FileFinder MCP Server: Universal Data Access for AI Applications

Overview: What is FileFinder MCP Server?

The FileFinder MCP (Model Context Protocol) server is a Python-based application designed to facilitate the search and retrieval of files based on partial path fragments within the file system. This server adheres to the Model Context Protocol, enabling seamless integration and data access for a variety of AI applications such as Claude Desktop, Continue, Cursor, and others.

🔧 Core Features & MCP Capabilities

The FileFinder MCP server is characterized by its robust capabilities in searching and returning files in JSON format. It supports real-time searches based on user-defined parameters, ensuring efficient and accurate data retrieval. This server’s integration with the Model Context Protocol enables AI applications to query file paths without the need for custom scripting.

Search Functionality

Users can initiate a search using a partial path fragment as input. For example:

curl "http://localhost:8080/?path=example"

or through postman requests:

http://localhost:8080/?path=readme

Upon execution, the server processes the request and returns matching file entries in JSON format.

Data Format

The response from the server is structured as a list of dictionaries, each representing a file with relevant details such as name, path, size, and creation date:

[
    {
        "name": "readme.markdown",
        "path": "/AdobePhotoshop\\Adobe\\Adobe Photoshop CC 2019\\Required\\Generator-builtin\\node_modules\\optimist\\readme.markdown",
        "size": 11208,
        "creation_date": "2019-10-17T15:25:58Z"
    },
    {
        "name": "readme.markdown",
        "path": "/AdobePhotoshop\\Adobe\\Adobe Photoshop CC 2019\\Required\\Generator-builtin\\node_modules\\optimist\\node_modules\\minimist\\readme.markdown",
        "size": 1712,
        "creation_date": "2019-10-17T15:25:58Z"
    },
    // Additional file entries
]

⚙️ MCP Architecture & Protocol Implementation

The FileFinder MCP server is architected to align with the Model Context Protocol’s standards, ensuring seamless interaction between AI applications and data sources. The protocol flow can be visualized as follows:

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

This diagram illustrates how an AI application uses the MCP client to interact with the MCP protocol, which then routes requests to the corresponding server and ultimately retrieves data from the designated source.

🚀 Getting Started with Installation

To set up the FileFinder MCP server on your system:

1. Clone this repository or download the file_finder_mcp.py script.
2. Ensure you have Python 3.x installed on your machine.
3. Open a terminal and navigate to the directory containing file_finder_mcp.py.
4. Run the following command to start the server:

python file_finder_mcp.py

The server will start running on port 8080, and you should see the message:

Starting MCP server on port 8080...

💡 Key Use Cases in AI Workflows

Here are two realistic examples of how FileFinder can be integrated into AI workflows:

Scenario 1: Real-Time Document Search for Developers

Developers often need to quickly locate specific files within a project directory structure. With the FileFinder MCP server, developers can initiate searches using a partial path fragment and receive relevant results in real time.

curl "http://localhost:8080/?path=src/app.js"

The response might look like this:

[
    {
        "name": "app.js",
        "path": "/DevelopmentProjects\\MyProject\\src\\app.js",
        "size": 5321,
        "creation_date": "2021-10-30T14:20:48Z"
    },
    {
        "name": "app.js",
        "path": "/DevelopmentProjects\\MyOtherProject\\src\\app.js",
        "size": 6325,
        "creation_date": "2021-11-15T15:30:48Z"
    },
]

Scenario 2: Automated File Logging for Data Processing

In a data processing environment, automated logging might require frequent updates to files. The FileFinder MCP server can be employed to monitor and retrieve these logs in real-time, ensuring that all relevant data is always up-to-date.

🔌 Integration with MCP Clients

To integrate the FileFinder MCP server with an MCP client (such as Claude Desktop or Continue):

1. Install VSCode to host the configuration.
2. Open VSCode and install the Cline extension from the marketplace.
3. Ensure Python 3.x is installed.
4. Navigate to the project directory in VSCode, open a terminal, and run:

python file_finder_mcp.py

Create or modify a .vscode/cline-config.json file with configuration details:

{
    "mcpServers": {
        "file-finder-mcp": {
            "args": [
                "python",
                "file_finder_mcp.py"
            ],
            "command": "python",
            "autoApprove": [],
            "disabled": false
        }
    }
}

Save the file, and VSCode should automatically connect to the MCP server.

📊 Performance & Compatibility Matrix

The FileFinder MCP server is compatible with a range of MCP clients. Below is an example compatibility matrix:

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

This matrix ensures that developers can leverage the strengths of different tools and resources while maintaining consistency in data access.

🛠️ Advanced Configuration & Security

For advanced usage, you may need to adjust the configuration based on your specific needs. Here are a few additional steps:

1. Modify the server code as needed.
2. Secure connections by implementing authentication mechanisms if required.
3. Customize the response format for better integration with AI applications.

❓ Frequently Asked Questions (FAQ)

Q1: Is the FileFinder MCP Server compatible with all MCP clients?

A1: The server is designed to be compatible with Claude Desktop, Continue, and Cursor. However, for more limited features in some clients, refer to the compatibility matrix provided.

Q2: How can I secure data access in the FileFinder MCP Server?

A2: Security concerns require implementing authentication and authorization mechanisms. Review the documentation for detailed steps on securing connections.

Q3: Can I customize the response format of the FileFinder MCP Server?

A3: Yes, you can modify the server code to change the structure or content of the JSON responses as needed.

Q4: How do I handle large file databases with FileFinder MCP Server?

A4: For larger datasets, consider optimizing indexing and query performance to ensure efficient data retrieval.

Q5: Are there any known issues with the FileFinder MCP Server?

A5: While the server is generally reliable, issues may arise from version mismatches or unexpected configurations. Refer to the official documentation for troubleshooting tips.

👨‍💻 Development & Contribution Guidelines

To contribute to the development of the FileFinder MCP server:

1. Fork and clone the repository.
2. Review the existing codebase and documentation.
3. Submit pull requests with well-documented changes.

We welcome contributions from the community to enhance the functionality and usability of the FileFinder.

🌐 MCP Ecosystem & Resources

The wider MCP ecosystem includes a variety of tools, servers, and clients that work together to provide a unified platform for AI developers. Resources like documentation, tutorials, and community forums are available to help you get started and stay informed about the latest developments in the field.

By leveraging the FileFinder MCP server, developers can significantly streamline their development process and enhance their integration with various AI tools.