"Weather API" matching MCP servers:

MemOS (Memory Operating System) is a memory management operating system designed for AI applications. Its goal is: to enable your AI system to have long-term memory like a human, not only remembering what users have said but also actively invoking, updating, and scheduling these memories.

Provides a local-first, provenance-aware memory layer that enables MCP-capable AIs to store, recall, validate, and reason over facts with contradiction detection, trust weighting, deduplication, and encryption, supporting offline private operation without GPUs or API keys.

Provides a compressed knowledge graph of the NVIDIA AI developer stack for deterministic traversal, enabling agents to answer questions about dependencies and prerequisites with minimal tokens.

A personal digital twin MCP server that manages professional context, including skills, experience, and project learnings. It allows AI assistants to retrieve and update user preferences and career interests across different development environments.

A server that enables AI agents to perform sophisticated knowledge discovery and analysis across Obsidian vaults through the Local REST API plugin, supporting complex multi-step workflows with advanced filtering and full content retrieval.

Enables AI agents to use Obsidian vaults as persistent, bidirectional knowledge workspaces with wikilink/backlink resolution, structured frontmatter/tag indexing, task aggregation, and Obsidian Headless Sync.

An MCP server for managing work logs, research results, and task checkpoints to enable seamless collaboration and state recovery between AI agents. It provides a persistent memory layer for tracking project history and resuming workflows across different sessions or tools.

MCP server that enables AI assistants to interact with the MemOS API, providing memory search, feedback management, and knowledge base operations via MCP-compliant tools.

Unofficial Notion MCP server built on Notion's private API (token_v2 cookie). Gives LLM agents full read/write access to the entire workspace — no integration token and no per-page sharing.

A Model Context Protocol server that provides persistent memory capabilities for AI systems, enabling true continuity of consciousness across conversations through episodic, semantic, procedural, and strategic memory types.

AI development assistant with 36 specialized tools for memory management, code analysis, project planning, and problem-solving through natural language interactions in Korean and English.

Enables AI agents to persist, search, and evolve knowledge through a Markdown vault with a typed knowledge graph and MCP interface.

Persistent semantic memory-as-a-service for legal AI agents. Store and recall case notes, client context, and matter history via MCP. Namespace-isolated, audit-logged, and GDPR-compliant.

A structured project memory MCP server that persists plans, builds, reviews, and decisions across AI coding sessions and tools, enabling continuous project management with a dashboard.

Enables AI assistants to search, create, and manage knowledge graphs in Aiqbee brains for architecture, portfolio, and digital strategy.

Provides LLM clients with persistent memory tools for searching, reading, creating, and updating notes, retrieving backlinks, listing recent notes, and managing tasks using self-hosted MySQL storage.

Open-source AI memory layer for LLM agents. Importance scoring, temporal decay, hierarchical memory (facts, summaries, themes), YMYL prioritization, and active retrieval with contradiction detection. Supports OpenAI, Anthropic, Ollama. Local-first with SQLite + FAISS.

An MCP server that tracks AI prompts in Claude Code and automatically annotates git commits with the history of what was asked. It provides tools to query session summaries, retrieve uncommitted work, and manage AI provenance directly within Claude.

Enables searching imported AI conversations locally across different providers from an MCP client, with read-only access to the archive.

Enables AI coding agents to persist and retrieve project knowledge using three MCP tools with local vector and full-text search.