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Open-source load balancer for distributed MCP server architecture

Hacker News

Open-source load balancer for distributed MCP server architecture

Like many others, I’ve been hacking on MCP servers lately. The issue I ran into was that running multiple MCP servers behind a unified backend was hard. I needed a way to combine all the tool calls from different MCP servers so the client got a unified view, route requests to the correct server based on the tool call, and maintain session affinity for SSE requests. This led me to build CATIE, which solves the above issues. CATIE is a lightweight proxy that routes MCP requests to the appropriate backend services based on their content. Key Features - Content-Based Routing: Routes requests based on tool call. This allows server operators to use a micro-service architecture, with the user only installing one server. This separation allows server operators to scale tool calls independently. - Unified Tool Call Response: Combine the tool list response from multiple MCP servers. - Session Stickiness: Maintains client connections to the same backend - Pattern Matching: Uses regex patterns to route tool requests - Real-time Monitoring: Simple dashboard to see traffic patterns and performance with built-in Prometheus integration. - Backend Switching: Change where requests go without client reconfiguration How It Works CATIE sits between your clients and your MCP servers. When a request comes in, it: - Parses the JSON-RPC request to understand what it's trying to do - Applies your routing rules to determine the appropriate backend - Forwards the request to the backend - Maintains session stickiness for ongoing conversations - Has a built-in UI for monitoring statistics and integrates with Prometheus CATIE is fully open source under the MIT license. Contributions, feedback, and feature requests are all welcome! - GitHub: https://github.com/mclenhard/catie-mcp - Docs: https://www.catiemcp.com/docs/

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Product HuntOn track for Day 1 leaderboard · Strong signals: mcp, user, open · Missing: mac, agents, macos
86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, io · Missing: https docs, excited, just released
39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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