Many people either don’t care or know about the importance of context for LLMs and use the AI tools with vague/short queries like:

“Hey, build me the X tool” or “Generate a long-form article on Global Warming”.

This was me at some point and I got frustrated by the generic-slop responses I get from the models. However, the issue extends beyond the prompt, I needed a more systematic solution.

So I began developing the AI capabilities for my agency's management tool, Overbooked.

After hours of testing and iteration, I figured the output quality you can get with useful data plus agent tools is unmatched and I wanna share how OP the workflow gets.

There are two sides to the coin:

Using the MCP Server inside the IDE (Cursor for me)

You can connect Overbooked to modern IDEs like Cursor super easily with just this config:

{
  "mcpServers": {
    "overbooked": {
      "url": "https://www.overbooked.app/api/mcp"
    }
  }
}

Your Cursor agent now has access to over 15 valuable tools. Here are some real-life examples of how users, including myself, are utilizing them:

1. Juggling between related but separate repositories

I have many repositories that I actively maintain and Cursor has context only for the currently open project. This is fine but sometimes those projects are related (e.g. a NextJS frontend app coupled with a separate backend repo). When this is the case, you can either spend hours trying to put them into a monorepo or use the Overbooked MCP server like:

“Create a resource in project Alpha including the required changes we need to apply on the server repository.” and on the server repo:

“We need to do some changes on the X route, get the Required Changes resource and start refactoring.“

and just like that you preserved the context of both projects.

2. Delegating tasks and preventing hallucinations

You want to add a new feature to your app so you open Cursor and it starts implementing it. But it can get ahead of itself quite often, generating unnecessary code or losing track of the task. Instead, try this approach:

“We will implement X feature using existing modules whenever possible. Please create the necessary tasks and update their statuses as you go.”

The plan is now clear and accessible for the LLM. It also reduces the amount of hallucinations significantly.

3. Initializing and structuring new projects

Overbooked provides a variety of valuable tools for generating essential project documents, such as PRDs, tech stacks, and core features, as well as creating branded assets like color palettes, typography styles, and logos in seconds. You can always export them from the app, but they are also accessible via the MCP server for convenience. Try these:
“Initialize a Nextjs app based on the documents of project X.“

“Pull the styles of project X and update the global.css to include the colors and typography styles.”

Using the Chat Window inside the app (Overbooked)

All MCP tools are accessible within the project dashboard, and some of them are more effective when used in the app's chat window. Some examples:

1. Brainstorming in a group chat

Each project in Overbooked includes a dedicated group chat tab. This is very useful for brainstorming purposes because anyone on the team can tag Overbooked at any time to ask questions, gather information from the internet or plan actionable steps to move the project forward.

2. Generate long-form articles or social media posts

Overbooked can use project context to produce well-crafted long-form articles and social media posts. Below is a screenshot demonstrating Overbooked conducting research before generating the article:

3. Talk to your calendar

You can even talk to your calendar and let Overbooked create reminders for you.

Inventory — tools available to Overbooked AI

The AI assistant can execute the following built-in tools (functions):

get_asset_url — retrieve public URLs for generated assets (images) using a storageId.
get_team_members — list team members and search for assignee IDs.
get_projects — list projects available to the team.
get_project_tasks — fetch tasks for a specific project.
get_project_milestones — view milestones for a project.
get_project_blocks — access project resource blocks.
get_project_block_with_resources — fetch a specific block and its resources.
create_task — create a new task with title, description, priority, status, label, and assignee.
update_task — modify task details (title, description, priority, status, label, assignee).
update_task_status — quickly change a task's status (todo, in-progress, done).
get_folders — list resource folders and find folder slugs.
get_folder_with_resources — browse resources inside a folder.
create_prototype — create a new prototype resource for a project.
get_project_prototypes — list prototypes for a project.
update_prototype_status — change a prototype's status.
create_resource — add a new document or link resource into a folder.
update_resource — update an existing resource's content, title, tags, or URL.
publish_resource / unpublish_resource — make resources public or private.
read_repo — read files from a linked GitHub repository.
get_project_context — fetch contextual project metadata for smarter suggestions.
load_older_messages — retrieve older chat messages from the project chat history.
generate_image — generate images (returns storageIds) for hero banners, UI mockups, infographics, etc.

Here are some workflows that can be run with these tools:

Thread summarizer
Action extractor → task creator
Proposal generator (contextual)
Follow-up email builder
Objection response snippets
Timeline & milestone planner
Meeting notes + decisions log
Image/OG generator
Copy variants & tone rewrites
File search & context lookup
Code/spec assistant
Localization / translation
Case study/outcomes blurb generator
RFP / presales answer starter
Billing & invoice draft helper
Plus: persona prompts, permission-aware checks, and audit export.

Comparison: Overbooked vs. competitors

The table below compares how each platform treats collaboration, context, and delivery. The goal: show where a project‑native AI chat changes outcomes.

Feature	Overbooked	Tidbit	ChatGPT	Slack	Jira
Project context access	Reads full project history, files, milestones, and client messages.	Long‑context channels; chat-focused (may require manual file links).	Session-based; needs integrations to access private project data.	Channel-based; context limited to messages and attached files.	Task-first; context in tickets, not in freeform chat.
Action extraction → tasks	One-click conversion with assignee, estimate, due date.	Manual or semi-automated extraction in chat.	Possible via plugins/scripts; not native.	Apps can add this; not native by default.	Native task management but not from conversational threads.
Deliverable generation (proposals, timelines)	Built-in prompts that generate client-ready outputs using project data.	Chat-centered drafting; may need external tools for final formatting.	Strong generation capabilities; context must be supplied per session.	Plugins can help with generation; not project-aware by default.	Template-based docs; generation is not chat-native.
Client portal / audit sync	Publish chat outcomes to client portal with audit trail.	Not native (focus on internal collaboration).	Requires custom integration.	Requires separate client-sharing tools.	Clients often need restricted views; not chat-integrated.
Built-in AI tools	15+ tools baked into workflows (summaries, proposals, billing, images).	AI tools focused on chat/long-context collaboration.	Many capabilities via API and plugins; user must configure.	Third-party apps; native AI limited.	Automation focused on workflow rules, not AI-generated content.
Best for	Freelancers and SMBs that need chat + project delivery in one app.	Teams that want only a dedicated long-context collaborative chat experience.	Users who need general-purpose AI assistants and custom integrations.	Team communication and integrations hub.	Issue tracking and engineering workflows.

How to adopt AI group chat without chaos

Start with two channels: projects (project-specific chat) and actions (tasks auto-created from chat).
Enable action extraction for pilot projects and measure time-to-first-task completion.
Train the team: show 2–3 examples where a chat message became a milestone, so adoption feels natural.

Conclusion

If you wanna see it in action check out this demo we have prepared:

Using this daily has boosted our productivity, lowered stress levels, and provided us with more free time. I invite you to explore it and share your thoughts; any feedback is welcome!