"Using a search engine to find evidence to answer a question" matching MCP servers:

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.

Turns GitHub repository history into a cited maintainer skill for coding agents, providing tools to collect evidence, query the knowledge graph, and inspect bundles.

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.

An MCP server for managing K-12 assessment question banks with IRT metadata, enabling creation, organization, and search of test questions with Bloom's taxonomy classification, difficulty calibration, and topic tagging through natural language with Claude.

Pull Deep Memory from across your Claude Code Sessions — when you need it.

Turn Obsidian notes into agent-ready context, preview-only plans, and safe repo handoffs.

Enables AI assistants to read and write a self-hosted Gramps Web family tree through the REST API, creating facts with citations attached and auditing unsourced or duplicate data with 83 evidence-disciplined tools. It also exposes legacy GEDCOM exports as a read-only reference layer of untrusted hints, with living-person privacy filtering on by default.

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

An MCP server providing an append-only, hash-chained evidence ledger for agent actions, where every record is a tamper-evident receipt cryptographically bound to all prior records and persisted as human-readable JSONL local state. It exposes tools to append records, verify chain integrity (pinpointing tampering), query records by actor/action/target/time, and fetch ledger stats—with no update or delete capabilities by design.

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

Enables AI agents to extract PDF text, search with PDF++ citations, access vault files, manage tags, and run Dataview queries in Obsidian via MCP.

Provides coding agents with persistent, evidence-backed project memory and knowledge across sessions, using a structured memory tree and local knowledge base.

Enables programming agents to capture errors and conversation signals, reflect on root causes, consolidate reusable skills, and retrieve relevant context for future tasks, providing a self-learning memory loop.

Enables AI agents to maintain persistent, searchable two-layer memory with 37 tools, hybrid search, knowledge graphs, and enterprise features like authentication and backups.

Graph RAG based context management engine for coding agents

Enables querying PDF documents using natural language with grounded answers and source citations via a local RAG pipeline.

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.

Wraps a dependency-free memory lifecycle engine as six read-only MCP tools, letting any agent extract facts with pure regex, score signals, compute Ebbinghaus decay and archival judgments, and simulate a full five-stage dream cycle for long-term memory growth. Runs over stdio or streamable HTTP with no API key, storing no caller data.

Persistent memory for AI agents with semantic memory, belief tracking, and dream consolidation, enabling cross-session knowledge retention.