"Overview and Applications of Mixture of Experts in Machine Learning" matching MCP servers:

Provides MCP tools to search, retrieve, and answer questions from the public Open Finance Brasil Confluence documentation using BM25-based local indexing.

Enables semantic search over local knowledge bases like Obsidian notes, SQLite, and plaintext files, exposing results to Claude via MCP server.

Provides search and retrieval over a local database of Pathfinder 2e rules. Enables Claude Code to answer rules questions with cited, accurate answers from fetched entries.

Shrimp Task Manager is a task tool built for AI Agents, emphasizing chain-of-thought, reflection, and style consistency. It converts natural language into structured dev tasks with dependency tracking and iterative refinement, enabling agent-like developer behavior in reasoning AI systems.

An MCP server that uses Groq's API to expose raw chain-of-thought tokens from Qwen's qwq model, enabling LLMs to think step-by-step before responding.

Enables AI agents to search and retrieve historical Spanish Call of Duty gaming community posts, with free search previews and pay-per-page citation-ready access via x402.

A scientific reasoning framework that leverages graph structures and the Model Context Protocol (MCP) to process complex scientific queries through an Advanced Scientific Reasoning Graph-of-Thoughts (ASR-GoT) approach.

Transforms prompts into Chain of Draft (CoD) or Chain of Thought (CoT) format to enhance LLM reasoning quality while reducing token usage by up to 92.4%, supporting multiple LLM providers including Claude, GPT, Ollama, and local models.

Enables fetching Google's AI Overview answers and cited sources as structured JSON, with support for batch queries and country/language targeting.

Enables AI agents to index, search, and validate lessons learned across project repositories via a local SQLite index, providing learning-loop resources and verified-solution tracking without loading full files into conversations.

Enables Codex to rank available tools and skills, rerank code and documentation search results, and triage saved output artifacts using Jev-based relevance scoring while leaving final decisions to Codex.

Provides stateful prompt optimization using research-backed techniques like APE and OPRO, learning from historical performance data via a vector database. It enables users to automatically refine prompts, retrieve high-performing examples, and track performance analytics through iterative feedback.

MCP server for tiered, injection-resistant natural-language access to a washing-machine Turso database, supporting stdio and HTTP transports with security guarantees like silent downgrades and audit logging.

All In One Model Context Protocol: qdrant, google suite (gmail, calendar, ...), jira, gitlab, CLI, ...

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Provides LLM-free search tools across Confluence, JIRA, code, and project knowledge for any MCP client.

Tracks learning progress and generates personalized daily learning insights using RAG to fetch relevant content from blogs, RSS feeds, and Reddit based on your current topics and learning goals.

A Node In Layers package that simplifies creation of MCP (Model-Control-Protocol) servers with tools for defining models, adding CRUD operations, and interacting with clients.

An MCP (Model Context Protocol) server that turns the book Deep Learning with Python (François Chollet) into a searchable knowledge base, so Claude can act as a deep-learning/ML expert grounded in the book's content.

Provides a standardized protocol for tool invocation, enabling an AI system to search the web, retrieve information, and provide relevant answers through integration with LangChain, RAG, and Ollama.