ContextsBase

Product Introduction

1. Definition: ContextsBase is a specialized context infrastructure platform designed for AI-powered coding agents. It operates as a centralized, persistent memory system that serves project specifications, business rules, data models, and test cases to agents via the Model Context Protocol (MCP).
2. Core Value Proposition: It exists to eliminate the "context reset" problem in AI-assisted development, where agents like Claude Code, Cursor, or GitHub Copilot start each session with no prior project knowledge. ContextsBase provides a single source of truth, ensuring agents build consistently from the correct specifications without manual copy-pasting, thereby accelerating development velocity and reducing errors.

Main Features

1. Centralized Context Repository: This is the core database for all project knowledge. Users write and store feature specifications (F-1, F-2), business logic rules (R-2), entity-relationship data models (User, Session), and test cases (T-7, T-8) in one structured location. How it works: The platform organizes this information into queryable, versioned artifacts that are served on-demand.
2. MCP (Model Context Protocol) Server Integration: This is the delivery mechanism. ContextsBase exposes its repository via a standards-based HTTP MCP server. How it works: Developers configure their compatible IDE or agent (e.g., Cursor, Windsurf) with a simple .mcp.json config block containing an API endpoint and authentication token. The agent then pulls relevant context at the start of every session.
3. Automated Context-Aware Development Workflow: The platform enables a structured, iterative build process. How it works: Developers define "iterations" (e.g., I-1 Accounts) containing specific features. The coding agent reads the provided context for each feature, builds the code, and runs it against the supplied test cases. ContextsBase can flag code that drifts from updated specs.
4. Unified Design and Page Edit Management: Beyond logic, it manages visual consistency. How it works: It stores design tokens (theme-v3) for colors, typography, and radii, and allows direct specification of page edits (e.g., "change h1"), ensuring the agent's output remains on-brand.

Problems Solved

1. Pain Point: The "Groundhog Day" effect in AI-assisted coding, where developers must repeatedly paste the same project specs, rules, and data models into each new chat session, wasting time and causing inconsistency.
2. Target Audience: Software engineers and development teams using AI coding agents (Claude Code, Cursor, GitHub Copilot, Windsurf) in their daily workflow; tech leads and product managers who need to ensure AI-built features adhere to business rules and design systems.
3. Use Cases: Onboarding a new AI agent or team member to a complex existing project; implementing a multi-feature epic (like user authentication) where consistency across features is critical; maintaining a large codebase where the data model and business logic must be perfectly understood by the AI to avoid destructive changes.

Unique Advantages

1. Differentiation: Unlike simple prompt templates or manually managed documentation, ContextsBase is a dynamic, API-driven infrastructure layer. It's not a chat interface but a backend service for agents. Compared to hoping an agent remembers context from a previous chat, it guarantees context delivery via the standardized MCP.
2. Key Innovation: Its application of the Model Context Protocol as a dedicated "memory server" for coding-specific context. It moves project knowledge from transient chat histories and human memory into a structured, machine-readable, and always-available system, fundamentally changing the agent from a stateless tool to a context-aware team member.

Frequently Asked Questions (FAQ)

1. What is MCP and how does ContextsBase use it? MCP (Model Context Protocol) is an open standard developed by Anthropic for tools to provide context to AI models. ContextsBase implements an MCP server that streams your project's specs, rules, and data models directly to compatible coding agents like Cursor or Claude Code, acting as a permanent memory source.
2. Can I use ContextsBase with GitHub Copilot? Yes, ContextsBase works with any AI coding assistant that supports the Model Context Protocol (MCP). This includes Cursor, Claude Code, Windsurf, and others. You configure the MCP connection in your agent's settings to point to your ContextsBase project.
3. How does ContextsBase handle changes to my specifications? When you update a feature spec or business rule in ContextsBase, the change is immediately available to agents via the MCP server. The platform can also help identify code that may no longer align with the updated context, prompting a review or rebuild.
4. Is my code stored on ContextsBase servers? No, based on the provided information, ContextsBase acts as a context provider, not a code repository. It stores and serves the specifications, rules, and data models that inform how code should be built. The actual code generation and storage happen in your local IDE and version control system (like Git).
5. What does the free plan include? The free forever plan includes one real project, support for up to 100 features, 3 team members, and one MCP token, which is sufficient for individual developers or small teams to integrate context management for a single project with their AI coding agent.