Driflyte MCP Server

Driflyte MCP Server

MCP Server for Driflyte.

The Driflyte MCP Server exposes tools that allow AI assistants to query and retrieve topic-specific knowledge from recursively crawled and indexed web pages. With this MCP server, Driflyte acts as a bridge between diverse, topic-aware content sources (web, GitHub, and more) and AI-powered reasoning, enabling richer, more accurate answers.

What It Does

• Deep Web Crawling: Recursively follows links to crawl and index web pages.
• GitHub Integration: Crawls repositories, issues, and discussions.
• Extensible Resource Support: Future support planned for Slack, Microsoft Teams, Google Docs/Drive, Confluence, JIRA, Zendesk, Salesforce, and more.
• Topic-Aware Indexing: Each document is tagged with one or more topics, enabling targeted, topic-specific retrieval.
• Designed for RAG with RAG: The server itself is built with Retrieval-Augmented Generation (RAG) in mind, and it powers RAG workflows by providing assistants with high-quality, topic-specific documents as grounding context.
• Designed for AI with AI: The system is not just for AI assistants — it is also designed and evolved using AI itself, making it an AI-native component for intelligent knowledge retrieval.

Usage & Limits

• Free Access: Driflyte is currently free to use.
• No Signup Required: You can start using it immediately — no registration or subscription needed.
• Rate Limits: To ensure fair usage, requests are limited by IP:
• `100` API requests per `5` minutes per IP address.
• Future changes to usage policies and limits may be introduced as new features and resource integrations become available.

Prerequisites

• Node.js 18+
• An AI assistant (with MCP client) like Cursor, Claude (Desktop or Code), VS Code, Windsurf, etc ...

Configurations

CLI Arguments

Driflyte MCP server supports the following CLI arguments for configuration:

• --transport - Configures the transport protocol (defaults to stdio).
• --port – Configures the port number to listen on when using streamable-http transport (defaults to 3000).

Quick Start

This MCP server (using STDIO or Streamable HTTP transport) can be added to any MCP Client like VS Code, Claude, Cursor, Windsurf Github Copilot via the @driflyte/mcp-server NPM package.

ChatGPT

• Navigate to Settings under your profile and enable Developer Mode under the Connectors option.
• In the chat panel, click the + icon, and from the dropdown, select Developer Mode. You’ll see an option to add sources/connectors.
• Enter the following MCP Server details and then click Create:
• Name: Driflyte
• MCP Server URL: https://mcp.driflyte.com/openai
• Authentication: No authentication
• Trust Setting: Check I trust this application

See How to set up a remote MCP server and connect it to ChatGPT deep research and MCP server tools now in ChatGPT – developer mode for more info.

Claude Code

Run the following command. See Claude Code MCP docs for more info.

Local Server

claude mcp add driflyte -- npx -y @driflye/mcp-server

Remote Server

claude mcp add --transport http driflyte https://mcp.driflyte.com/mcp

Claude Desktop

Local Server

Add the following configuration into the claude_desktop_config.json file. See the Claude Desktop MCP docs for more info.

{

"mcpServers": { "driflyte": { "command": "npx", "args": ["-y", "@driflyte/mcp-server"] } } }

Remote Server

Go to the Settings > Connectors > Add Custom Connector in the Claude Desktop and add the new MCP server with the following fields:

• Name: Driflyte
• Remote MCP server URL: https://mcp.driflyte.com/mcp

Copilot Coding Agent

Add the following configuration to the mcpServers section of your Copilot Coding Agent configuration through Repository > Settings > Copilot > Coding agent > MCP configuration. See the Copilot Coding Agent MCP docs for more info.

Local Server

{

"mcpServers": { "driflyte": { "type": "local", "command": "npx", "args": ["-y", "@driflyte/mcp-server"] } } }

Remote Server

{

"mcpServers": { "driflyte": { "type": "http", "url": "https://mcp.driflyte.com/mcp" } } }

Cursor

Add the following configuration into the ~/.cursor/mcp.json file (or .cursor/mcp.json in your project folder). Or setup by 🖱️One Click Installation. See the Cursor MCP docs for more info.

Local Server

{

"mcpServers": { "driflyte": { "command": "npx", "args": ["-y", "@driflyte/mcp-server"] } } }

Remote Server

{

"mcpServers": { "driflyte": { "url": "https://mcp.driflyte.com/mcp" } } }

Gemini CLI

Add the following configuration into the ~/.gemini/settings.json file. See the Gemini CLI MCP docs for more info.

Local Server

{

"mcpServers": { "driflyte": { "command": "npx", "args": ["-y", "@driflyte/mcp-server"] } } }

Remote Server

{

"mcpServers": { "driflyte": { "httpUrl": "https://mcp.driflyte.com/mcp" } } }

Smithery

Run the following command. You can find your Smithery API key here. See the Smithery CLI docs for more info.

npx -y @smithery/cli install @serkan-ozal/driflyte-mcp-server --client <SMITHERY-CLIENT-NAME> --key <SMITHERY-API-KEY>

VS Code

Add the following configuration into the .vscode/mcp.json file. Or setup by 🖱️One Click Installation. See the VS Code MCP docs for more info.

Local Server

{

"mcp": { "servers": { "driflyte": { "type": "stdio", "command": "npx", "args": ["-y", "@driflyte/mcp-server"] } } } }

Remote Server

{

"mcp": { "servers": { "driflyte": { "type": "http", "url": "https://mcp.driflyte.com/mcp" } } } }

Windsurf

Add the following configuration into the ~/.codeium/windsurf/mcp_config.json file. See the Windsurf MCP docs for more info.

Local Server

{

"mcpServers": { "driflyte": { "command": "npx", "args": ["-y", "@driflyte/mcp-server"] } } }

Remote Server

{

"mcpServers": { "driflyte": { "serverUrl": "https://mcp.driflyte.com/mcp" } } }

Components

Tools

• list-topics: Returns a list of topics for which resources (web pages, etc ...) have been crawled and content is available. This allows AI assistants to discover the most relevant and up-to-date subject areas currently indexed by the crawler.
• Input Schema: No input parameter supported.
• Output Schema:
• topics:
• Optinal: false
• Type: Array
• Description: List of the supported topics.
• search: Given a list of topics and a user question, this tool retrieves the top-K most relevant documents from the crawled content. It is designed to help AI assistants surface the most contextually appropriate and up-to-date information for a specific topic and query. This enables more informed and accurate responses based on real-world, topic-tagged web content.
• Input Schema:
• topics
• Optinal: false
• Type: Array
• Description: A list of one or more topic identifiers to constrain the search space. Only documents tagged with at least one of these topics will be considered.
• query
• Optinal: false
• Type: string
• Description: The natural language query or question for which relevant information is being sought. This will be used to rank documents by semantic relevance.
• topK
• Optinal: true
• Type: number
• Default Value: 10
• Min Value: 1
• Max Value: 30
• Description: The maximum number of relevant documents to return. Results are sorted by descending relevance score.
• Output Schema:
• documents:
• Optional: false
• Type: Array
• Description: Matched documents to the search query.
• Type: Document:
• content
• Optinal: false
• Type: string
• Description: Related content (full or partial) of the matched document.
• metadata
• Optinal: false
• Type: Map
• Description: Metadata of the document and related content in key-value format.
• score
• Optinal: false
• Type: number
• Min Value: 0
• Max Value: 1
• Description: Similarity score (between 0 and 1) for the content of the document.

Resources

N/A

Roadmap

• Support more content types (.pdf, .ppt/.pptx, .doc/.docx, and many others applicable including audio and video file formats ...)
• Integrate with more data sources (Slack, Teams, Google Docs/Drive, Confluence, JIRA, Zendesk, Salesforce, etc ...))
• And more topics with their resources

Issues and Feedback

Please use GitHub Issues for any bug report, feature request and support.

Contribution

If you would like to contribute, please

• Fork the repository on GitHub and clone your fork.
• Create a branch for you