Dappier MCP Server
OfficialDappier MCP Server
Ermöglichen Sie eine schnelle, kostenlose Echtzeit-Websuche und greifen Sie auf Premiumdaten vertrauenswürdiger Medienmarken zu – Nachrichten, Finanzmärkte, Sport, Unterhaltung, Wetter und mehr. Erstellen Sie leistungsstarke KI-Agenten mit Dappier.
Entdecken Sie eine große Auswahl an Datenmodellen in unserem Marktplatz unter marketplace.dappier.com .
Video-Komplettlösung
Klicken Sie auf das Bild, um die vollständige Videoanleitung zum Einrichten von Dappier MCP mit Claude Desktop anzusehen .
Related MCP server: Deep Research MCP Server
Erste Schritte
Holen Sie sich den Dappier-API-Schlüssel. Gehen Sie zu Dappier , um sich anzumelden und einen API-Schlüssel zu generieren.
Installation über Smithery
So installieren Sie dappier-mcp für Claude Desktop automatisch über Smithery :
npx -y @smithery/cli install @DappierAI/dappier-mcp --client claudeInstallation
Installieren Sie zuerst uv .
MacOS/Linux :
curl -LsSf https://astral.sh/uv/install.sh | shWindows :
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"Verwendung
Claude Desktop
Aktualisieren Sie Ihre Claude-Konfigurationsdatei ( claude_desktop_config.json ) mit dem folgenden Inhalt:
{
"mcpServers": {
"dappier": {
"command": "uvx",
"args": ["dappier-mcp"],
"env": {
"DAPPIER_API_KEY": "YOUR_API_KEY_HERE"
}
}
}
}Hinweis : Möglicherweise müssen Sie den vollständigen Pfad zur ausführbaren
uvxDatei imcommandangeben. Sie erhalten diesen, indem Siewhich uvxunter macOS/Linux oderwhere uvxunter Windows ausführen.
Speicherort der Konfigurationsdatei:
macOS :
~/Library/Application Support/Claude/claude_desktop_config.jsonWindows :
%APPDATA%\Claude\claude_desktop_config.json
Zugriff über die Anwendung:
macOS :
Öffnen Sie die Claude Desktop-Anwendung.
Klicken Sie in der Menüleiste auf
Claude>Settings.Navigieren Sie zur Registerkarte
Developer.Klicken Sie auf
Edit Configum die Konfigurationsdatei in Ihrem Standard-Texteditor zu öffnen.
Windows :
Öffnen Sie die Claude Desktop-Anwendung.
Klicken Sie auf das Zahnradsymbol, um auf
Settingszuzugreifen.Navigieren Sie zur Registerkarte
Developer.Klicken Sie auf
Edit Configum die Konfigurationsdatei in Ihrem Standard-Texteditor zu öffnen.
Hinweis : Wenn die Registerkarte
Developernicht sichtbar ist, stellen Sie sicher, dass Sie die neueste Version von Claude Desktop verwenden.
Cursor
Aktualisieren Sie Ihre Cursor-Konfigurationsdatei ( mcp.json ) mit dem folgenden Inhalt:
{
"mcpServers": {
"dappier": {
"command": "uvx",
"args": ["dappier-mcp"],
"env": {
"DAPPIER_API_KEY": "YOUR_API_KEY_HERE"
}
}
}
}Hinweis : Möglicherweise müssen Sie den vollständigen Pfad zur ausführbaren
uvxDatei imcommandangeben. Sie erhalten diesen, indem Siewhich uvxunter macOS/Linux oderwhere uvxunter Windows ausführen.
Speicherort der Konfigurationsdatei:
Globale Konfiguration :
macOS :
~/.cursor/mcp.jsonWindows :
%USERPROFILE%\.cursor\mcp.json
Projektspezifische Konfiguration :
Platzieren Sie die Datei
mcp.jsonim Verzeichnis.cursorin Ihrem Projektordner:<project-root>/.cursor/mcp.json
Zugriff über die Anwendung:
Öffnen Sie die Cursor-Anwendung.
Navigieren Sie zu
Settings>MCP.Klicken Sie auf
Add New Global MCP Server.Die Anwendung öffnet die Datei
mcp.jsonzur Bearbeitung in Ihrem Standard-Texteditor.
Hinweis : Wenn unter Windows die Konfiguration auf Projektebene nicht erkannt wird, sollten Sie den MCP-Server über die Cursor-Einstellungsschnittstelle hinzufügen.
Windsurf
Aktualisieren Sie Ihre Windsurf-Konfigurationsdatei ( mcp_config.json ) mit dem folgenden Inhalt:
{
"mcpServers": {
"dappier": {
"command": "uvx",
"args": ["dappier-mcp"],
"env": {
"DAPPIER_API_KEY": "YOUR_API_KEY_HERE"
}
}
}
}Hinweis : Möglicherweise müssen Sie den vollständigen Pfad zur ausführbaren
uvxDatei imcommandangeben. Sie erhalten diesen, indem Siewhich uvxunter macOS/Linux oderwhere uvxunter Windows ausführen.
Speicherort der Konfigurationsdatei:
macOS :
~/.codeium/windsurf/mcp_config.jsonWindows :
%USERPROFILE%\.codeium\windsurf\mcp_config.json
Zugriff über die Anwendung:
Öffnen Sie die Windsurf-Anwendung.
Navigieren Sie zu
Settings>Cascade.Scrollen Sie nach unten zum Abschnitt
Model Context Protocol (MCP) Servers.Klicken Sie auf
View raw config, um die Dateimcp_config.jsonin Ihrem Standardtexteditor zu öffnen.
Hinweis : Klicken Sie nach dem Bearbeiten der Konfigurationsdatei im Abschnitt „MCP-Server“ auf die Schaltfläche
Refresh, um die Änderungen anzuwenden.
Merkmale
Der Dappier MCP Remote Server bietet leistungsstarke Echtzeitfunktionen – ganz ohne Schulung oder Feinabstimmung. Nutzen Sie ihn, um interaktive Live-Tools zu erstellen, die auf aktuellen Webdaten, Finanzmärkten oder KI-kuratierten Inhalten basieren.
Echtzeit-Websuche
Modell-ID: am_01j06ytn18ejftedz6dyhz2b15
Durchsuchen Sie das Live-Web mit dem KI-basierten Index von Dappier. Erhalten Sie Echtzeitzugriff auf:
Aktuelle Nachrichten aus aller Welt
Wettervorhersagen und lokale Updates
Reisewarnungen und Fluginformationen
Trendthemen und virale Inhalte
Online-Deals und Shopping-Highlights
Ideal für Anwendungsfälle wie Nachrichtenagenturen, Reiseplaner, Alarm-Bots und mehr.
Einblicke in den Aktienmarkt
Modell-ID: am_01j749h8pbf7ns8r1bq9s2evrh
Dieses Modell bietet sofortigen Zugriff auf Marktdaten, Finanznachrichten und Handelseinblicke. Perfekt für Portfolio-Dashboards, Trading-Copiloten und Investment-Tools.
Es bietet:
Aktienkurse in Echtzeit
Finanznachrichten und Unternehmensupdates
Handelssignale und Trends
Zusammenfassungen der Marktbewegungen
KI-kuratierte Analyse mit Live-Daten von Polygon.io
KI-gestützte Inhaltsempfehlungen
Wählen Sie aus mehreren domänenspezifischen KI-Modellen, die auf die Inhaltserkennung, -zusammenfassung und Feed-Generierung zugeschnitten sind.
Sportnachrichten
Modell-ID: dm_01j0pb465keqmatq9k83dthx34
Bleiben Sie mit Sportschlagzeilen, Spielzusammenfassungen und Expertenanalysen in Echtzeit auf dem Laufenden.
Lifestyle-Updates
Modell-ID: dm_01j0q82s4bfjmsqkhs3ywm3x6y
Entdecken Sie kuratierte Lifestyle-Inhalte – zu den Themen Wellness, Unterhaltung und Inspiration für den Alltag.
iHeartDogs KI
Modell-ID: dm_01j1sz8t3qe6v9g8ad102kvmqn
Ihr intelligenter Assistent für die Hundepflege – Zugriff auf Trainingstipps, Gesundheitsratschläge und Einblicke in das Verhalten.
iHeartCats KI
Modell-ID: dm_01j1sza0h7ekhaecys2p3y0vmj
Eine Experten-KI für alles, was mit Katzen zu tun hat – von der Ernährung über die Spielzeit bis hin zur Fellpflege.
Grünes Monster
Modell-ID: dm_01j5xy9w5sf49bm6b1prm80m27
Entdecken Sie nachhaltige Lifestyle-Ideen, ethische Entscheidungen und grüne Innovationen.
WISH-TV KI
Modell-ID: dm_01jagy9nqaeer9hxx8z1sk1jx6
Greifen Sie auf hyperlokale Nachrichten, Politik, Kultur, Gesundheit und multikulturelle Neuigkeiten zu.
Jede Empfehlung umfasst:
Ein klarer Titel und eine prägnante Zusammenfassung
Das ursprüngliche Veröffentlichungsdatum
Die vertrauenswürdige Quelle und Domäne
Bildvorschau (falls verfügbar)
Ein Relevanz-Score zur Priorisierung
Mit den erweiterten Optionen können Sie:
Optimieren Sie den Suchalgorithmus (
semantic,most_recent,trendingusw.).Konzentrieren Sie die Ergebnisse auf einen bestimmten Bereich (
ref)Passen Sie an, wie viele Ergebnisse Sie möchten (
similarity_top_k,num_articles_ref)
Debuggen
Führen Sie den MCP-Inspector aus, um den Server zu debuggen:
npx @modelcontextprotocol/inspector uvx dappier-mcpBeitragen
Wir freuen uns über Beiträge zur Erweiterung und Verbesserung des Dappier MCP-Servers. Egal, ob Sie neue Suchfunktionen hinzufügen, bestehende Funktionen erweitern oder die Dokumentation verbessern möchten – Ihr Input ist wertvoll.
Beispiele für andere MCP-Server und Implementierungsmuster finden Sie unter: https://github.com/modelcontextprotocol/servers
Pull Requests sind willkommen! Tragen Sie gerne neue Ideen, Fehlerbehebungen oder Verbesserungen bei.
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
Related MCP Connectors
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Structured, cited, real-world data from 300+ sources and the open web for your AI agents.
Give your agent web search and authoritative datasets: S&P Global, FRED, OECD, SimilarWeb & more.
Web data for agents: YouTube transcripts, screenshots, Google News, WHOIS, jobs, tech stack, more.
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