Context Harness by Parallax Labs is an MCP server tool designed to function as a structured memory layer for AI tools. It ingests data from various external knowledge sources, including filesystems, Git repositories, and S3 buckets, into a local SQLite store. This local-first context ingestion and retrieval framework enables AI tools to access and search stored information efficiently. The platform exposes its capabilities via a command-line interface and an MCP-compatible HTTP server, supporting popular AI clients like Cursor and Claude, alongside other MCP-compatible agents. It aims to empower developers building advanced AI applications.

What does Context Harness connect to?

Context Harness integrates with diverse external knowledge sources to build its local context store. It draws information directly from filesystems, Git repositories, and S3 buckets, making it versatile for developers. Additionally, it can ingest data from Lua scripts, expanding its adaptability for custom data processing. All ingested data is then consolidated into a local SQLite database, serving as a unified memory layer for connected AI tools and agents. This approach centralizes disparate information for efficient retrieval.

Core features and tools

The tool primarily offers its functionality through the ctx command-line interface and an MCP-compatible HTTP server. This server enables AI clients such as Cursor and Claude to search and retrieve ingested context. Context Harness supports Connector-driven ingestion for various sources and Extension Registries, allowing for community contributions of connectors, tools, and agents. The specific callable tools exposed by the MCP server provide search and retrieval capabilities for the stored context.

Who is it for?

Context Harness targets developers building with AI and AI agents. Its design benefits those creating applications that require a local, structured memory layer for AI interactions. It is suitable for engineers needing to provide AI agents with specific documentation or runbook access. Developers integrating custom Lua tools for dynamic agent capabilities will also find it valuable. Skip this if your project does not involve AI agents or requires direct, real-time access to live external APIs without local caching.

How to get started

Access is granted through external measures, as the platform's authentication model is not natively described. It utilizes MCP Streamable HTTP endpoint and REST endpoints for data transport.

Use cases

• Enabling offline-first Retrieval-Augmented Generation (RAG) for web applications by pre-building a knowledge base and shipping the SQLite database as a static asset.
• Providing AI agents like Cursor and Claude with context by pointing Context Harness at documentation and runbooks via its MCP server.
• Facilitating Lua-extensible agent tools, allowing agents to dynamically discover and call custom tools written in Lua for tasks such as RAG-enriched Jira tickets or Slack posts.

Context Harness offers a valuable local-first framework for managing AI context by ingesting diverse data into a SQLite store. It supports various AI clients through its MCP server, acting as a structured memory layer for agents. The tool enables offline RAG and dynamic Lua-extensible agent capabilities. It serves as a foundational component for developers integrating structured knowledge into AI workflows. Try building an offline-first RAG web application by pre-building a knowledge base with Context Harness.