Context

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.

Context Ontology Accelerator is an open-source semantic context layer for AWS and AI agents. It combines knowledge graphs, formal ontologies, rule-based systems, and modern AI in a Scan → Model → Serve workflow: it connects data sources, discovers schemas, enriches metadata, and ingests documents; induces and manages ontologies, defines metrics, and builds a unified semantic graph; then serves context through SPARQL federation using a Virtual Knowledge Graph, knowledge-graph traversal, queries, and MCP tools. The repository includes source-ingestion services for databases and documents, ontology induction and reasoning with HermiT and ELK, an Ontop-based Virtual Knowledge Graph, a context manager for query orchestration, and a React frontend. Access is controlled through namespace isolation and role-based authorization, with namespace-scoped roles and platform-level roles. The implementation uses Python and TypeScript, AWS CDK, Smithy-generated API contracts, and an Nx-managed monorepo; the project is licensed under Apache License 2.0.