"A GitHub repository named 'ahujasid/blender-mcp'" matching MCP servers:

MCP server that provides a citation-first memory layer for AI agents, enabling verified search across Git-backed repositories and optional conversation memory. It exposes tools for doctor, sync, search, get, init, and ingest, returning JSON results with verified citations or abstain status.

Enables AI agents to search, read, and traverse a local knowledge base of Markdown files using full-text search and relationship graph, reducing token usage.

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.

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.

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

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

Project management and session memory for AI agents that provides 13 tools for tracking projects, tasks, and context during AI coding sessions.

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

Provides AI assistants with persistent graph database memory using Neo4j, enabling task management, relationship understanding, semantic search with embeddings, file indexing, and multi-agent coordination through the Model Context Protocol.

MCP server for integrating LightRAG with AI tools. Provides a unified interface for interacting with LightRAG API through the MCP protocol.

A local-first RAG system that indexes personal documents and enables querying them via an MCP tool, keeping data private with local embeddings.

Enables personal knowledge management through Claude Desktop, allowing users to capture thoughts, connect ideas, and reflect on thinking changes via natural conversation.

Enables MCP clients to discover and fetch skills, agents, commands, and hooks from the Gen-e2 Lab Registry, and check local inventory compliance against the registry.

Enables MCP clients to search a Zotero library, read items, take notes, organize collections, and perform semantic search over PDFs.

A lean, local knowledge graph that joins a repo's code to its aSPARK delivery artifacts, enabling agents to trace user stories to code and assess impact of changes, served over MCP.

MCP server that syncs Markdown docs from a GitHub repository and provides search, reading, and listing capabilities for AI agents.

Enables context optimization for LLM-powered coding assistants by compressing prompts, injecting task protocols, and providing a 2nd Brain knowledge graph with tools for prompt compression, knowledge retrieval, metrics, and runtime configuration.

Enables AI assistants to persistently add, read, and summarize sticky notes through MCP-compliant tools, resources, and prompts.

A Compact Knowledge Graph MCP server providing pre-structured domain knowledge as a routing layer for agent stacks, enabling efficient structural queries (e.g., prerequisites, dependency chains) without hallucinations.