The Dappier MCP Server provides access to real-time web search, stock market data, and AI-powered content recommendations across various domains.
Real-Time Web Search: Access breaking news, weather, travel alerts, deals, and trending topics using the am_01j06ytn18ejftedz6dyhz2b15 model.
Stock Market Insights: Retrieve real-time financial news, stock prices, and trade signals using the am_01j749h8pbf7ns8r1bq9s2evrh model.
AI-Powered Content Recommendations: Get domain-specific recommendations for sports, lifestyle, pet care, sustainability, and local news using specialized models.
Advanced Search Customization: Tune search algorithms, focus on specific domains, and adjust results to prioritize relevance.
Enables real-time Google web search results, providing access to the latest news, weather, stock prices, travel information, and deals.
Offers the ability to retrieve the latest news about Meta through the real-time search functionality.
Provides real-time financial data from Polygon.io, including stock prices, trades, and financial news with AI-powered insights.
Integrates with WishTV to provide AI-powered content recommendations and articles.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Dappier MCP Server search for latest Tesla stock news and price updates"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
📌 Click the image below — use Ctrl+Click (or Cmd+Click on Mac) to open in a new tab.
Enable fast, free real-time web search and access premium data from trusted media brands—news, financial markets, sports, entertainment, weather, and more. Build powerful AI agents with Dappier.
Explore a wide range of data models in our marketplace at marketplace.dappier.com.
Related MCP server: Dappier MCP Server
Get Dappier API Key. Head to Dappier to sign up and generate an API key.
To install dappier-mcp for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @DappierAI/dappier-mcp --client claudeInstall uv first.
MacOS/Linux:
curl -LsSf https://astral.sh/uv/install.sh | shWindows:
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"Update your Claude configuration file (claude_desktop_config.json) with the following content:
{
"mcpServers": {
"dappier": {
"command": "uvx",
"args": ["dappier-mcp"],
"env": {
"DAPPIER_API_KEY": "YOUR_API_KEY_HERE"
}
}
}
}Hint: You may need to provide the full path to the
uvxexecutable in thecommandfield. You can obtain this by runningwhich uvxon macOS/Linux orwhere uvxon Windows.
Configuration file location:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Accessing via application:
macOS:
Open the Claude Desktop application.
In the menu bar, click on Claude > Settings.
Navigate to the Developer tab.
Click on Edit Config to open the configuration file in your default text editor.
Windows:
Open the Claude Desktop application.
Click on the gear icon to access Settings.
Navigate to the Developer tab.
Click on Edit Config to open the configuration file in your default text editor.
Note: If the
Developertab is not visible, ensure you're using the latest version of Claude Desktop.
Update your Cursor configuration file (mcp.json) with the following content:
{
"mcpServers": {
"dappier": {
"command": "uvx",
"args": ["dappier-mcp"],
"env": {
"DAPPIER_API_KEY": "YOUR_API_KEY_HERE"
}
}
}
}Hint: You may need to provide the full path to the
uvxexecutable in thecommandfield. You can obtain this by runningwhich uvxon macOS/Linux orwhere uvxon Windows.
Configuration file location:
Global Configuration:
macOS: ~/.cursor/mcp.json
Windows: %USERPROFILE%\.cursor\mcp.json
Project-Specific Configuration:
Place the mcp.json file inside the .cursor directory within your project folder: <project-root>/.cursor/mcp.json
Accessing via application:
Open the Cursor application.
Navigate to Settings > MCP.
Click on Add New Global MCP Server.
The application will open the mcp.json file in your default text editor for editing.
Note: On Windows, if the project-level configuration is not recognized, consider adding the MCP server through the Cursor settings interface.
Update your Windsurf configuration file (mcp_config.json) with the following content:
{
"mcpServers": {
"dappier": {
"command": "uvx",
"args": ["dappier-mcp"],
"env": {
"DAPPIER_API_KEY": "YOUR_API_KEY_HERE"
}
}
}
}Hint: You may need to provide the full path to the
uvxexecutable in thecommandfield. You can obtain this by runningwhich uvxon macOS/Linux orwhere uvxon Windows.
Configuration file location:
macOS: ~/.codeium/windsurf/mcp_config.json
Windows: %USERPROFILE%\.codeium\windsurf\mcp_config.json
Accessing via application:
Open the Windsurf application.
Navigate to Settings > Cascade.
Scroll down to the Model Context Protocol (MCP) Servers section.
Click on View raw config to open the mcp_config.json file in your default text editor.
Note: After editing the configuration file, click the
Refreshbutton in the MCP Servers section to apply the changes.
The Dappier MCP Remote Server provides powerful real-time capabilities out of the box — no training or fine-tuning needed. Use it to build live, interactive tools powered by the latest web data, financial markets, or AI-curated content.
Model ID: am_01j06ytn18ejftedz6dyhz2b15
Search the live web using Dappier’s AI-powered index. Get real-time access to:
Breaking news from across the globe
Weather forecasts and local updates
Travel alerts and flight info
Trending topics and viral content
Online deals and shopping highlights
Ideal for use cases like news agents, travel planners, alert bots, and more.
Model ID: am_01j749h8pbf7ns8r1bq9s2evrh
This model delivers instant access to market data, financial headlines, and trade insights. Perfect for portfolio dashboards, trading copilots, and investment tools.
It provides:
Real-time stock prices
Financial news and company updates
Trade signals and trends
Market movement summaries
AI-curated analysis using live data from Polygon.io
Choose from several domain-specific AI models tailored for content discovery, summarization, and feed generation.
Model ID: dm_01j0pb465keqmatq9k83dthx34
Stay updated with real-time sports headlines, game recaps, and expert analysis.
Model ID: dm_01j0q82s4bfjmsqkhs3ywm3x6y
Explore curated lifestyle content — covering wellness, entertainment, and everyday inspiration.
Model ID: dm_01j1sz8t3qe6v9g8ad102kvmqn
Your intelligent dog care assistant — access training tips, health advice, and behavior insights.
Model ID: dm_01j1sza0h7ekhaecys2p3y0vmj
An expert AI for all things feline — from nutrition to playtime to grooming routines.
Model ID: dm_01j5xy9w5sf49bm6b1prm80m27
Discover sustainable lifestyle ideas, ethical choices, and green innovations.
Model ID: dm_01jagy9nqaeer9hxx8z1sk1jx6
Tap into hyperlocal news, politics, culture, health, and multicultural updates.
Each recommendation includes:
A clear title and concise summary
The original publication date
The trusted source and domain
Image preview (if available)
A relevance score for prioritization
Advanced options let you:
Tune the search algorithm (semantic, most_recent, trending, etc.)
Focus results on a specific domain (ref)
Adjust how many results you want (similarity_top_k, num_articles_ref)
Run the MCP inspector to debug the server:
npx @modelcontextprotocol/inspector uvx dappier-mcpWe welcome contributions to expand and improve the Dappier MCP Server. Whether you want to add new search capabilities, enhance existing functionality, or improve documentation, your input is valuable.
For examples of other MCP servers and implementation patterns, see: https://github.com/modelcontextprotocol/servers
Pull requests are welcome! Feel free to contribute new ideas, bug fixes, or enhancements.
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. |
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.
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. |
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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0dappier_ai_recommendationsdappier_real_time_searchThe 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.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
The Dappier MCP server connects LLMs and AI agents to real-time, rights-cleared, proprietary data from trusted sources across various domains. It provides specialized knowledge through real-time web search, financial stock market and crypto data access, AI-powered content recommendations from premium publishers, and structured outputs with sub-300ms response times, enabling AI systems to respond to current events and trends.
Real-time data API for AI Agents: stocks, weather, forex, logistics, search, scrape, news, IP.
Give your agent web search and authoritative datasets: S&P Global, FRED, OECD, SimilarWeb & more.
Give AI assistants access to real-time data. Search the web, compare flights, find hotels, and more.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/DappierAI/dappier-mcp'
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