Arcade

arc-code is a minimal harness for testing whether a general coding agent can learn unfamiliar ARC-AGI-3 games during play. It runs a headless coding agent with a shell, filesystem, and action command inside an internet-isolated sandbox; the harness provides no solver, planner, world model, grid tooling, fine-tuning, MCP servers, subagents, plugins, or hooks. The agent interacts with each game only through `act.py`, which executes actions and appends resulting boards and actions to a log. It uses the shell and approved file tools to parse that log, record findings, and build task-specific machinery such as parsers, rule models, simulators, and search procedures, which are discarded when the game ends. `run.py` launches and records one session per game, while the `rig/` directory supplies sandboxing, brokering, auditing, scoring, and export infrastructure. The repository includes the harness and six complete session records.

mcpc is Apify's open-source universal command-line client for the Model Context Protocol (MCP). It maps MCP operations for tools, prompts, resources, asynchronous tasks, skills, notifications, and logging to shell commands, communicating with local stdio servers and remote Streamable HTTP servers. Persistent named sessions keep connections, caches, notifications, and authentication state available across commands. The CLI supports progressive tool discovery through grep and compact tool listings, interactive shells, JSON output for jq, xargs, and shell pipelines, schema validation for scripts and CI, OAuth 2.1 with PKCE and bearer authentication, and a local proxy mode that keeps upstream credentials and OAuth tokens outside AI sandboxes. Its single configuration and credential store can be reused by shell-based AI coding agents, with credentials stored in the operating system keychain. It is distributed as the `@apify/mcpc` npm package.

Context Mode is an MCP server and plugin for AI coding agents that reduces context-window usage by routing large tool outputs through sandboxed subprocesses. Its execution tools run code in supported languages, process files, fetch and analyze URLs, and return selected stdout or search results instead of exposing raw logs, snapshots, API responses, or file contents to the conversation. The project reports up to 98% context reduction in its benchmarks. It also provides session continuity across supported agents. Hooks capture tool calls, edits, prompts, decisions, errors, and other session events in SQLite; before compaction, the system builds a prioritized resume snapshot, and after compaction or session resumption it retrieves relevant events through SQLite FTS5 search with BM25 ranking. Content indexing uses heading-aware chunking, stemming, trigram matching, reciprocal-rank fusion, proximity reranking, fuzzy correction, and smart snippets. The MCP interface includes execution, batching, indexing, search, fetching, statistics, diagnostics, upgrade, and purge tools. Context Mode supports multiple coding-agent platforms through MCP servers, native plugins, and platform-specific hooks, with automatic routing enforcement where hooks are available and instruction files for platforms without them. The repository states that processing and SQLite storage are local, with no account or telemetry requirement. It is licensed under the Elastic License 2.0, which permits use, modification, and distribution but restricts offering the software as a hosted or managed service and removing licensing notices.

Model Context Protocol (MCP) is an open, standardized protocol layer for connecting large language models and AI agents with external data sources, hosted infrastructure tools, and other contextual data. It defines formats, metadata, protocol schemas, and APIs for sharing information and tools, attaching, referencing, and validating context such as documents, embeddings, and provenance, while supporting scoped authentication and permissions. MCP translates JSON requests from an agent into calls to service APIs, including CRM, container-management, and GKE capabilities, and can provide design-system context and related tools for generating consistent applications. The official project publishes its specification, documentation, and protocol schema; the schema is defined first in TypeScript and also provided as JSON Schema for broader compatibility. The protocol was created by David Soria Parra and Justin Spahr-Summers, is hosted at modelcontextprotocol.io, and is licensed under the MIT License.

MCP-Memory is a Model Context Protocol server that gives AI coding agents persistent memory across chat turns and sessions. It stores memory records as human-readable Markdown documents following the Open Knowledge Format (OKF v0.2), with YAML frontmatter, hierarchical index files, and an update log, while maintaining a local SQLite FTS5 index for key lookups, keyword search, tag filtering, and namespace-scoped retrieval. The server exposes MCP tools for storing, retrieving, searching, and deleting memories, and supports project, user, and default namespaces; a setup wizard configures supported MCP clients including Claude Desktop, Cursor, Antigravity, Windsurf, and Codex.