2105 Model Context Protocol servers — searchable, rated, ready to install.
New Relic MCP server for observing AI coding assistants (Claude Code, Cursor, Copilot, etc.)
Create projects, nodes, and tasks in UluP Spaces by conversation with Claude.
Local RAG MCP for markdown documentation. Retrieval only; host synthesizes answers.
LLM evals as MCP tools: score outputs for faithfulness, relevancy, and hallucination.
Conversational what-if simulation: build, diagnose and compare Petri-net models; CC0 catalog.
Local-first, zero-knowledge AI memory across Claude, Cursor, and every MCP client. 0 RTT reads.
Source discipline for Torah study in Claude: read the real texts, quote exactly, never fabricate
LLM pricing where every rate carries its source and the date it was last confirmed.
Closed-source remote MCP: model benchmarks, costs, HN signals, tech registry.
Local multi-client RAG server over the Model Context Protocol.
Track and query EzStat metrics from Claude, Cursor, or any MCP agent. StatHat-compatible.
Time-series nobody else archives: LLM price history, prediction markets, gas, perp funding.
A business back-office for Claude — tasks, deals, customers, publishing and tracked links.
LinkedIn outreach, commenting, scheduling, and data via Claude and human approval gates.
OpenAI organization usage and cost reporting through an admin API key connected by the user.
Shared memory for a team in Claude Code: what one person records, everyone has.
Train, explain, optimise and deploy transparent glass-box ML models via workflow tools.
An MCP memory server. One memory your agents share — across models, devices and apps.
Retail media measurement in Gus Dantas's own words. No model inside: every answer is text he wrote.
Owned, portable working memory: a plain-files markdown brain any model can read and cite.
AI visibility & recommendation monitoring for ChatGPT, Claude, Gemini & Perplexity.
Statistical regression testing for LLM agents: p-value, effect size, and CI on behavior change.
Sends the same prompt to multiple LLM providers in parallel and returns a divergence score via MCP.
Local-first RAG MCP server: hybrid search over a folder of your own documents
POST /mcp