Dappier MCP Server
OfficialServidor MCP de Dappier
Permite búsquedas web rápidas, gratuitas y en tiempo real, y accede a datos premium de medios de comunicación de confianza: noticias, mercados financieros, deportes, entretenimiento, el tiempo y más. Crea potentes agentes de IA con Dappier.
Explore una amplia gama de modelos de datos en nuestro mercado en marketplace.dappier.com .
Tutorial en vídeo
Haga clic en la imagen para ver la guía de video completa sobre cómo configurar Dappier MCP con Claude Desktop.
Related MCP server: Deep Research MCP Server
Empezando
Obtén la clave API de Dappier. Visita Dappier para registrarte y generar una clave API.
Instalación mediante herrería
Para instalar dappier-mcp para Claude Desktop automáticamente a través de Smithery :
npx -y @smithery/cli install @DappierAI/dappier-mcp --client claudeInstalación
Primero instale uv .
MacOS/Linux :
curl -LsSf https://astral.sh/uv/install.sh | shVentanas :
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"Uso
Escritorio de Claude
Actualice su archivo de configuración de Claude ( claude_desktop_config.json ) con el siguiente contenido:
{
"mcpServers": {
"dappier": {
"command": "uvx",
"args": ["dappier-mcp"],
"env": {
"DAPPIER_API_KEY": "YOUR_API_KEY_HERE"
}
}
}
}Consejo : Es posible que deba proporcionar la ruta completa del ejecutable
uvxen el campocommand. Puede obtenerla ejecutandowhich uvxen macOS/Linux owhere uvxen Windows.
Ubicación del archivo de configuración:
macOS :
~/Library/Application Support/Claude/claude_desktop_config.jsonVentanas :
%APPDATA%\Claude\claude_desktop_config.json
Accediendo mediante aplicación:
macOS :
Abra la aplicación Claude Desktop.
En la barra de menú, haga clic en
Claude>Settings.Vaya a la pestaña
Developer.Haga clic en
Edit Configpara abrir el archivo de configuración en su editor de texto predeterminado.
Ventanas :
Abra la aplicación Claude Desktop.
Haga clic en el ícono del engranaje para acceder a
Settings.Vaya a la pestaña
Developer.Haga clic en
Edit Configpara abrir el archivo de configuración en su editor de texto predeterminado.
Nota : Si la pestaña
Developerno está visible, asegúrese de estar utilizando la última versión de Claude Desktop.
Cursor
Actualice su archivo de configuración del cursor ( mcp.json ) con el siguiente contenido:
{
"mcpServers": {
"dappier": {
"command": "uvx",
"args": ["dappier-mcp"],
"env": {
"DAPPIER_API_KEY": "YOUR_API_KEY_HERE"
}
}
}
}Consejo : Es posible que deba proporcionar la ruta completa del ejecutable
uvxen el campocommand. Puede obtenerla ejecutandowhich uvxen macOS/Linux owhere uvxen Windows.
Ubicación del archivo de configuración:
Configuración global :
macOS :
~/.cursor/mcp.jsonWindows :
%USERPROFILE%\.cursor\mcp.json
Configuración específica del proyecto :
Coloque el archivo
mcp.jsondentro del directorio.cursordentro de la carpeta de su proyecto:<project-root>/.cursor/mcp.json
Accediendo mediante aplicación:
Abra la aplicación Cursor.
Vaya a
Settings>MCP.Haga clic en
Add New Global MCP Server.La aplicación abrirá el archivo
mcp.jsonen su editor de texto predeterminado para editarlo.
Nota : En Windows, si no se reconoce la configuración a nivel de proyecto, considere agregar el servidor MCP a través de la interfaz de configuración del cursor.
Windsurf
Actualice su archivo de configuración de Windsurf ( mcp_config.json ) con el siguiente contenido:
{
"mcpServers": {
"dappier": {
"command": "uvx",
"args": ["dappier-mcp"],
"env": {
"DAPPIER_API_KEY": "YOUR_API_KEY_HERE"
}
}
}
}Consejo : Es posible que deba proporcionar la ruta completa del ejecutable
uvxen el campocommand. Puede obtenerla ejecutandowhich uvxen macOS/Linux owhere uvxen Windows.
Ubicación del archivo de configuración:
macOS :
~/.codeium/windsurf/mcp_config.jsonWindows :
%USERPROFILE%\.codeium\windsurf\mcp_config.json
Accediendo mediante aplicación:
Abra la aplicación Windsurf.
Vaya a
Settings>Cascade.Desplácese hacia abajo hasta la sección
Model Context Protocol (MCP) Servers.Haga clic en
View raw configpara abrir el archivomcp_config.jsonen su editor de texto predeterminado.
Nota : Después de editar el archivo de configuración, haga clic en el botón
Refreshen la sección Servidores MCP para aplicar los cambios.
Características
El servidor remoto Dappier MCP ofrece potentes funciones en tiempo real listas para usar, sin necesidad de capacitación ni ajustes. Úselo para crear herramientas interactivas en tiempo real basadas en los datos web más recientes, los mercados financieros o contenido optimizado con IA.
Búsqueda web en tiempo real
Identificación del modelo: am_01j06ytn18ejftedz6dyhz2b15
Busque en la web en tiempo real con el índice de Dappier basado en IA. Obtenga acceso en tiempo real a:
Noticias de última hora de todo el mundo
Pronósticos meteorológicos y actualizaciones locales
Alertas de viaje e información de vuelos
Temas de tendencia y contenido viral
Ofertas en línea y artículos destacados de compras
Ideal para casos de uso como agentes de noticias, planificadores de viajes, bots de alerta y más.
Perspectivas del mercado de valores
Identificación del modelo: am_01j749h8pbf7ns8r1bq9s2evrh
Este modelo ofrece acceso instantáneo a datos de mercado, noticias financieras y análisis de operaciones. Ideal para paneles de control de cartera, copilotos de trading y herramientas de inversión.
Proporciona:
Precios de las acciones en tiempo real
Noticias financieras y actualizaciones de la empresa
Señales y tendencias comerciales
Resúmenes de movimientos del mercado
Análisis controlado por IA con datos en vivo de Polygon.io
Recomendaciones de contenido impulsadas por IA
Elija entre varios modelos de IA específicos del dominio diseñados para el descubrimiento de contenido, el resumen y la generación de feeds.
Noticias deportivas
Identificación del modelo: dm_01j0pb465keqmatq9k83dthx34
Manténgase actualizado con titulares deportivos en tiempo real, resúmenes de juegos y análisis de expertos.
Actualizaciones de estilo de vida
Identificación del modelo: dm_01j0q82s4bfjmsqkhs3ywm3x6y
Explore contenido de estilo de vida seleccionado que abarca bienestar, entretenimiento e inspiración diaria.
IA de iHeartDogs
Identificación del modelo: dm_01j1sz8t3qe6v9g8ad102kvmqn
Su asistente inteligente para el cuidado de perros: acceda a consejos de entrenamiento, asesoramiento de salud y conocimientos sobre el comportamiento.
IA de iHeartCats
Identificación del modelo: dm_01j1sza0h7ekhaecys2p3y0vmj
Una IA experta para todo lo relacionado con los felinos: desde la nutrición hasta el tiempo de juego y las rutinas de aseo.
Monstruo verde
Identificación del modelo: dm_01j5xy9w5sf49bm6b1prm80m27
Descubra ideas de estilo de vida sostenible, opciones éticas e innovaciones ecológicas.
WISH-TV IA
ID del modelo: dm_01jagy9nqaeer9hxx8z1sk1jx6
Acceda a noticias hiperlocales, política, cultura, salud y actualizaciones multiculturales.
Cada recomendación incluye:
Un título claro y un resumen conciso
La fecha de publicación original
La fuente y el dominio confiables
Vista previa de la imagen (si está disponible)
Una puntuación de relevancia para la priorización
Las opciones avanzadas le permiten:
Ajuste el algoritmo de búsqueda (
semantic,most_recent,trending, etc.)Centrar los resultados en un dominio específico (
ref)Ajuste la cantidad de resultados que desea (
similarity_top_k,num_articles_ref)
Depuración
Ejecute el inspector MCP para depurar el servidor:
npx @modelcontextprotocol/inspector uvx dappier-mcpContribuyendo
Agradecemos sus contribuciones para ampliar y mejorar el servidor MCP de Dappier. Ya sea que desee añadir nuevas funciones de búsqueda, mejorar la funcionalidad existente o mejorar la documentación, su opinión es muy valiosa.
Para ver ejemplos de otros servidores MCP y patrones de implementación, consulte: https://github.com/modelcontextprotocol/servers
¡Aceptamos solicitudes de incorporación de cambios! Siéntete libre de contribuir con nuevas ideas, correcciones de errores o mejoras.
Available Tools
2 toolsdappier_ai_recommendationsB
Fetch AI-powered recommendations from Dappier by processing the provided query with a selected data model that tailors results to specific interests.
- **Sports News (dm_01j0pb465keqmatq9k83dthx34):**
Get real-time news, updates, and personalized content from top sports sources.
- **Lifestyle News (dm_01j0q82s4bfjmsqkhs3ywm3x6y):**
Access current lifestyle updates, analysis, and insights from leading lifestyle publications.
- **iHeartDogs AI (dm_01j1sz8t3qe6v9g8ad102kvmqn):**
Tap into a dog care expert with access to thousands of articles covering pet health, behavior, grooming, and ownership.
- **iHeartCats AI (dm_01j1sza0h7ekhaecys2p3y0vmj):**
Utilize a cat care specialist that provides comprehensive content on cat health, behavior, and lifestyle.
- **GreenMonster (dm_01j5xy9w5sf49bm6b1prm80m27):**
Receive guidance for making conscious and compassionate choices benefiting people, animals, and the planet.
- **WISH-TV AI (dm_01jagy9nqaeer9hxx8z1sk1jx6):**
Get recommendations covering sports, breaking news, politics, multicultural updates, and more.
Based on the chosen `data_model_id`, the tool processes the input query and returns a formatted summary including article titles, summaries, images, source URLs, publication dates, and relevance scores.
| Name | Required | Description | Default |
|---|---|---|---|
| ref | No | The site domain where recommendations should be prioritized. | |
| query | Yes | The input string for AI-powered content recommendations. | |
| data_model_id | Yes | The data model ID to use for recommendations. Available Data Models: - dm_01j0pb465keqmatq9k83dthx34: (Sports News) Real-time news, updates, and personalized content from top sports sources like Sportsnaut, Forever Blueshirts, Minnesota Sports Fan, LAFB Network, Bounding Into Sports and Ringside Intel. - dm_01j0q82s4bfjmsqkhs3ywm3x6y: (Lifestyle News) Real-time updates, analysis, and personalized content from top sources like The Mix, Snipdaily, Nerdable and Familyproof. - dm_01j1sz8t3qe6v9g8ad102kvmqn: (iHeartDogs AI) A dog care expert with access to thousands of articles on health, behavior, lifestyle, grooming, ownership, and more from the industry-leading pet community iHeartDogs.com. - dm_01j1sza0h7ekhaecys2p3y0vmj: (iHeartCats AI) A cat care expert with access to thousands of articles on health, behavior, lifestyle, grooming, ownership, and more from the industry-leading pet community iHeartCats.com. - dm_01j5xy9w5sf49bm6b1prm80m27: (GreenMonster) A helpful guide to making conscious and compassionate choices that benefit people, animals, and the planet. - dm_01jagy9nqaeer9hxx8z1sk1jx6: (WISH-TV AI) Covers sports, politics, breaking news, multicultural news, Hispanic language content, entertainment, health, and education. | |
| num_articles_ref | No | Minimum number of articles to return from the reference domain. | |
| search_algorithm | No | The search algorithm to use for retrieving articles. | most_recent |
| similarity_top_k | No | Number of top similar articles to retrieve based on semantic similarity. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It describes the output as a 'formatted summary including article titles, summaries, images, source URLs, publication dates, and relevance scores,' which is helpful. However, it does not mention authorization requirements, rate limits, or side effects, which would be expected for a tool with no annotations.
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 starts with a clear purpose and organizes data models with bullet points, but it is somewhat verbose by repeating model information that is also in the schema. It is front-loaded but could be more concise, as every sentence earns its place but some repetition exists.
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 six parameters, no output schema, and 100% schema coverage, the description is nearly complete. It explains the output format and each data model's domain, covering the core functionality. It could be more complete by briefly explaining how ref, num_articles_ref, search_algorithm, and similarity_top_k affect results, but the schema handles those sufficiently.
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 baseline is 3. The description adds value by naming and explaining data model IDs (e.g., 'Sports News: Real-time news...'), but these details are already present in the schema's description for data_model_id. Other parameters like ref, query, and search_algorithm are not elaborated beyond the schema, so the description provides minimal additional meaning.
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 'Fetch AI-powered recommendations from Dappier by processing the provided query with a selected data model,' which identifies the verb (fetch recommendations) and resource (Dappier data models). It distinguishes the tool from the sibling dappier_real_time_search by emphasizing recommendations with tailored content, though it doesn't explicitly contrast them.
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 by listing data models and their domains (e.g., Sports News, Lifestyle News), but it does not explicitly state when to use this tool over the sibling or provide conditions for use. It lacks exclusions or alternative suggestions, leaving the agent to infer context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
dappier_real_time_searchB
Retrieve real-time search data from Dappier by processing an AI model that supports two key capabilities:
- Real-Time Web Search:
Access the latest news, weather, travel information, deals, and more using model `am_01j06ytn18ejftedz6dyhz2b15`.
- Stock Market Data:
Retrieve real-time financial news, stock prices, and trade updates using model `am_01j749h8pbf7ns8r1bq9s2evrh`.
Based on the provided `ai_model_id`, the tool selects the appropriate model and returns search results.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The search query to retrieve real-time information. | |
| ai_model_id | Yes | The AI model ID to use for the query. Available AI Models: - am_01j06ytn18ejftedz6dyhz2b15: (Real Time Data) Access real-time Google web search results, including the latest news, weather, travel, deals, and more. - am_01j749h8pbf7ns8r1bq9s2evrh: (Stock Market Data) Access real-time financial news, stock prices, and trades from Polygon.io, with AI-powered insights and up-to-the-minute updates. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behaviors. It mentions that an AI model is selected based on ai_model_id and returns results, but it does not address side effects, permissions, rate limits, error handling, or return structure. This is insufficient for full 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 concise, well-structured into two paragraphs, and front-loads the purpose. Every sentence adds value with no redundancy.
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 no output schema, no annotations, and two distinct models, the description lacks details on return format, error scenarios, and guidance on model selection. It is incomplete for effective agent use.
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 coverage is 100% with detailed descriptions for both parameters. The description adds no meaningful new information beyond what is already in the schema, so it meets the baseline without enhancement.
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 'Retrieve real-time search data from Dappier' and distinguishes between two specific capabilities (web search and stock market data) using model IDs. The tool name and description differentiate it from the sibling tool 'dappier_ai_recommendations' by focusing on real-time data 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 lists two use cases but does not explicitly guide when to use this tool versus the sibling tool. It implies usage context through capabilities but lacks direct instructions or exclusions.
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.
2 tool updates
v1.0.0- First observed
dappier_ai_recommendations - First observed
dappier_real_time_search
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: one fetches AI recommendations with various data models, the other performs real-time search. No overlap in functionality.
Both tools share the 'dappier_' prefix and use descriptive noun phrases ('ai_recommendations', 'real_time_search'), though the first uses underscores between words and the second uses a more compound adjective form, leading to minor inconsistency.
With only 2 tools, the server is very focused. For a purpose-built API covering recommendations and search, this number is reasonable though minimal. Slightly under the typical 3-15 range but not drastically.
The server covers its stated domains (recommendations and real-time search) adequately, but lacks tools for managing data models or retrieving raw content. It's sufficient for basic use but leaves out potential CRUD operations.
Maintenance
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