"Information on ROO Code" matching MCP servers:

Persistent, graph-based memory for Roo Code via the Model Context Protocol, enabling structured knowledge management with semantic search and context window control.

Enables AI assistants to maintain persistent project context across sessions by storing and retrieving structured information in markdown files organized in a memory bank directory.

Enables Claude Code to search, browse, and read its own past conversation history across all projects using BM25 keyword search.

An MCP server that preserves LLM context by intercepting large data outputs and returning only concise summaries or relevant sections. It enables efficient sandboxed code execution, file processing, and documentation indexing across multiple programming languages and authenticated CLIs.

Context Mode is an MCP server that reduces context window waste by sandboxing data-heavy tools, tracking session state in SQLite, and promoting code-based analysis over raw data reads, achieving up to 98% context savings.

An MCP server that lets Claude Code recall the context of past conversations from any project on demand.

Shared, code-grounded memory for developers and their coding agents. Capture a learning once and the whole team plus every agent recalls it; memory is grounded in your code and stored as git-tracked JSON reviewed in PRs, with citations validated on write and stale memory withheld from recall. Works with any MCP client.

MCP server that indexes a knowledge base of past bugs and fixes, making them searchable via BM25 from Claude Code or any MCP client.

MCP server for reading Claude Code session history from the local filesystem, exposing project and session data to MCP-compatible clients.

Provides a git-aware knowledge base for AI coding agents to store and retrieve memories anchored to code changes, with automatic staleness detection.

An MCP server that enables users to retrieve, filter, and search through Claude Code conversation history stored in local projects. It provides tools for listing projects and sessions, paginating through message history, and searching across conversations with keyword filtering.

Enables keyword search, filtering, and retrieval of Claude Code memory files, reducing context overhead by fetching only relevant memories on demand.

Enables AI assistants to maintain persistent understanding of code projects across chat sessions via a SQLite brain that caches structural maps, summaries, and decisions. Provides tools for querying project structure, searching code, and recording insights to reduce token usage and avoid re-analysis.

Provides persistent, searchable memory for AI agents, enabling them to retain, recall, and reflect on information across conversations.

Enables storing and querying structured information about software code entities (classes, functions, files) and their relationships (calls, imports) along with qualitative observations like design decisions and change rationale.

Knowledge graph for token-efficient code reviews. Builds a structural map of your codebase with Tree-sitter, tracks changes incrementally, and gives AI agents precise context via MCP tools. Features fixed multi-word search, qualified call resolution, dual-mode embedding (ONNX local + LiteLLM cloud), and output pagination.

An MCP server that provides semantic memory on top of Qdrant vector search, with tools to store and retrieve information using embeddings.

A custom Memory MCP Server that acts as a cache for Infrastructure-as-Code information, allowing users to store, summarize, and manage notes with a custom URI scheme and simple resource handling.

MCP Memory Agent Server - A VS Code chatmode and instruction manager with library integration

Enables storing and retrieving text passages based on semantic meaning using local embeddings (Ollama) and vector storage (ChromaDB), allowing conversational memorization and retrieval of information.