Conduit is a local-first Model Context Protocol (MCP) gateway designed to solve the problem of excessive token usage when using multiple AI tools. Developed as a native desktop application, it acts as a central hub for all your MCP servers, allowing you to configure and authenticate each server once for use across various AI clients like Claude, Cursor, VS Code, and others. By centralizing these connections, Conduit eliminates the need for redundant setup and provides a unified interface for managing server health, API secrets, and tool accessibility.

Functionality centers on optimizing how AI agents perceive and interact with available tools. Instead of exposing every tool from every connected server to the agent at all times—which often leads to massive context inflation—Conduit employs a lazy discovery mechanism. This feature reduces the tool list presented to the agent to just three meta-tools, regardless of how many actual servers or tools are connected. This architectural approach significantly minimizes token consumption and improves agent performance by ensuring that the context window remains focused on relevant capabilities while maintaining access to the full tool ecosystem.

Some of the key features are:

• Token Optimization: Exposes only three meta-tools to the agent, reducing tool definition overhead by up to 97% and total token count by approximately 90%.
• Universal Integration: Allows AI clients to connect to a single gateway instead of individual MCP servers, ensuring consistent behavior across environments.
• Secure Credential Management: Stores API keys in the operating system's native keychain and injects them at runtime to keep secrets out of client configurations.
• Per-Tool Governance: Offers granular control to toggle individual tools on or off, allowing users to hide specific functionalities fleet-wide.
• Live Observability: Provides built-in monitoring for per-server latency, error rates, and a comprehensive audit trail of every tool call.
• Local-First Architecture: Operates as a local desktop process without requiring cloud infrastructure or external accounts.

Conduit functions as a middleware layer that routes tool requests between the AI agent and the target MCP servers. During operation, when an agent requires a capability, it first interacts with the Conduit meta-tools to search for relevant functionality. Conduit then retrieves the specific tool schemas from the appropriate server and executes the call. This flow ensures that the agent only loads necessary context, effectively transforming context from an unbounded list of definitions into a managed resource that the agent spends purposefully. The process remains transparent to the user, who benefits from faster, more reliable agent performance and lower operational costs.

Some common use cases include:

• Enterprise Agent Management: Enabling teams to share securely configured MCP servers across multiple developers and AI agents with centralized governance.
• Reducing Infrastructure Costs: Minimizing the token usage of large-scale agent deployments to decrease API expenses and improve overall system margin.
• Optimizing Complex Agent Workflows: Improving reasoning accuracy by providing the AI with a relevant, manageable subset of tools rather than an overwhelming list of hundreds of schemas.
• Secure Secret Handling: Centralizing the management of sensitive API keys for numerous local and remote servers within a secure, local-first environment.