Reference
AI Context Glossary
Plain-English definitions for the key concepts in AI context management, MCP, and the Wire platform.
AI Agent
An autonomous software program that uses a large language model to plan and execute multi-step tasks.
Unlike a simple chatbot that responds to single prompts, an AI agent can take actions like browsing the web, writing files, and calling APIs to complete a goal. Agents need access to relevant context to do their work effectively, which is why context management systems like Wire exist.
Context as a Service
A model for delivering structured, AI-optimized context to agents and tools on demand.
Instead of embedding context in every prompt or storing it in siloed tools, Context as a Service externalizes it into portable containers. AI agents query the service at runtime, getting exactly the context they need. Wire is a Context as a Service platform.
Context Container
A portable, shareable unit of organized context (documents, data, and structured information) made accessible to AI agents through auto-generated tools.
A context container in Wire holds your files and structured data, automatically processes them into AI-optimized format, and exposes them via auto-generated MCP tools. Containers are private by default, can be shared with teams, and work across any MCP-compatible AI tool.
Context Engineering
The practice of deliberately designing, structuring, and managing the information provided to AI models to improve output quality and relevance.
Context engineering goes beyond prompt engineering by focusing on what information an AI system has access to, not just how instructions are phrased. It involves decisions about what to include and what to leave out, how to structure it, when to retrieve it, and how to keep it current. Wire automates much of the mechanical work of context engineering.
Context Portability
The ability to use the same context across multiple AI tools and applications without re-uploading or re-configuring.
Context portability means your research notes, company documents, or project files aren't locked into a single AI tool. With Wire, you create a container once and connect it to Claude, Cursor, Cline, or any MCP-compatible tool. Your context follows you.
Context Rot
The gradual degradation of an AI system's usefulness as the context it relies on becomes stale, incomplete, or outdated.
Context rot happens when the information you've provided to an AI tool no longer reflects reality: a product has changed, a team member left, a process was updated. Wire addresses context rot by making it easy to update containers and keep AI context current.
Context Window
The maximum amount of text (measured in tokens) that a language model can process in a single inference call.
Every LLM has a finite context window. Fitting all relevant information into this window is a core challenge in AI development, especially for long documents or large knowledge bases. Techniques like RAG and structured context containers help work around this limitation by retrieving only what's needed.
MCP (Model Context Protocol)
An open protocol that standardizes how AI applications provide context and tools to language models.
MCP, developed by Anthropic, defines a standard way for AI tools (like Claude Desktop or Cursor) to connect to external data sources and capabilities. Instead of every AI tool building custom integrations, MCP provides a common interface. Wire generates MCP servers for every context container automatically.
MCP Server
A server that implements the Model Context Protocol, exposing data and tools to MCP-compatible AI clients.
An MCP server is what AI tools like Claude Desktop connect to in order to access external context. Wire automatically creates an MCP server for each context container, with custom tools tailored to your data. Your AI tools can browse, search, and query your context without any manual configuration.
RAG (Retrieval-Augmented Generation)
A technique that retrieves relevant documents or data at inference time and injects them into the model's context window before generating a response.
RAG is commonly used to give LLMs access to knowledge bases that are too large to fit in the context window. Wire uses semantic search and structured retrieval to implement RAG automatically. When an agent queries a container, Wire retrieves the most relevant context and returns it.
Semantic Search
A search method that finds results based on meaning and intent rather than exact keyword matching.
Semantic search uses vector embeddings to represent text as mathematical points in space, allowing it to find conceptually related content even when the exact words differ. Wire includes semantic search on all context containers, so agents can find relevant information using natural language queries.
Wire
A Context as a Service platform that lets you create composable context containers accessible to AI agents through MCP.
You create containers, upload files or let agents write entries directly, and Wire processes everything into AI-optimized context with auto-generated MCP tools. Teams use Wire to share context across all their AI tools without duplication or configuration overhead.
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