float-mcp

--- title: Research Session query: 'swarm-development-centralized-1751992067817/architect/analysis' date: 7/8/2025 time: 12:31:16 PM timestamp: 2025-07-08T16:31:16.782Z exchanges: 1 --- # Research Session ## Initial Query **Question:** swarm-development-centralized-1751992067817/architect/analysis **Response:** ## MCP Architecture Overview **Model Context Protocol (MCP)** is designed around a modular, client-server architecture that enables AI-powered applications to interact with external tools and data sources in a standardized way. The architecture is composed of three primary components: - **Host**: The central AI application (e.g., Claude Desktop, Cursor IDE) that users interact with. It manages the overall system and serves as the main interface for AI functionality[1][3][4]. - **Client**: Acts as an intermediary, maintaining connections between the host and one or more MCP servers. It handles the communication protocol and manages the flow of data and commands[1][3][4]. - **Server**: Exposes specific functionalities, data sources, or tools to AI models through a standardized interface. Each server provides a set of "tools" (executable functions) that the AI can call[1][3][4]. This architecture allows for **centralized management** of AI tools and resources, while also supporting modularity and extensibility. Each server can be specialized (e.g., file system, email, calendar, or, in your case, Float MCP for project management), and hosts can connect to multiple servers as needed[3][4]. --- ## MCP Server Structure and Tooling Each MCP server exposes a set of **tools**—discrete, callable functions that perform specific actions or queries. These tools are registered with the server and made available to the host via the client. For example, a Float MCP server might expose tools like `create_project`, `list_tasks`, or `get_person`[3][5]. **Example tool registration (Python/FastMCP):** ```python from mcp.server.fastmcp import FastMCP mcp = FastMCP("float_mcp") @mcp.tool() def create_project(name: str, client_id: int) -> dict: # Implementation here return {"project_id": 123, "status": "created"} ``` This pattern ensures that all tools are discoverable and callable in a uniform way, regardless of the underlying resource or action[3][5]. --- ## Centralization and Modularity - **Centralized Control**: The host application orchestrates all interactions, providing a single point of entry for users and AI models. This centralization simplifies authentication, logging, and monitoring[1][3]. - **Modular Expansion**: New servers (and thus new toolsets) can be added without modifying the host or client, supporting rapid expansion and integration of new capabilities[1][2][3]. - **Security and Access Control**: Servers can be configured for different access levels (e.g., read-only vs. full access), allowing for safe exploration in production environments or full control in development[4]. --- ## Application to Float MCP Project For your **Float MCP** project, this architecture means: - The **Float MCP server** acts as a specialized MCP server, exposing project management, task management, people management, and other tools to the host (e.g., Claude Desktop). - The **client** (integrated into the host or as a standalone component) manages the connection and protocol, ensuring secure and reliable communication. - The **host** (AI application) can discover and invoke any registered tool, enabling advanced AI-driven workflows for project/resource management. This structure supports both your current TypeScript implementation and the planned Python/FastMCP migration, as both follow the same architectural principles and tool registration patterns[3][5]. --- ## Architectural Best Practices - **Tool Granularity**: Keep tools focused and atomic (one action per tool) for composability and clarity. - **Error Handling**: Implement robust error handling and validation at the server/tool level to ensure predictable behavior for the host and client. - **Extensibility**: Design the server to allow easy addition of new tools/resources as Float API capabilities expand. - **Security**: Use bearer token authentication and enforce rate limits at the server level, as per Float API requirements. --- ## Example: Centralized vs. Modular MCP Architecture | Aspect | Centralized (Host-centric) | Modular (Server-centric) | | ----------- | -------------------------- | ---------------------------- | | Control | Host manages all logic | Servers encapsulate logic | | Expansion | Add servers/tools easily | Add tools per server | | Security | Centralized auth/logging | Per-server access control | | Maintenance | Host updates for new logic | Server updates for new tools | --- **In summary:** MCP's architecture provides a robust, scalable, and modular foundation for building AI-integrated applications like Float MCP, supporting both centralized management and flexible expansion through standardized tool interfaces[1][3][4]. --- _Generated by Task Master Research Command_ _Timestamp: 2025-07-08T16:31:16.782Z_