Conduit

Product Introduction

1. Definition: Conduit is a local-first MCP (Model Context Protocol) gateway. It functions as a native desktop application that acts as an intermediary between AI-powered development tools (like coding agents and editors) and multiple MCP servers. Its technical category is an infrastructure optimization layer for the agentic AI development workflow.
2. Core Value Proposition: Conduit exists to solve the critical problem of MCP server tool bloat. It dramatically reduces token consumption and operational overhead by consolidating hundreds of potential tool definitions from numerous MCP servers into just 3 meta-tools in the AI agent's context. This enables significant token cost savings (~90% fewer tokens) and maintains context efficiency without sacrificing task success rates.

Main Features

1. Local-First Gateway with Meta-Tools: Conduit runs as a native desktop application on Windows, macOS, and Linux. Instead of an AI client connecting directly to multiple MCP servers (which would load all their tool definitions into the context), clients point to the Conduit gateway. Conduit then proxies requests to the appropriate backend servers. It exposes only 3 meta-tools (Search_Tools, Invoke_Tool, List_Servers) to the AI agent. The agent uses Search_Tools to dynamically discover the specific tool it needs from the entire toolset managed by Conduit, then uses Invoke_Tool to execute it. This on-demand search mechanism ensures the agent's context remains flat and efficient, regardless of how many servers or tools are added.
2. Secure Secret Management: API keys and other sensitive credentials are stored securely in the host operating system's native keychain (e.g., macOS Keychain, Windows Credential Manager). Secrets are injected at runtime directly into server requests and are never stored in client configuration files, code, or transmitted to any cloud service. This provides robust security for sensitive data within the MCP ecosystem.
3. Per-Tool Governance and Live Toggles: The Conduit application provides a centralized dashboard with granular control. Users can toggle individual tools on or off with a single switch. This allows for fleet-wide control, such as instantly hiding all destructive or high-risk tools from every connected AI client without requiring restarts or configuration changes. This is crucial for enforcing security policies and managing tool availability.
4. Live Observability and Audit Trail: Built into the desktop app is a comprehensive monitoring dashboard. It displays per-server latency, error rates, and provides a full, searchable audit trail of every tool call made through the gateway. This gives developers and administrators complete visibility into tool usage, performance bottlenecks, and debugging information.

Problems Solved

1. Pain Point: MCP context window inflation and token cost bloat. Every time an AI agent makes a request, all connected MCP servers dump their entire tool definitions into the agent's context. With even a few servers (e.g., 3), this can consume tens of thousands of tokens (~24k) before the user even inputs a query. This leads to high API costs, slower response times, and reduced effective context length for the actual task.
2. Target Audience: This product is essential for AI-powered developers and teams using tools like Claude, Cursor, VS Code (with AI extensions), Codex, Windsurf, or Antigravity in conjunction with multiple MCP servers (e.g., GitHub, Stripe, Supabase, Vercel, Linear, Notion, Sentry, Cloudflare). It is designed for cost-conscious developers, DevOps engineers, and AI platform builders who want to optimize their agentic workflows.
3. Use Cases: A developer using an AI coding assistant connected to MCP servers for GitHub, Stripe, and Supabase would benefit from Conduit to keep their coding context lean and affordable. A DevOps team managing AI agents in a CI/CD pipeline can use Conduit to enforce tool governance and monitor usage across all agents. Anyone experiencing slow agent responses due to large contexts or high token bills from MCP tool usage will find this essential.

Unique Advantages

1. Differentiation: Traditional MCP setups require each AI client to be configured for and aware of every single MCP server, leading to redundant and bloated tool list injection. Conduit introduces a decoupling layer. Clients only need a single, simple configuration pointing to the local gateway. The gateway handles the complexity of managing multiple server connections, secrets, and tool routing. This is analogous to an API gateway in cloud infrastructure, but applied locally to the MCP protocol.
2. Key Innovation: The core innovation is the "meta-tool" architecture combined with a local-first, zero-cloud design. By exposing just three dynamic, searchable meta-tools (Search_Tools, Invoke_Tool, List_Servers), Conduit fundamentally changes the cost model of using multiple MCP servers. It shifts from paying for a constant, large context size to paying for only the tool discovery and invocation steps. The local execution and OS keychain integration ensure performance, privacy, and security without requiring accounts, cloud services, or complex Docker infrastructure.

Frequently Asked Questions (FAQ)

1. What is a Meta-Tool in Conduit and how does it save tokens? A Meta-Tool is a high-level, dynamic tool provided by Conduit that can discover and invoke any specific tool from your connected MCP servers. Instead of loading all 500 tool definitions (costing ~24k tokens) into your AI's context, Conduit loads just 3 meta-tool definitions (costing ~658 tokens). Your AI agent then uses the Search_Tools meta-tool on-demand to find the exact function it needs, like searching a library catalog instead of keeping every book on your desk. This reduces context overhead by 97% per request and saves ~90% of tokens overall.
2. How do I configure my AI tool (like Cursor or VS Code) to use Conduit? You simply point your AI tool's MCP server configuration to your local Conduit gateway URL (typically http://localhost:PORT). Conduit then manages all connections to your actual MCP servers (GitHub, Stripe, etc.). Your AI tool only needs to know about this single endpoint, not each individual server.
3. Is Conduit secure? Where are my API keys stored? Conduit is designed with security and privacy as a priority. API keys are stored only in your operating system's native keychain (like macOS Keychain or Windows Credential Manager). They are injected at runtime for server requests and are never saved in plain text in configuration files or sent to any cloud service. All communication and data processing happen locally on your machine.
4. What AI coding tools and MCP servers does Conduit work with? Conduit is compatible with any AI tool that supports the MCP client protocol, including popular choices like Claude, Cursor, VS Code (with extensions like Continue), Windsurf, Codex, and Antigravity. It can manage connections to virtually any MCP server, with listed examples including GitHub, Stripe, Supabase, Vercel, Linear, Notion, Sentry, and Cloudflare.
5. Is Conduit free and open source? Yes, Conduit is completely free to use and is released as open-source software. The project is available for download for Windows, macOS, and Linux, and its source code is publicly hosted on GitHub for transparency and community contribution.