scheduler-mcp 1.1.0

MCP server for AVC course scheduling and registration analysis with web scraping, prerequisite validation, and schedule optimization

MCP Scheduling Tool

A comprehensive course planning and schedule generation system built as an MCP (Model Context Protocol) server. This tool helps students search courses, validate prerequisites, detect schedule conflicts, generate optimal schedules, and estimate workload.

Features

Course Discovery

• Search Courses: Find courses by keyword, department, or course ID
• Course Information: Get detailed course info including prerequisites, units, and workload
• Section Listing: View available sections with meeting times (when data available)

Schedule Planning

• Conflict Detection: Identify time overlaps between selected courses
• Schedule Generation: Generate all valid schedule combinations
• Smart Recommendations: Get the best schedule based on your preferences (balanced, compact, no mornings)

Academic Planning

• Prerequisite Validation: Verify you meet requirements for requested courses
• Corequisite Checking: Ensure corequisites are included in your schedule
• Multi-Semester Planning: Validate long-term course plans across multiple semesters

Workload Management

• Workload Estimation: Calculate weekly hours including lecture, lab, and study time
• Deadline Clusters: Predict busy weeks with overlapping deadlines
• Heavy Semester Detection: Get warnings about overloaded schedules

Installation

Using UV (Recommended)

# Install from PyPI (when published)
uv pip install scheduler-mcp

# Or install from source
git clone <repository-url>
cd canvasMCP
uv pip install -e .

Using pip

pip install scheduler-mcp

Usage

As an MCP Server

The tool runs as an MCP server using stdio transport, making it compatible with AI assistants and MCP clients.

# Run the server
scheduler-mcp

# Or use with uvx
uvx scheduler-mcp

Testing with MCP Inspector

npx @modelcontextprotocol/inspector uv run scheduler-mcp

Available Tools

Course Discovery

search_courses(query, semester="fall_2026")

# Search for courses
search_courses("computer science")
search_courses("MATH")

get_course_info(course_id, semester="fall_2026")

# Get detailed course information
get_course_info("acct111")

Schedule Planning

detect_schedule_conflicts(course_ids)

# Check for time conflicts
detect_schedule_conflicts(["cs120", "math150", "acct111"])

generate_possible_schedules(requested_courses, max_units=18)

# Generate all valid schedules
generate_possible_schedules(["cs120", "math150", "engl101"], max_units=15)

suggest_best_schedule(requested_courses, preference="balanced", max_units=18)

# Get the best schedule recommendation
suggest_best_schedule(
    ["cs120", "math150", "acct111"],
    preference="no_mornings",
    max_units=18
)

Preferences:

• balanced: Evenly distributed workload
• compact: Dense schedule with minimal gaps
• no_mornings: Avoid early morning classes

Academic Validation

validate_prerequisites(completed_courses, requested_courses)

# Check if prerequisites are satisfied
validate_prerequisites(
    completed_courses=["acct111"],
    requested_courses=["acct113"]
)

validate_course_plan(course_plan)

# Validate multi-semester plan
validate_course_plan([
    ["cs101", "math140"],  # Semester 1
    ["cs120", "math150"],  # Semester 2
    ["cs220", "cs240"]     # Semester 3
])

Workload Analysis

estimate_semester_workload(course_ids)

# Estimate weekly workload
estimate_semester_workload(["cs120", "math150", "phys201"])

detect_deadline_clusters(course_ids)

# Predict busy weeks
detect_deadline_clusters(["cs120", "math150", "chem101"])

Example Workflow

# 1. Search for courses
courses = search_courses("accounting")

# 2. Get detailed info
course_info = get_course_info("acct111")

# 3. Validate prerequisites
validation = validate_prerequisites(
    completed_courses=["math101"],
    requested_courses=["acct111", "acct113"]
)

# 4. Generate schedules
schedules = generate_possible_schedules(
    ["acct111", "math150", "engl101"],
    max_units=15
)

# 5. Get best schedule
best = suggest_best_schedule(
    ["acct111", "math150", "engl101"],
    preference="balanced"
)

# 6. Estimate workload
workload = estimate_semester_workload(["acct111", "math150", "engl101"])

# 7. Check for deadline clusters
clusters = detect_deadline_clusters(["acct111", "math150", "engl101"])

Data Format

The tool expects course data in JSON format with the following structure:

{
  "course_id": "acct111",
  "title": "Bookkeeping",
  "course_title": "ACCT 111",
  "units": "3.0 Units",
  "description": "Course description...",
  "prereqs": ["ACCT 101"],
  "coreqs": [],
  "advisory": [],
  "min_units": "3.0",
  "max_units": "3.0",
  "contact_hours": "54.0",
  "out_of_class_hours": "108.0",
  "lecture_hours": "54.0",
  "lab_hours": "0.0",
  "department": "accounting"
}

Place course data in src/scheduler_mcp/data/courses.json or src/scheduler_mcp/data/courses_<semester>.json.

Development

Project Structure

canvasMCP/
├── src/
│   └── scheduler_mcp/
│       ├── run.py              # Main entry point with MCP tools
│       ├── database.py         # Course data loading and queries
│       ├── schueduling.py      # Scheduling algorithms
│       ├── workload.py         # Workload estimation
│       ├── data/               # Course data directory
│       │   ├── courses.json    # Default course database
│       │   └── courses_*.json  # Semester-specific databases
│       ├── tools/
│       │   └── courses.py      # Tool implementations
│       └── tests/
│           ├── testConflicts.py
│           ├── testPrereqs.py
│           └── testSchueduler.py
├── pyproject.toml             # Package configuration
└── README.md

Running Tests

# Install dev dependencies
uv pip install pytest

# Run tests
pytest src/scheduler_mcp/tests/

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Run the test suite
6. Submit a pull request

Architecture

The MCP Scheduling Tool follows a layered architecture:

1. MCP Layer (run.py): Exposes tools via FastMCP decorators
2. Tool Layer (tools/courses.py): Thin wrappers that delegate to core logic
3. Logic Layer (schueduling.py, workload.py): Core algorithms and business logic
4. Data Layer (database.py): Course data loading and queries

This separation ensures:

• Clean, testable code
• Easy maintenance
• Clear responsibilities
• Reusable components

Limitations

• Section/meeting time data not yet available in current dataset
• Conflict detection requires section data to be fully functional
• Schedule generation currently simplified without section combinations

Future Enhancements

• Add section/meeting time data support
• Implement full conflict detection with time overlaps
• Add Canvas API integration for real-time data
• Support for multiple institutions
• Web scraping for automatic data updates
• Visual schedule generation
• Export to calendar formats (iCal, Google Calendar)

MIT License

MIT License

Copyright (c) 2026 Elijah Sayres

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Contact

[e-mail]