambiar el transporte, algo que hacen ambos archivos compose.

Configuración

Todos los ajustes son variables de entorno y todas son opcionales: con ninguna establecida, pexafy-mcp arranca. Hay dos que conviene conocer.

Variable	Default	Propósito
`PEXAFY_MCP_TRANSPORT`	`stdio`	`stdio` para un cliente local, `http` para servir de forma remota
`PEXAFY_API_BASE_URL`	`http://localhost:8000`	Raíz de la API de Pexafy — apúntala a `https://api.pexafy.com` o a tu propio despliegue

El resto pertenece a un despliegue más que a quien ejecuta el contenedor, y vive en .env.example: una clave PEXAFY_API_KEY de respaldo para el uso de stdio cuando el cliente no envía ninguna propia, PEXAFY_THUMB_BASE_URL y PEXAFY_THUMB_HMAC_SECRET para firmar las miniaturas de la cuadrícula en línea, y las variables PEXAFY_OAUTH_* con MCP_RESOLVE_SECRET para ejecutar el transporte HTTP como servidor de recursos OAuth. Ninguna de ellas es necesaria para arrancar el servidor.

Cómo funciona

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)

Las herramientas se generan a partir de la especificación OpenAPI de Pexafy mediante FastMCP.from_openapi(), por lo que la API sigue siendo la única fuente de verdad; a continuación, tooling.py las reforma para el LLM, reduciendo la superficie al núcleo de la búsqueda, eliminando parámetros que despistan a la IA e incluyendo los conjuntos cerrados de valores para que no sea necesario hacer clic en ninguna faceta.
build_server() lo monta todo. Importar el paquete no tiene efectos secundarios y no realiza I/O de red: lee los archivos integrados assets/openapi.json y assets/facets.json. prepare.sh los regenera.
search_photos_by_image está escrita a mano: un asistente de chat no puede subir un archivo binario a una herramienta MCP, por lo que la herramienta toma una URL de imagen y la descarga en el servidor.
La cuadrícula en línea es un recurso de interfaz de MCP Apps. El cliente ext-apps viene empaquetado e incrustado, porque la siembre aislada del host no puede obtener scripts externos en tiempo de ejecución.

Desarrollo

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

¡Las contribuciones se agradecen! Consulta CONTRIBUTING.md.

Licencia

MIT — consulta LICENSE.

The package also redistribute assets of third parties (the Inter typeface, the package @modelcontextprotocol/ext-apps for browsers and the libraries included in it), each under its own license — consult 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

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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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Herramientas

search_photos — búsqueda semántica por texto

search_photos_by_image — búsqueda visual a partir de un ejemplo

get_similar_photos — más álbumes de este

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MCP directory API

`search_photos` — búsqueda semántica por texto

`search_photos_by_image` — búsqueda visual a partir de un ejemplo

`get_similar_photos` — más álbumes de este