MCP Server Giphy
Servidor MCP Giphy
Servidor MCP para la API de Giphy, que permite que los modelos de IA busquen, recuperen y utilicen GIF de Giphy.
Características
Filtrado de contenido : filtre los resultados por clasificación (G, PG, PG-13, R) para garantizar contenido apropiado
Formato de respuesta optimizado : datos de respuesta optimizados para el consumo del modelo de IA
Múltiples métodos de búsqueda : compatibilidad con recuperación de GIF basada en consultas, aleatoria y de tendencias
Metadatos completos : cada GIF viene con metadatos completos que incluyen dimensiones, formatos y atribución.
Compatibilidad con paginación : controle el tamaño de los resultados y la paginación para un uso eficiente de la API
Herramientas
search_gifsBuscar GIF en Giphy con una cadena de consulta
Entradas:
query(cadena): término o frase de consulta de búsquedalimit(número opcional): número máximo de objetos a devolver (predeterminado: 10, máximo: 50)offset(número opcional): desplazamiento de resultados (predeterminado: 0)rating(cadena opcional): Clasificación del contenido (g, pg, pg-13, r)lang(cadena opcional): código de idioma (predeterminado: en)
Devuelve: Matriz de objetos GIF con metadatos
get_random_gifObtén un GIF aleatorio de Giphy, opcionalmente filtrado por etiqueta
Entradas:
tag(cadena opcional): Etiqueta para limitar resultados aleatoriosrating(cadena opcional): Clasificación del contenido (g, pg, pg-13, r)
Devuelve: objeto GIF aleatorio con metadatos
get_trending_gifsObtén los GIF que son tendencia actualmente en Giphy
Entradas:
limit(número opcional): número máximo de objetos a devolver (predeterminado: 10, máximo: 50)offset(número opcional): desplazamiento de resultados (predeterminado: 0)rating(cadena opcional): Clasificación del contenido (g, pg, pg-13, r)
Devoluciones: Matriz de objetos GIF de tendencia con metadatos
Related MCP server: Giphy MCP Server
Formato de respuesta
Cada GIF en la respuesta incluye:
id: Identificador único de Giphytitle: Título del GIFurl: URL del GIF en el sitio web de Giphyimages: Objeto que contiene varios formatos de imagen, cada uno con:url: URL directa al archivo de imagenwidth: Ancho de la imagenheight: Altura de la imagen
Metadatos adicionales cuando estén disponibles
Configuración
Instalación mediante herrería
Para instalar mcp-server-giphy para Claude Desktop automáticamente a través de Smithery :
npx -y @smithery/cli install mcp-server-giphy --client claudeClave API de Giphy
Crear una clave API de Giphy :
Regístrate para obtener una cuenta de desarrollador de Giphy
Crea una aplicación para obtener una clave API
Elija entre el nivel gratuito o las opciones de pago según sus necesidades.
Configuración del entorno
Crea un archivo .env con tu clave API:
GIPHY_API_KEY=your_api_key_hereUso con Claude Desktop
Para usar esto con Claude Desktop, agregue lo siguiente a su claude_desktop_config.json :
{
"mcpServers": {
"giphy": {
"command": "npx",
"args": ["-y", "mcp-server-giphy"],
"env": {
"GIPHY_API_KEY": "<YOUR_API_KEY>"
}
}
}
}Desarrollo
# Install dependencies
npm install
# Build the project
npm run build
# Start the server
npm start
# Run in development mode with hot reloading
npm run dev
# Run tests
npm test
# Use with MCP Inspector
npm run inspectorLicencia
Este servidor MCP cuenta con la licencia MIT. Esto significa que puede usar, modificar y distribuir el software libremente, sujeto a los términos y condiciones de la licencia MIT. Para más detalles, consulte el archivo de LICENCIA en el repositorio del proyecto.
Available Tools
3 toolsget_random_gifA
Get a random GIF from Giphy, optionally filtered by tag
| Name | Required | Description | Default |
|---|---|---|---|
| tag | No | Tag to limit random results (optional) | |
| rating | No | Content rating (g, pg, pg-13, r) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the tool fetches from Giphy but doesn't disclose behavioral traits like rate limits, authentication needs, response format, or error handling. For an external API tool with zero annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Get a random GIF from Giphy') and adds optional detail ('optionally filtered by tag') without waste. Every word earns its place, making it appropriately sized and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (external API call with parameters) and no annotations or output schema, the description is minimally adequate. It covers the basic purpose but lacks details on behavior, response format, or error handling, leaving gaps that could hinder effective use by an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 both parameters ('tag' and 'rating') with descriptions and enum values. The description adds minimal value by mentioning optional tag filtering, but doesn't provide additional syntax or context beyond what the schema provides, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get a random GIF') and resource ('from Giphy'), with optional filtering by tag. It distinguishes from siblings by specifying 'random' (vs. 'trending' or 'search'), making the purpose explicit and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for random GIF retrieval, but provides no explicit guidance on when to use this tool versus alternatives like 'get_trending_gifs' or 'search_gifs'. It mentions optional tag filtering, which hints at context, but lacks clear when/when-not instructions or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_trending_gifsC
Get currently trending GIFs on Giphy
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of objects to return (default: 10, max: 50) | |
| offset | No | Results offset (default: 0) | |
| rating | No | Content rating (g, pg, pg-13, r) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't add any behavioral context beyond that—such as rate limits, authentication requirements, or what the output looks like (e.g., format, pagination details). This is a significant gap for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain behavioral aspects like rate limits or output format, which are crucial for proper tool invocation. For a tool with three parameters and no structured output information, more context is needed to be fully helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear documentation for all three parameters (limit, offset, rating). The description doesn't add any parameter semantics beyond what the schema provides, so it meets the baseline score of 3 where the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('currently trending GIFs on Giphy'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_random_gif' or 'search_gifs', which would require mentioning it's specifically for trending content rather than random or search-based retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'get_random_gif' or 'search_gifs'. It lacks any context about scenarios where trending GIFs are preferred over random or searched ones, leaving the agent to infer usage based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_gifsC
Search for GIFs on Giphy with a query string
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query term or phrase | |
| limit | No | Maximum number of objects to return (default: 10, max: 50) | |
| offset | No | Results offset (default: 0) | |
| rating | No | Content rating (g, pg, pg-13, r) | |
| lang | No | Language code (default: en) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the action ('Search for GIFs') but fails to disclose critical traits like rate limits, authentication needs, error handling, or response format. This leaves significant gaps for an agent to understand operational behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a search tool with 5 parameters and no output schema or annotations, the description is incomplete. It lacks details on behavioral aspects, usage context, and output expectations, leaving the agent with insufficient information for effective tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional meaning beyond what the schema provides, such as usage examples or constraints not in the schema. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Search for GIFs') and resource ('on Giphy'), with the specific mechanism ('with a query string'). It distinguishes from siblings like 'get_random_gif' and 'get_trending_gifs' by specifying search functionality, though it could be more explicit about the distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'get_random_gif' or 'get_trending_gifs'. It lacks context such as use cases for search versus random/trending GIFs, making it unclear when this is the appropriate choice.
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.
3 tool updates
- First observed
get_random_gif - First observed
get_trending_gifs - First observed
search_gifs
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose: get_random_gif retrieves a single random GIF, get_trending_gifs fetches trending content, and search_gifs performs query-based searches. There is no overlap in functionality, making tool selection straightforward for an agent.
All tools follow a consistent verb_noun pattern with 'get' or 'search' verbs and descriptive nouns (random_gif, trending_gifs, gifs). The naming is uniform and predictable across the set.
With 3 tools, the server is well-scoped for its purpose of accessing Giphy content. Each tool serves a distinct and essential function (random, trending, search), and there are no extraneous or missing tools for this domain.
The tool surface covers the core workflows for a Giphy API: retrieving random GIFs, accessing trending content, and searching by query. This provides complete coverage for typical use cases without obvious gaps or dead ends.
Maintenance
Related MCP Connectors
Give AI assistants access to real-time data. Search the web, compare flights, find hotels, and more.
Discover, inspect and run 63,000+ agent tools from one balance. Pay per call, no subscriptions.
Discover, compare, route, and execute machine-accessible capabilities for AI agents.
Travel tools for AI agents: plan and edit real trips, search stays and tours, import travel videos.
Related MCP Servers
- FlicenseNot gradedqualityDmaintenanceThis is an auto-generated Multi-Agent Conversation Protocol server that enables interaction with the Giphy API, allowing users to access and use Giphy's GIF services through natural language commands.-
- FlicenseNot gradedqualityDmaintenanceAn MCP (Multi-Agent Conversation Protocol) Server that enables interaction with the Giphy API, allowing agents to search, retrieve, and manage GIF animations through natural language commands.-
- -licenseNot gradedqualityNot gradedmaintenanceAn MCP (Multi-Agent Conversation Protocol) Server that enables interaction with the Giphy API, allowing users to search, retrieve, and manipulate GIF content through natural language commands.-
- FlicenseNot gradedqualityDmaintenanceAn auto-generated Model Context Protocol Server that enables interaction with Giphy's API, allowing users to access and manipulate GIF content through natural language requests.-