mcp-server-collector MCP Server: Universal Adapter for AI Applications

Overview: What is mcp-server-collector MCP Server?

The mcp-server-collector is a specialized MCP (Model Context Protocol) server designed to facilitate the integration of various AI applications by collecting, analyzing, and submitting Model Context Protocols. This server acts as a bridge between diverse AI clients like Claude Desktop, Continue, Cursor, and other applications that need access to or submission of model contexts. By leveraging standard protocols, it ensures seamless interaction among different components in an AI ecosystem.

🔧 Core Features & MCP Capabilities

The mcp-server-collector implements several core features:

Data Collection: It can extract MCP Servers from URLs and content via command-line tools.
Submission: Allows submission of MCP servers to directories similar to mcp.so, thereby enabling wider distribution and usage.

Tools Overview

The server is equipped with three main tools:

extract-mcp-servers-from-url: This tool allows the extraction of MCP Servers from a given URL.
extract-mcp-servers-from-content: Enables the extraction of MCP Servers directly from provided content, such as text or raw data.
submit-mcp-server: Facilitates the submission of an MCP Server to a designated directory.

These tools are essential for maintaining and disseminating model contexts across various platforms and environments.

⚙️ MCP Architecture & Protocol Implementation

Data Flow through the MCP Protocol

MCP operates on a standardized protocol that ensures seamless interaction between AI applications, servers, and data sources. The following Mermaid diagram illustrates this flow:

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

MCP Clients Compatibility Matrix

To highlight its versatility, the mcp-server-collector supports a wide range of MCP clients. Here is the compatibility matrix:

MCP Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✅
Continue	✅	✅	✅
Cursor	❌	✅	❌

This table indicates that resources and tools are available for all clients, while prompts can only be used with certain applications.

🚀 Getting Started with Installation

Install

For Development/Unpublished Servers

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

Here is an example of the configuration:

{
  "mcpServers": {
    "fetch": { "command": "uvx", "args": ["mcp-server-fetch"] },
    "mcp-server-collector": {
      "command": "uv",
      "args": [ "--directory", "path-to/mcp-server-collector", "run", "mcp-server-collector" ],
      "env": { 
        "OPENAI_API_KEY": "sk-xxx", 
        "OPENAI_BASE_URL": "https://api.openai.com/v1", 
        "OPENAI_MODEL": "gpt-4o-mini",
        "MCP_SERVER_SUBMIT_URL": "https://mcp.so/api/submit-project"
      }
    }
  }
}

For Published Servers

The configuration remains largely the same, with only minor adjustments:

{
  "mcpServers": {
    "fetch": { "command": "uvx", "args": ["mcp-server-fetch"] },
    "mcp-server-collector": {
      "command": "uvx",
      "args": [ "mcp-server-collector" ],
      "env": { 
        "OPENAI_API_KEY": "sk-xxx", 
        "OPENAI_BASE_URL": "https://api.openai.com/v1", 
        "OPENAI_MODEL": "gpt-4o-mini",
        "MCP_SERVER_SUBMIT_URL": "https://mcp.so/api/submit-project"
      }
    }
  }
}

💡 Key Use Cases in AI Workflows

Example Workflow 1: Model Context Extraction and Submission

Suppose an AI developer, Alice, wants to integrate a new model context into her application. She can use the extract-mcp-servers-from-url tool on the mcp-server-collector server to gather data from an external source:

extract-mcp-servers-from-url "https://example.com/model-context"

Once extracted, she can then submit it using:

submit-mcp-server "https://example.com/model-context" "path/to/avatar.jpg"

Example Workflow 2: Real-Time Data Processing with MCP

For dynamic applications like live chatbots or real-time data processing systems, the mcp-server-collector ensures that all necessary model contexts are available in a standardized format. This makes it easier for developers to integrate new models without extensive code changes.

🔌 Integration with MCP Clients

To ensure compatibility and ease of integration, the mcp-server-collector supports both known and future MCP clients:

{
  "mcpServers": {
    "fetch": { "command": "uvx", "args": ["mcp-server-fetch"] },
    "mcp-server-collector": {
      "command": [ "uv", "run", "mcp-server-collector" ],
      "env": { 
        "OPENAI_API_KEY": "sk-xxx", 
        "OPENAI_BASE_URL": "https://api.openai.com/v1",
        "OPENAI_MODEL": "gpt-4o-mini",
        "MCP_SERVER_SUBMIT_URL": "https://mcp.so/api/submit-project"
      }
    }
  }
}

📊 Performance & Compatibility Matrix

The mcp-server-collector is optimized for performance and compatibility across different environments. It supports a wide range of platforms, including Windows, MacOS, and Linux.

Real-Time Data Streaming

For instance, real-time data streaming applications can rely on the mcp-server-collector to quickly adapt to incoming model contexts without downtime, ensuring smooth operation and high throughput.

🛠️ Advanced Configuration & Security

Environment Variables

Ensuring security and performance requires setting up environment variables correctly:

OPENAI_API_KEY="sk-xxx"
OPENAI_BASE_URL="https://api.openai.com/v1"
OPENAI_MODEL="gpt-4o-mini"

MCP_SERVER_SUBMIT_URL="https://mcp.so/api/submit-project"

Security Considerations

Sensitive information, such as API keys and URLs, should be stored securely to prevent exposure.

❓ Frequently Asked Questions (FAQ)

Q1: Can the mcp-server-collector handle dynamic and static model contexts? A: Yes, it supports both. The extract-mcp-servers-from-url tool can handle dynamic updates, while submit-mcp-server ensures static contexts are submitted accurately.

Q2: Is there a limit to how many model contexts can be processed at once? A: There is no specific processing limit; however, optimal performance may require tuning based on the system architecture and network conditions.

Q3: How do I ensure data integrity during submission? A: Implement checksum verification or use versioned submissions to maintain data integrity.

Q4: Do these tools support cross-platform integration? A: Yes, they are designed to work across multiple platforms, including Windows, MacOS, and Linux.

Q5: Are there any known issues with the submit-mcp-server tool? A: Users have reported occasional delays in submission due to network conditions. Monitoring and optimizing for high connectivity can mitigate these issues.

👨‍💻 Development & Contribution Guidelines

Building and Publishing

To distribute this package, follow these steps:

Sync dependencies and update the lockfile:

uv sync

Build package distributions:

uv build

Publish to PyPI:

uv publish

Ensure you have set your PyPI credentials via environment variables or flags.

Debugging

Due to the nature of stdio, debugging can be challenging. We recommend using the MCP Inspector.

To launch the MCP Inspector:

npx @modelcontextprotocol/inspector uv --directory path-to/mcp-server-collector run mcp-server-collector

Upon launching, the Inspector will provide a URL to access via your browser.

🌐 MCP Ecosystem & Resources

Get involved and connect with other users:

Learn more about the MCP Protocol from:

By utilizing this MCP server, developers and AI application creators can establish robust connectivity standards that enhance interoperability across various platforms.

mcp-server-collector MCP server