Your agent got slower the more MCP servers you added, and it's not the model. Every server dumps its whole tool list into context on every request: 3 servers cost ~24k tokens before you even say hi. Conduit puts them behind one local gateway that exposes 3 meta-tools the agent searches on demand. Measured: 97% less tool overhead per request, ~90% fewer tokens, same task success. Cloud or local, one tool or five. Keys in your OS keychain. Free and open source.
I kept adding MCP servers to my AI tools, and the more I added, the slower and less reliable my agents got. The reason surprised me: every MCP server loads its entire tool list into the model's context on every single request. On my setup that was roughly 24,000 tokens of tool definitions sitting in context before I'd typed a word, and the model discards all of it between calls, so you pay for it again on the next one.
Conduit fixes that. It's a local-first gateway that sits between your AI tools and your MCP servers. Each client connects to it once, and instead of exposing every tool, it exposes three meta-tools the agent searches on demand. The full catalog is still available; it just no longer sits in context on every request.
I benchmarked it on a real setup: 97% less tool overhead per request, around 90% fewer total tokens, at the same task success rate. The full method and numbers are in the repo.
This helps no matter how you work. On cloud models, those tokens are your bill. On local models, the tool definitions eat your context window. Either way, you stop paying for tools the agent never calls.
A few things I focused on:
• API keys stay in your OS keychain, never in a config file
• Nothing phones home, it's fully local-first
• Works with 17 clients today across Windows, macOS, and Linux
• Free and open source
I'd love your feedback, especially on which MCP servers or clients you'd like supported next. Thanks for checking it out 🙏
About Conduit on Product Hunt
“Fix the tool-list bloat slowing your AI agent”
Conduit launched on Product Hunt on June 23rd, 2026 and earned 132 upvotes and 20 comments, placing #13 on the daily leaderboard. Your agent got slower the more MCP servers you added, and it's not the model. Every server dumps its whole tool list into context on every request: 3 servers cost ~24k tokens before you even say hi. Conduit puts them behind one local gateway that exposes 3 meta-tools the agent searches on demand. Measured: 97% less tool overhead per request, ~90% fewer tokens, same task success. Cloud or local, one tool or five. Keys in your OS keychain. Free and open source.
On the analytics side, Conduit competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Conduit performed against the three products that launched closest to it on the same day.
Who hunted Conduit?
Conduit was hunted by Tyler. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Conduit including community comment highlights and product details, visit the product overview.
Tyler
Hi Product Hunt 👋
I'm Tyler, the maker of Conduit
I kept adding MCP servers to my AI tools, and the more I added, the slower and less reliable my agents got. The reason surprised me: every MCP server loads its entire tool list into the model's context on every single request. On my setup that was roughly 24,000 tokens of tool definitions sitting in context before I'd typed a word, and the model discards all of it between calls, so you pay for it again on the next one.
Conduit fixes that. It's a local-first gateway that sits between your AI tools and your MCP servers. Each client connects to it once, and instead of exposing every tool, it exposes three meta-tools the agent searches on demand. The full catalog is still available; it just no longer sits in context on every request.
I benchmarked it on a real setup: 97% less tool overhead per request, around 90% fewer total tokens, at the same task success rate. The full method and numbers are in the repo.
This helps no matter how you work. On cloud models, those tokens are your bill. On local models, the tool definitions eat your context window. Either way, you stop paying for tools the agent never calls.
A few things I focused on:
• API keys stay in your OS keychain, never in a config file
• Nothing phones home, it's fully local-first
• Works with 17 clients today across Windows, macOS, and Linux
• Free and open source
I'd love your feedback, especially on which MCP servers or clients you'd like supported next. Thanks for checking it out 🙏