AI Context API: Give Assistants Current Project Information

An AI context API is an endpoint that returns the current state of a project — rules, open tasks, decisions and where things were left — so an assistant can read it at the start of a session instead of asking you to re-explain the work. The key design question is what to include: too little and the assistant still asks; too much and the useful signal is buried. Tools like fenbs provide this over MCP (the Model Context Protocol, a standard way for an AI app to call an outside tool), with the context structured around rules first, then tasks, lessons and progress.

What an AI context API is meant to do

In a normal AI session, you spend the first few messages re-establishing what the project is, what was decided and where it stands. A context API gives the assistant a structured read of that state before it asks you anything. The assistant calls the endpoint, reads the response and starts from an informed position.

This is different from a search endpoint (which returns documents matching a query) or a retrieval-augmented generation setup (which chunks and embeds text for similarity search). A context API returns a structured, curated summary of the current state of specific work: what rules apply now, which tasks are in progress, and what was last agreed. It is designed for the start-of-session read, not for mid-session document lookup.

For related ideas on what gets stale and why, see AI context rot. For the question of how large a context window an assistant can use, see AI context window.

What to include in the context response

A useful context response includes the things that change the next action. Structure them in priority order, with the most binding information first.

• Standing rules and decisions: instructions that apply to every session on this project. These come first so the assistant reads them before anything else.
• Current tasks: what is in progress, what is blocked and what is waiting on a person. Include the task reference, a short description and its current state.
• Where we left off: a plain-language summary of where the last session ended and what the next session should start with.
• Lessons learned: the most relevant lessons from past sessions that affect how similar work should be approached.
• Glossary: key terms and their project-specific meanings, so the assistant uses the same vocabulary as the team.

Leave out raw chat history, large source documents and anything that describes a past state rather than the current one. A stale summary is worse than no summary: the assistant will act on the wrong state. Keep the response under a few thousand tokens so it fits comfortably at the start of a session without consuming the entire context window.

Design principles for a scoped context endpoint

Scope the response to a specific project or scope of work. An endpoint that returns everything for a team of ten is not useful to an assistant working on one feature. The caller should be able to specify the project reference and receive only the context for that scope.

Attribute every item. Each rule, task and lesson should say who created it and when it was last updated. An assistant that knows a rule was set last week by the project lead treats it differently from one that has no idea how old it is or who agreed it.

Return current state, not history. The endpoint should reflect what is true now. If a decision was superseded, return the current decision and a link to the old one rather than both in full. For the broader pattern of giving assistants the right information at the right granularity, see context engineering for AI agents.

Authentication and access control

A context API needs authentication. The context it returns describes live work, decisions and potentially sensitive project state. Bearer token authentication with scoped access is the standard approach: the client authenticates once, and the token determines which projects and which operations are available.

fenbs uses OAuth 2.1 with PKCE for browser-based sign-in: the user signs in, approves the connection and the AI app receives a token. That token is tied to one person's identity and role on each board, so an assistant acting through that token cannot exceed what that person is permitted to do. There is no API key to paste into a config file.

Keeping the context current

A context API is only as useful as the record behind it. If nobody updates Where we left off when a task changes state, the next assistant reads stale information and starts from the wrong position. The discipline of saving progress before a session ends is the main maintenance requirement.

That discipline is easier when saving progress is part of the natural end of a session rather than a separate administrative task. Ask the assistant to update the tasks it touched and save Where we left off before you close the chat. For related patterns in managing what context an assistant carries across tool boundaries, see AI context layer tools.

How to do this with fenbs

1. Create a free account and add a project for the work you want to supply as context.
2. Write the rules, decisions and current tasks that belong in the context. For rules, use Decisions and rules and mark the important ones as standing rules.
3. Connect a supported AI assistant over MCP from Integrations. When you start a session, ask the assistant to read the project context before it does anything else.
4. Before you end a session, ask the assistant to save progress on the tasks it touched and update Where we left off. That keeps the context current for the next session.
5. Review the context for a project at the start of each week. Remove stale tasks, update decisions that have changed and add lessons from the previous week.

fenbs does not train a model or read your chat history. It keeps the structured work record that you and your connected assistants maintain together. If you are comparing tools that provide project context to assistants, see AI context layer tools compared for how different approaches compare.