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ContextsBase

ContextsBase is an MCP-connected workspace that organizes features, schemas, business rules, and tests for AI coding agents to build and test software in sequence.

ContextsBase

Agent-native backlog workspace

ContextsBase is an agent-native project workspace for software backlogs and specifications. It replaces loose markdown notes with structured features, data models, business rules, and test cases that an AI coding agent can retrieve through the Model Context Protocol (MCP).

A typical workflow is to organize features into an iteration, open it to agents, and send an implementation command. The agent claims the work, builds each feature, and tests it in order, reducing the need to repaste project context at the start of every coding session. The site presents the product for MCP-compatible agents including Claude Code, Cursor, Copilot, and Windsurf.

Workspace and agent execution features

Feature specifications

Capture features in plain language so an agent has a defined backlog item to implement.

Business rules

Record the logic that tests should assert, including examples such as plan or account rules.

Data models

Define entities and fields for the application data model, giving agents structured schema context instead of requiring them to infer it.

Test cases

Write given-when-then test cases that agents can turn into implementation tests and run as part of feature work.

Autonomous iterations

Group features into iterations and open an iteration to agents, which can claim, build, and test its features sequentially.

Design and page context

Store design tokens and page-edit instructions alongside the backlog so agents can apply interface changes with the project context.

Practical use cases

  • Execute a planned iteration

    Turn a multi-feature release into an iteration, expose it to an MCP-connected agent, and let the agent work through the feature list in sequence.

  • Provide schema and rule context

    Store entities, fields, and business rules once so an agent can reference the application model while implementing features instead of inferring it from incomplete notes.

  • Translate requirements into tests

    Define acceptance behavior as given-when-then test cases, allowing feature work to be checked against explicit expected outcomes.

  • Coordinate interface updates

    Keep design tokens and page-edit instructions with the project backlog when an agent needs to make interface changes alongside application work.

Pros and Cons

Pros

  • Keeps feature specifications, business rules, data models, and test cases in one structured workspace.
  • Supports iteration-based execution in which agents claim, build, and test features in sequence.
  • Provides MCP access for named coding tools and other MCP clients.
  • Includes project context for design tokens and page edits in addition to backlog items.

Cons

  • The available site content provides limited detail about setup requirements, workflow boundaries, and operational or security features.
  • The paid plan is marked as coming soon, so current paid-tier availability and limits are not established by the source.

FAQ

How is ContextsBase connected to an AI coding agent?

ContextsBase is configured as an MCP server in an MCP client. The site shows a configuration using the ContextsBase HTTP endpoint and a bearer token environment variable, after which compatible agents can access the project workspace.

What is the workflow for implementing an iteration?

You organize features into an iteration, open the iteration to agents, and issue an implementation command through MCP. The agent then claims features, builds them, and tests them in order.

What information can be stored in the workspace?

The workspace stores features and specifications, business rules, data-model entities and fields, test cases, design tokens, and page edits. These provide the context agents use while building.

Which agents and clients are supported?

The site names Claude Code, Cursor, Copilot, and Windsurf, and states that ContextsBase works with any MCP client. The available evidence does not document every client's setup or workflow boundaries.

Quick Facts

Category
Developer Tool
Primary workflow
MCP-based backlog retrieval and iteration execution
Named clients
Claude Code, Cursor, Copilot, and Windsurf
Free offering
Free for one project
Workspace content
Features, rules, data models, tests, design tokens, and page edits
Website
contextsbase.com