"SearXNG, a privacy-respecting metasearch engine" matching MCP servers:

MCP server for using various search tools like Tavily API. Planning to support various search tools (i.e. wiki search, searxng, etc)

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.

Enables natural language interaction with the Korean Personal Information Protection Act (PIPA) through 37 MCP tools that search, compare, analyze, and verify legal texts, official guidelines, and consulting cases from authoritative sources.

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.

Enables privacy-preserving document retrieval by exposing read-only search tools over MCP, allowing agents to query local vector and structured indexes without sending data to the cloud.

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

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 grounded, dated research from a curated AI-knowledge repository, offering free BM25 retrieval and a research loop that combines the knowledge base, curated sources, and the open web with tiered citations.

Serves a Markdown memory vault to AI agents over MCP, letting them write, search, edit, and verify memories while full-text and graph indexes stay in sync in the background. Combines SQLite FTS5 retrieval with a Graphiti temporal knowledge graph so agents can recall both documents and entity facts over time.

Long-term persistent memory for AI agents with hybrid search (DiskANN + FTS), bi-temporal knowledge graph, and 81.1% LongMemEval accuracy.

Graph RAG based context management engine for coding agents

Enables AI applications to submit claims, decisions, and evidence so it maintains a typed state model of beliefs, conflicts, changes, and next actions, preserving claim lineage and semantic evidence across versioned persistence.

A task-aware context compression layer for Agent workflows, RAG pipelines, and AI Coding assistants, reducing noisy logs, retrieval chunks, and code context into high-signal LLM inputs via CLI, Python SDK, and MCP.

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.

MCP server for managing and serving dynamic prompt templates using elegant and powerful text template engine. Create reusable, logic-driven prompts with variables, partials, and conditionals that can be served to any compatible MCP client like Claude Code, Claude Desktop, Gemini CLI, etc.

MCP server for querying a personal knowledge base using semantic RAG, powered by DeepSeek and local embeddings from Ollama.

A persistent world your agents share across sessions and models. MCP memory server with semantic memory retrieval, cross-agent handoff, a hosted remote endpoint with OAuth, a free 24-hour room, and an open-source local stdio server.

An ultra-rational A2A protocol for zero-token edge pre-filtering and FEP-driven deadlock prevention. Uses Cloudflare Vectorize (384d cosine similarity) with a 24h deposit model, restricting bargaining to a 4-rally limit before forcing HTTP 402 dimension jumps.

Indexes your codebase so AI coding agents can search instead of re-reading files, saving up to 94% of tokens.

A narrative graph engine that enables LLMs to generate, track, and mutate complex fictional worlds while maintaining consistency between factions, characters, and locations. It acts as a specialized RAG framework for storytelling, allowing models to manage thousands of entities without exceeding context limits.

Enables unified access to SQL, semantic vector search, and columnar analytics through a single MCP interface.

Enables MCP clients to index code repositories and run AST-aware semantic and hybrid keyword search across files, commits and branches, returning relevant code snippets with expanded file context. It handles incremental re-indexing with local embeddings and Qdrant storage, and can serve either over stdio or as a bearer-token-protected HTTP endpoint for remote or containerized use.

Enables an event-driven operations agent to query internal SQL, GitHub, Slack, Jira, documentation, and Kubernetes telemetry through 28 RBAC-gated MCP tools, with shared memory for playbook retrieval and incident workflows.

A read-only Model Context Protocol server that exposes a semantic knowledge base to AI agents via 27 tools. It enables querying of documents and data integrated from sources like Notion, SharePoint, HubSpot, and Slack.

An MCP server that enables natural language interaction with Google's Discovery Engine API, allowing users to search, recommend, and manage data through conversational interfaces.

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 an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

Enables agents to perform controlled enterprise data queries through semantic intent, with runtime validation of statistics, filters, granularity, permissions, and physical bindings. Exposes tools like semantic_query for safe, fail-closed access to data horizons and capabilities.