"Using llama.cpp and Searxng with local models" matching MCP servers:

Provides structured search tools (web, news, images, videos, research) via a SearXNG instance, with direct URL reading disabled by default for security.

Integrates SearXNG API to give AI assistants web search and URL reading capabilities.

An MCP server that integrates the SearXNG API for web search and URL content extraction with advanced features like pagination, caching, and proxy support.

A minimal MCP server that exposes a private SearXNG instance as a search tool over streamable-HTTP, enabling web search from the llama.cpp WebUI or any compatible MCP client.

Provides LLM-free search, fetch, and rerank tools over any reachable SearXNG instance, returning extracted, deduplicated, cross-encoder-reranked results with citation-ready markers and coverage signals. Lets agent hosts such as Open WebUI, Claude, or tool-calling models drive their own iterative research loop while sharing one deterministic retrieval and ranking core.

Enables web search through SearXNG instances, offering tools for general and category-specific queries, engine selection, time filtering, and engine discovery.

Enables AI agents to run SearXNG-powered metasearch across multiple engines and categories, with optional vector-based semantic re-ranking of results and Tavily-compatible responses. It also converts many document and media formats—including PDF, Office files, Outlook messages, and audio or YouTube recordings—into text.

Exposes locally hosted MLX models as MCP tools for Claude Desktop and Codex, with optional SearXNG search augmentation for private local inference.

Enables AI tools to search and manage a private local knowledge base via MCP or HTTP, using local Chinese semantic retrieval without sending data externally.

A local-first document retrieval MCP server that enables AI coding tools like Codex to search private local documents via semantic search and keyword boost, supporting ingestion of PDF, DOCX, TXT, Markdown, and HTML files.

MCP server that enables local hybrid semantic and keyword search over private PDF, DOCX, Markdown, and text documents without sending data to embedding APIs.

Privacy-first local document search using semantic search. Runs entirely on your machine with no cloud services, supporting PDF, DOCX, TXT, and Markdown files.

Enables developers to index and search their own local PDF, DOCX, Markdown, and text documents from an MCP client or terminal, combining semantic similarity with keyword matching for exact technical terms. Everything runs on the user's machine with no API key, Docker, or external database, so private documents stay private and work offline after the initial model download.

Enables full-text search and contextual lookup across local DSH session logs, workspace documents, skills, and persona files using an offline SQLite FTS5 index with Chinese bigram tokenization, plus incremental indexing and status statistics.

A fully local RAG MCP server for semantic code search and code intelligence, using AST-level chunking and hybrid search to pinpoint functions, classes, and APIs. No cloud, no API keys, zero setup.

A local MCP server that indexes codebases using Tree-sitter AST parsing and LanceDB, enabling AI models to search across repositories with sub-second latency.

Enables storing and retrieving text passages based on semantic meaning using local embeddings (Ollama) and vector storage (ChromaDB), allowing conversational memorization and retrieval of information.

A local-first MCP server for PageIndex — the vectorless, reasoning-based RAG framework. It lets local AI agents index and query local PDF and Markdown documents through a self-hosted PageIndex installation, without requiring any PageIndex cloud API key.

Local-first temporal knowledge graph with six read-only MCP retrieval tools. Supports Ollama and embedded LadybugDB; additions use a separate CLI proposal queue with human approval and explicit application.

Enables MCP clients to administer a local LM Studio instance: loading, estimating, downloading, configuring and unloading models, running chat and embedding inference, diagnosing the server, engines and hardware, and managing MCP connectors with conflict-safe configuration. It also builds a local document index supporting citation-checked search and answers grounded in TXT, Markdown, PDF and DOCX files.