MCP Search Server with Feedback-Driven Ranking for Claude Desktop

An MCP (Model Context Protocol) search server has been built for Claude Desktop that implements feedback-driven ranking. The server runs two search engines—Exa and Tavily—in parallel, and no API key is required for use.
Setup and Configuration
To add this server to your Claude Desktop configuration, use the following JSON snippet:
{
"mcpServers": {
"meyhem": {
"command": "npx",
"args": ["mcp-remote", "https://api.rhdxm.com/mcp/"]
}
}
}
Tool Workflow
The server provides three tools that follow a specific sequence:
search→select→outcome
After using a search result, users report whether it worked via the outcome tool. This feedback signal is then fed back into the ranking algorithm, causing URLs that help agents succeed to rise in priority for all users. The system is designed to improve as more people use it.
The server is accessible at https://api.rhdxm.com and was shared on the r/ClaudeAI subreddit by user /u/Dashcamvideo.
📖 Read the full source: r/ClaudeAI
👀 See Also

Harnessing Claude Code for Bot Consultancy: A Deep Dive
Exploring the integration of Claude Code within bot development to enhance functionality through AI consultancy, as shared by an enthusiast on r/clawdbot.

Claude Code UltraPlan Workflow Changes and Performance Observations
Claude Code UltraPlan introduces a cloud-based planning workflow with terminal launch, browser review interface, and execution options. Testing showed approximately 2x faster repeated runs than local planning, with mixed quality improvements.

Building a Self-Improving Knowledge System with Claude Code and Obsidian
A developer built a 25-tool system that gives Claude Code persistent memory through semantic search, knowledge graphs, and spaced repetition over an Obsidian vault. The system indexes content with bge-m3 embeddings, detects contradictions, auto-prunes stale notes, and generates Obsidian Canvas maps automatically.

Nanocode: Training Claude-like coding agents with JAX on TPUs
Nanocode is a JAX library for training Claude-like coding agents end-to-end, using Constitutional AI and TPU optimization. The 1.3B parameter model can be trained in ~9 hours for $200 on TPU v6e-8.