"The Guardian" matching MCP servers:

Provides OpenCode with adaptive long-term memory by storing preferences, lessons, and usage history locally in SQLite, enabling AI to remember and adapt to users through context-based learning.

Provides tools to fetch and search news from The Verge's RSS feed, allowing users to get today's news, retrieve random articles from the past week, and search for specific keywords in recent Verge content.

A high-performance persistent memory layer for AI agents with multi-modal support, 47 retrieval channels, and 50-tier guardian chains, integrating 12+ memory approaches into a unified architecture.

MCP server that cuts cloud LLM costs 36-42% by indexing context locally and giving agents precision retrieval tools instead of raw context dumps.

Enables retrieval-augmented question answering over a private podcast archive with hybrid search (BM25 + dense embeddings), returning ranked passages with timestamps and citations.

An enterprise-ready MCP server that exposes a RAG tool for retrieving relevant context and metadata from a Qdrant vector database using natural language queries.

Enables AI assistants to search the Godot 4.7 documentation locally using hybrid keyword and semantic search, providing accurate API references with URLs and section hierarchy.

The open-source, self-organizing memory for all your AI tools. Persistent memory over MCP (remember, recall, observe): background agents extract entities, resolve conflicts, and keep cited syntheses current in an append-only, encrypted, single-tenant vault.

Hardened MCP bridge to the full Ollama ecosystem — local and cloud models. 6 tools covering health, model management, chat, and generation with SSRF mitigation, singleton HTTP client, and structured error handling. Two deps, one file.

An MCP server that finds relevant xkcd comics during conversation by semantically matching the context to comic titles, alt text, and transcripts.

Enables any MCP-aware LLM host to filter noisy text down to query-relevant sentences via a topic-conditioned relevancy classifier, reducing token spend before the model processes the full input.

Local-first memory for coding agents. Discovers the memory, instructions, and rules Claude Code, Codex, and Cursor already wrote on your machine, combines them into one canonical Markdown tree, and serves grounded, cited recall through tools like ask_memory and canonical_memory.

Procedural memory for coding agents: not facts about the user, but how a task was solved — trigger, steps, outcome, lessons. Hooks recall it automatically; skills strengthen only on outside evidence and unused ones decay. Each memory carries its provenance and only the owner approves one as a rule. Local SQLite + FTS5, no API key, no cloud. One database for Claude Code, Codex and Cursor.

MCP server for the full Pāli Tipiṭaka — ~444,000 segments at parity with SuttaCentral (Sutta + Vinaya + Abhidhamma). Hybrid search, full-sutta fetch with cross-references, segment-aligned translation comparison, and Pāli word lookup. Offered as Dhamma Dāna.

An MCP server that turns the Hugging Face Hub into a conversational model picker with a tool-use eval harness to prove the agent actually uses it correctly.

Official MCP server for Perplexity API Platform, enabling AI assistants with real-time web search, reasoning, and research capabilities through Sonar models and the Search API.

MCP server that integrates with LM Studio to provide a search_notes tool, allowing the chat model to retrieve and answer from a local Obsidian vault.

Enables agents to perform full-text search and read Hermes Agent documentation offline from a local cache, without scraping the site.

A multi-agent Retrieval-Augmented Generation system exposed as an MCP server. Ask a question and a LangGraph pipeline plans the retrieval, pulls evidence from a pgvector knowledge base, optionally augments it with live web research, drafts a cited answer, and then self-critiques it for grounding — revising until the answer is supported by the sources.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.