"Understanding Inference Models" matching MCP servers:

Enables AI agents to maintain persistent, inspectable understanding through typed, revisable updates, and to coordinate multi-agent work via shared graph-based stigmergy.

A structure-aware MCP server for querying the SPOKE biomedical knowledge graph, enabling entity resolution, schema introspection, path finding, and safe Cypher queries for biomedical knowledge inference.

A self-contained, in-memory graph database for AI Agents. Provides semantic code understanding through the Model Context Protocol (MCP).

Provides brain-inspired long-term memory for MCP clients, enabling persistent user understanding across sessions with Chinese-optimized hybrid retrieval and evolving user profiles.

Enables AI coding agents to evaluate code snippets and learn from execution outcomes through local bio-inspired associative memory, delivering instant zero-token bug and antipattern reflexes without LLM inference.

A local semantic memory and code-indexer that uses AST parsing for structural understanding and persists architectural decisions to help AI assistants bypass context window limits.

An MCP server that provides persistent memory for database schemas by storing table structures and metadata in a local SQLite file. It enables LLMs to save, retrieve, and manage database snapshots to maintain a structured understanding of database architectures.

TMF helps AI coding agents detect stale source context and retrieve source-linked code understanding on demand. It preserves traceable context across code paths so agents can consider upstream and downstream effects when making changes.

MCP server exposing a deterministic, local knowledge graph over stdio. Zero LLM calls in the bridge; answers are classified as Fact, Inference, or Unknown and persisted in redb (ACID, BLAKE3-hashed).

Cline MCP integration that allows users to save, search, and format memories with semantic understanding, providing tools to store and retrieve information using vector embeddings for meaning-based search.

Enables Codex to use a local EverOS instance as long-term memory through MCP. Supports memory operations like search, get, save, and flush with automatic project ID inference.

Saves the user's exact words as immutable, SHA-256-hashed captures before anything else, then derives events, entities, context cards, and causal hypotheses — every fact traceable to its verbatim source. Local-first personal memory for Claude Code, Codex, and any MCP client.

This project implements a Model Context Protocol (MCP) server for connecting AI models with Obsidian knowledge bases. Through this server, AI models can directly access and manipulate Obsidian notes, including reading, creating, updating, and deleting notes, as well as managing folder structures.

SourceSage is an MCP (Model Context Protocol) server that efficiently memorizes key aspects of a codebase—logic, style, and standards—while allowing dynamic updates and fast retrieval. It's designed to be language-agnostic, leveraging the LLM's understanding of code across multiple languages.

A Model Context Protocol server implementation that enables LLMs to interact with NebulaGraph database for graph exploration, supporting schema understanding, queries, and graph algorithms.

An MCP server that enables AI models to retrieve information from Ragie's knowledge base through a simple 'retrieve' tool.

Enables an AI assistant to build and revise an explicit shared world model, then reason, create, narrate, remember, and give writing feedback through inspectable processes, concepts, and cuts. Supports general-purpose modeling, storytelling, agent/user memory, and ideation over one engine and graph while keeping estimates, interpretations, and observations distinct.

Allows AI models to query and interact with FalkorDB graph databases through the Model Context Protocol (MCP) specification.

An open-source memory layer that provides persistent project context and architectural history for AI development tools across multiple platforms and sessions. It enables AI assistants to maintain a shared understanding of codebases while integrating directly with services like Notion for documentation management.

The first Model Context Protocol server for Unity projects. It gives AI assistants real understanding of Unity scenes, prefabs, and C# scripts through a queryable knowledge graph.