Search-Fetch Server MCP Server: Streamlining Integrated Note Management and Data Fetching

Overview: What is Search-Fetch Server MCP Server?

The Search-Fetch Server is a TypeScript-based Model Context Protocol (MCP) server that implements a simple but powerful notes management system. It showcases fundamental MCP concepts by offering tools for creating, fetching, and searching through notes—each with its own note:// URI and metadata. This document will detail the capabilities of this server, how it integrates with AI applications like Claude Desktop, and provide you with the necessary setup instructions.

🔧 Core Features & MCP Capabilities

Resources

The Search-Fetch Server equips users with a resource-based model where notes are accessible via note:// URIs. Each note carries essential details such as:

• Title: A brief description of the note content.
• Content: The main text or data stored in the note.
• Metadata: Additional information that can be used for categorization and filtering.

The server supports plain text MIME types, allowing straightforward rendering and editing. This architecture ensures seamless integration with AI applications that require structured and context-rich data.

Tools

To facilitate efficient workflow and data management, this MCP server offers a range of tools:

• Create New Notes (create_note): Allows users to generate new notes by specifying the title and content. Once submitted, these notes are stored within the server’s state.
• Fetch URLs (fetch_url): Enables downloading and processing of web content. The API accepts a URL as input and provides the option to convert fetched results into markdown using Puppeteer if desired. This feature is crucial for integrating external data sources directly into your workflow.
• Perform Searches (duckduckgo_search): Facilitates robust search capabilities through interactions with the DuckDuckGo search engine. Users can pass queries, which are then translated by the server and returned as structured JSON responses.

Prompts

For advanced usage scenarios, the summarize_notes prompt generates detailed summaries of all stored notes. This prompt incorporates all note content as embedded resources to ensure comprehensive coverage, making it ideal for summarization or review processes within AI applications.

⚙️ MCP Architecture & Protocol Implementation

The Search-Fetch Server is designed according to the Model Context Protocol (MCP), which serves as a standardized framework for connecting AI applications with data sources and tools. By adhering to MCP, this server ensures compatibility across various platforms and maximizes interoperability.

MCP Protocol Flow

The following Mermaid diagram illustrates the core protocol flow when using the Search-Fetch Server:

graph TD;
    A[AI Application] -->|MCP Client| B[MCP Protocal]
    B --> C[Search-Fetch Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe;
    style C fill:#f3e5f5;
    style D fill:#e8f5e8;

Data Architecture

The underlying architecture of the Search-Fetch Server is designed to efficiently manage notes and associated data. The server maintains a state that stores all created, fetched, or summarized notes, ensuring real-time updates for any connected client.

🚀 Getting Started with Installation

To get started using this MCP server, follow these steps:

Install Dependencies

npm install

Build the Server

Run the build command to compile the necessary files:

npm run build

Develop with Auto-Reload

For an enhanced development experience with live-reloading capabilities:

npm run watch

💡 Key Use Cases in AI Workflows

Case Study 1: Rapid Note-Summarization for Knowledge Base Construction

In a knowledge base development project, the Search-Fetch Server can be used to create and summarize notes, enabling quick access to information across multiple sources. For instance:

• Users might collect data from various web pages relevant to their field.
• Using the create_note tool, they store this data in easily managed format.
• The summarize_notes prompt generates a concise summary that captures key insights.

Case Study 2: Seamless Integration for Custom Prompting

For an application that requires dynamic prompting capabilities, incorporating the Search-Fetch Server provides several benefits:

• Users can leverage pre-existing notes to generate context-aware prompts.
• The duckduckgo_search tool facilitates real-time searching for additional information.
• This integration ensures the AI application has up-to-date and relevant data.

🔌 Integration with MCP Clients

The Search-Fetch Server is compatible with several MCP clients, including:

| MCP Client | Resources | Tools | Prompts | Status |
|------------|-----------|-------|---------|---------|
| Claude Desktop | ✅ | ✅ | ✅ | Full Support |
| Continue | ✅ | ✅ | ✅ | Full Support |
| Cursor | ❌ | ✅ | ❌ | Tools Only |

Developers are encouraged to explore these integrations by configuring the server following instructions provided in the MCP documentation.

📊 Performance & Compatibility Matrix

The Search-Fetch Server has been optimized for efficient data fetching and search operations. Its performance metrics include:

• Notes storage: Handles thousands of notes without significant degradation.
• URL fetching: 30-second timeout with Puppeteer support.
• Search responses: Returns results within a few seconds.

MCP Configuration Sample

Here’s an example of how to set up the Search-Fetch Server in your MCP client configuration:

{
  "mcpServers": {
    "search-fetch-server": {
      "command": "node",
      "args": ["/path/to/search-fetch-server/build/index.js"]
    }
  }
}

🛠️ Advanced Configuration & Security

Environment Variables

Ensure your environment variables are correctly set to maintain the confidentiality and integrity of sensitive data:

{
  "env": {
    "API_KEY": "your-api-key"
  }
}

For added security, it is highly recommended to manage these keys securely using environment management tools.

❓ Frequently Asked Questions (FAQ)

Q1: Can I use this server with other MCP clients?

A1: Yes, the Search-Fetch Server supports several MCP clients including Claude Desktop and Continue. However, integration with Cursor requires additional setup due to its current feature limitations.

Q2: How do I integrate third-party tools using fetch_url?

A2: Use the fetch_url tool by passing a URL parameter. The optional use_puppeteer flag can convert fetched content into markdown format for better readability, especially from dynamic web pages.

Q3: Are there any limitations on the number of notes or searches?

A3: There are no hard-coded limits, but performance may degrade with extremely large numbers of notes or concurrent searches. Regular maintenance and optimization are recommended for optimal performance.

Q4: How do I debug issues within the Search-Fetch Server?

A4: Utilize the MCP Inspector tool by running:

npm run inspector

This provides detailed debugging tools accessible via your web browser.

Q5: Can I customize resources like note titles and metadata through MCP?

A5: Yes, the resource management is highly customizable. However, modifications require updates to both client and server configurations for seamless interaction.

👨‍💻 Development & Contribution Guidelines

Contributions are welcome! To contribute code or documentation, please follow these guidelines:

• Fork the repository.
• Create a new branch from main.
• Open a pull request detailing your changes.

For detailed development instructions, refer to the official MCP documentation and our contribution guidelines.

🌐 MCP Ecosystem & Resources

Explore more about the Model Context Protocol and its ecosystem at the official MCP GitHub Repository. This community-driven project aims to enhance interoperability between AI applications through standardized protocols like MCP.

Additionally, join our community forums or slack channels for networking with other developers and users.