S
Scope
Full Feature List

Everything in Scope

45 MCP tools, a 7-step adaptive wizard, AI-powered ticket generation, and seamless integrations with your favorite AI coding tools.

MCP Tools

45 MCP Tools for AI Coding

Every tool your AI needs to understand and build your project autonomously.

Workflow

Manage your development workflow

get_next_ticket

Get next actionable ticket with learnings + git workflow

get_current_ticket

Get ticket currently in progress

complete_ticket

Mark done, log work, capture learnings

update_ticket_status

Change ticket state (backlog → ready → in_progress → done)

create_ticket

Create new ticket with full context

review_milestone

Analyze gaps, suggest missing tickets

Learning & Memory
Key Differentiator

AI remembers patterns, decisions, and gotchas across sessions

save_learning

Capture patterns/decisions (auto-embeds to Qdrant)

get_relevant_learnings

Surface relevant learnings for current ticket

get_project_patterns

Retrieve all project knowledge

backfill_learnings_to_vector

Migrate existing learnings to Qdrant

get_change_history

View requirement evolution

Context

Access project context and specifications

get_implementation_context

Full context: entities, tech stack, patterns

get_relevant_context

Semantic search with token budget

get_project_context

Complete project + milestones/tickets (~5000 tokens)

get_project_summary

Lightweight context (~200 tokens)

Wizard Data

Access wizard-captured specifications

get_wizard_context

Complete wizard state

get_requirements

Requirements + project type

get_entities

All entities + relationships

get_entity

Single entity with full schema

get_user_flows

User journeys with steps

get_pages

Frontend pages + routes

get_api_design

API endpoints + auth

get_tech_stack

Technology choices

get_schema

Database schema

Project Management

Manage projects, milestones, and tickets

list_projects

List all user projects

list_milestones

View project phases

list_tickets

Filter by status

log_work

Record completed work

get_recent_work

View work history

get_work_summary

Aggregated summary

Dependencies & Blockers

Manage ticket dependencies and blockers

check_dependencies

Verify ticket prerequisites

mark_blocked

Mark ticket as blocked

resolve_blocker

Unblock a ticket

get_blockers

View blocking issues

validate_dependencies

Detect cycles, validate refs

get_dependents

Get tickets depending on a ticket

analyze_ticket_impact

Find related by entity/endpoint/file

Cascading Changes

Preview and apply change impacts

generate_change_set

Preview impact of ticket changes

get_change_set

Get pending change proposals

update_change_approvals

Approve/reject individual changes

apply_change_set

Apply approved changes atomically

Premium: Codebase Analysis
Pro

Analyze existing codebases (Pro plan)

analyze_codebase_structure

Detect tech stack

extract_entities_from_code

Extract data models

extract_api_endpoints

Extract routes

extract_frontend_pages

Discover UI pages

infer_user_flows

Infer navigation

generate_analysis_summary

Full wizard-format context

generate_feature_ticket

AI-generated ticket

generate_bugfix_ticket

Context-aware bugfix

sync_codebase

Drift detection

get_sync_status

Check sync compliance

Example: What Your AI Receives

get_next_ticket() response:

{
  "ticket": {
    "title": "Create users API endpoint",
    "files_to_create": ["/src/routes/users.ts"],
    "entity_schema": { "name": "User", ... }
  },
  "relevant_learnings": [
    "Use Zod for request validation",
    "Return 201 for created resources"
  ]
}

complete_ticket() response:

{
  "status": "completed",
  "learnings_saved": 2,
  "next_action": {
    "type": "git",
    "command": "git push && gh pr create"
  },
  "next_ticket_available": true
}
Project Wizard

7-Step Adaptive Wizard

Define your entire project through AI-guided conversation. The wizard adapts to your project type.

01

Requirements

  • Problem statement capture
  • Target audience definition
  • MoSCoW prioritization (Must/Should/Could Have)
  • Technical constraints
  • AI-guided conversation
02

Entities

  • Data model definition
  • Typed fields (string, number, boolean, uuid, etc.)
  • Primary/foreign key detection
  • Relationship mapping (1:1, 1:M, M:M)
  • Auto-generated ERD visualization
03

User Flows

  • User journey mapping
  • Step-by-step flow definition
  • Branching/decision points
  • Start → Action → Decision → End
  • Visual flow diagram
04

Pages

  • Frontend page definition
  • Route/path specification
  • Component requirements
  • Auth requirements per page
  • Navigation structure
05

API Design

  • RESTful endpoint definition
  • HTTP method specification
  • Path parameters
  • Auth requirements
  • Request/response hints
06

Tech Stack

  • AI-recommended technologies
  • Backend framework selection
  • Frontend framework selection
  • Database selection
  • Testing framework preferences
  • Containerization options
07

Review

  • Full specification review
  • Edit any previous step
  • Generate milestones & tickets
  • Export to AI coding tools

Adaptive Flow by Project Type

Full Stack

All 7 steps

Backend Only

Skip Pages

Frontend Only

Skip API Design

CLI/Library

Requirements → Entities → Tech → Review

Mobile

Requirements → Entities → Flows → Pages → Tech → Review

AI Ticket Generation

Implementation-Ready Tickets

Claude generates tickets that AI coding tools can execute autonomously.

Constitutional AI

5 principles ensure tickets are autonomous, complete, testable, ordered, and actionable

32-Field Tickets

Every ticket includes file paths, entity schemas, migration SQL, test requirements, and more

Git Workflow

Branch names, PR titles, and auto-merge suggestions built into every ticket

Acceptance Criteria

Clear, verifiable criteria for each ticket so AI knows when it's done

Dependency Ordering

Tickets are ordered respecting dependencies - migrations before API, API before frontend

Verification Commands

Shell commands to verify ticket completion: run tests, check endpoints, validate schema

Vector Search

Semantic Context Search

All wizard data is embedded in Qdrant for intelligent retrieval within token budgets.

Embedded Content Types

entity_schema
api_endpoint
page_definition
user_flow
tech_stack
requirement
learning

How It Works

Voyage AI Embeddings

voyage-3-lite model, 1024 dimensions

Qdrant Vector Database

Project-isolated, filtered searches

Token-Budgeted Retrieval

Returns context within LLM limits

Integrations

Connects to Your Tools

Works with any MCP-compatible AI tool, plus GitHub and Stripe.

AI Coding Tools

Cursor

Configure MCP in Cursor settings

Claude Code

Add to claude_desktop_config.json

Lovable

MCP-compatible AI builder

Codex

OpenAI's coding assistant

Any MCP Client

Open standard protocol

GitHub

  • OAuth authentication
  • Repository connection
  • File sync (100KB limit, 500 files max)
  • Webhook support
  • Codebase analysis (Pro)

Billing

  • Stripe integration
  • 7-day free trial
  • Pro plan: $20/month
  • Customer portal
  • Usage tracking

Ready to Try Scope?

Start capturing context in minutes. Free plan includes unlimited projects.

Start FreeHow It Works