Provides comprehensive GitHub project management capabilities including fetching project issues as calendar events, analyzing team workloads, managing task assignments, and accessing project boards through both GraphQL and REST APIs.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@GitHub Calendar MCP Server show me the team workload distribution for next week"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server that provides GitHub project calendar and team management capabilities. This server allows AI assistants like Goose to interact with GitHub project boards, analyze team workloads, and provide scheduling insights.
📅 Calendar Events: Fetch GitHub project issues as calendar events
👥 Team Status: Get current status of all team members
📊 Workload Analysis: Analyze team workload distribution
🎯 Smart Assignment: Find the best team member for new tasks
📋 Personal Schedules: Get individual team member schedules
🔍 Flexible Filtering: Filter by organization, project, dates, and assignees
Related MCP server: GitHub Team Management MCP Server
Clone or create the project directory:
mkdir github-calendar-mcp-server
cd github-calendar-mcp-serverInstall dependencies:
npm installSet up environment variables:
Create a .env file or set environment variables:
export GITHUB_TOKEN=your_github_personal_access_tokenTo create a GitHub token:
Go to GitHub Settings → Developer settings → Personal access tokens
Create a new token with these permissions:
repo (for private repositories)
read:org (for organization data)
read:project (for project boards)
npm startThe server will start and listen for MCP connections on stdio.
Add this configuration to your Goose MCP settings:
{
"mcpServers": {
"github-calendar": {
"command": "node",
"args": ["/full/path/to/github-calendar-mcp-server/index.js"],
"env": {
"GITHUB_TOKEN": "your_github_token_here"
}
}
}
}get_team_statusGet current status of all development team members.
Example prompts:
"What's the current team status?"
"Show me how busy everyone is"
"Get team workload overview"
get_person_scheduleGet schedule for a specific team member.
Parameters:
login (required): GitHub username
days (optional): Number of days to look ahead (default: 7)
Example prompts:
"What's Alice's schedule for the next week?"
"Show me Bob's upcoming work for the next 14 days"
analyze_workloadAnalyze team workload distribution.
Example prompts:
"Analyze the team workload"
"Who has the most/least work?"
"Show me workload distribution"
find_best_assigneeFind the team member with the lightest workload.
Example prompts:
"Who should I assign this new task to?"
"Find the best person for a new assignment"
"Who has the lightest workload?"
get_calendar_eventsGet GitHub project calendar events with filtering options.
Parameters:
org (optional): GitHub organization (default: "squareup")
project (optional): Project number (default: 333)
since (optional): ISO date string to filter from (default: "2025-08-01")
assignee (optional): Filter by GitHub username
Example prompts:
"Show me all calendar events"
"Get events for Alice"
"Show me events since September 2025"
"Get calendar events for the design team project"
The server is pre-configured for:
Organization: squareup
Project: 333
Label Filter: area: devrel-opensource
Date Range: From August 2025 onwards
You can modify these defaults in the index.js file:
const DEFAULT_ORG = 'your-org';
const DEFAULT_PROJECT_NUMBER = 123;
const DEFAULT_LABEL = 'your-label';The server fetches data from:
GitHub Projects v2 API (GraphQL) - Primary source
GitHub Search API - Fallback if GraphQL fails
Issue custom fields - For start/end dates
Issue body parsing - Backup date extraction
Milestone due dates - Additional date source
The server includes comprehensive error handling:
Authentication errors: Clear messages about GitHub token issues
API rate limits: Graceful handling of GitHub API limits
Network issues: Fallback between GraphQL and REST APIs
Missing data: Sensible defaults for incomplete information
You: "What's the team status?"
Goose: "Here's the current team status:
**alice**
- Active Issues: 3
- Upcoming Issues: 1
- Overdue Issues: 0
- Total Workload: 4
**bob**
- Active Issues: 1
- Upcoming Issues: 2
- Overdue Issues: 1
- Total Workload: 4"
You: "Who should I assign a new task to?"
Goose: "**alice** has the lightest workload:
- Current workload: 2 issues
- Active: 2
- Upcoming: 0
- Overdue: 0"
You: "Show me Bob's schedule for next week"
Goose: "# Schedule for bob (Next 7 days)
**Fix login bug** (open)
- Start: Sep 23, 2025
- End: Sep 25, 2025
- URL: https://github.com/org/repo/issues/123""Authentication failed" error:
Verify your GITHUB_TOKEN is set correctly
Check that the token has required permissions
Ensure the token hasn't expired
"Project not found" error:
Verify the organization and project number
Check that your token has access to the project
Ensure the project exists and is accessible
"No events found" error:
Check the date range (default starts from Aug 2025)
Verify the label filter matches your issues
Ensure issues exist with the specified criteria
Server doesn't start:
Run npm install to ensure dependencies are installed
Check Node.js version (requires 16+)
Verify the index.js file has execute permissions
For debugging, you can add console logging by modifying the server code or checking the error output when running the server.
To extend the server:
Add new tools: Modify the setupToolHandlers() method
Add new data sources: Extend the GitHub API integration
Add UI components: Integrate with @mcp-ui/server for interactive interfaces
Add caching: Implement Redis or file-based caching for better performance
MIT License - feel free to modify and distribute as needed.
Analyze team workload distribution and identify who can take on new tasks
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions analysis and identification but fails to describe how the tool behaves: e.g., what data sources it uses, whether it's read-only or has side effects, if it requires specific permissions, or what the output format looks like. For a tool with zero annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It's front-loaded with the core function and avoids redundancy, making it highly concise and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of workload analysis and the lack of annotations or output schema, the description is incomplete. It doesn't explain what the analysis entails, how results are returned, or any behavioral traits. For a tool that likely involves data processing and decision-making, more context is needed to guide the agent effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, meaning there are no parameters to document. The description appropriately doesn't discuss parameters, which aligns with the schema. Since there are no parameters, the baseline is 4, as the description doesn't need to compensate for any gaps in parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: analyzing team workload distribution and identifying capacity for new tasks. It uses specific verbs ('analyze', 'identify') and resources ('team workload distribution', 'who can take on new tasks'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_team_status' or 'find_best_assignee', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'find_best_assignee' and 'get_team_status' that might overlap in functionality, there's no indication of when this analysis is preferred, what prerequisites exist, or any exclusions. This leaves the agent without contextual usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find the team member with the lightest workload for assigning new tasks
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool finds a team member based on workload, but doesn't describe how workload is measured, what data sources are used (e.g., tasks, calendar events), whether it's read-only or has side effects, or what the output format is. For a tool with zero annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core functionality and appropriately sized for a tool with no parameters, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters (simplifying input) but lacks annotations and an output schema, the description is minimally adequate. It explains what the tool does but doesn't cover behavioral aspects like how workload is determined or what the output looks like. For a tool that likely involves data analysis and returns a recommendation, more context would be helpful, but it meets the basic threshold.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameters need documentation. The description doesn't add parameter details, which is appropriate here. Baseline is 4 for 0 parameters, as there's nothing to compensate for, and the description doesn't introduce confusion about inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Find the team member with the lightest workload for assigning new tasks.' It specifies the verb ('find') and resource ('team member'), and indicates the selection criterion ('lightest workload') and intended use ('assigning new tasks'). However, it doesn't explicitly differentiate from sibling tools like 'analyze_workload' or 'get_team_status', which might provide related but different functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for task assignment based on workload, but provides no explicit guidance on when to use this tool versus alternatives like 'analyze_workload' or 'get_team_status'. It lacks context on prerequisites, exclusions, or specific scenarios where this tool is preferred over siblings, leaving the agent to infer usage from the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get GitHub project calendar events with optional filtering
| Name | Required | Description | Default |
|---|---|---|---|
| org | No | GitHub organization name (default: squareup) | squareup |
| project | No | GitHub project number (default: 333) | |
| since | No | ISO date string to filter events from (default: 2025-08-01) | |
| assignee | No | Filter events by assignee GitHub username |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states it's a read operation ('Get'), implying no destructive effects, but doesn't disclose behavioral traits like authentication needs, rate limits, pagination, error handling, or what the return format looks like. For a tool with no annotation coverage, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core purpose and includes essential context about filtering. Every word earns its place, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and 4 parameters, the description is incomplete. It lacks information on return values, error conditions, authentication, or usage context. For a tool with this complexity and no structured support, the description should do more to compensate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all four parameters with descriptions and defaults. The description adds no additional meaning beyond what's in the schema, such as explaining relationships between parameters or filtering logic. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('GitHub project calendar events') with scope ('with optional filtering'). It distinguishes from siblings like 'get_person_schedule' by specifying it's about project events, not personal schedules. However, it doesn't explicitly differentiate from all siblings like 'analyze_workload' or 'get_team_status'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'get_person_schedule' or 'analyze_workload'. The description mentions optional filtering but doesn't specify use cases, prerequisites, or exclusions. This leaves the agent without contextual direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the schedule and upcoming work for a specific team member
| Name | Required | Description | Default |
|---|---|---|---|
| login | Yes | GitHub username of the team member | |
| days | No | Number of days to look ahead (default: 7) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves data ('Get'), implying a read-only operation, but doesn't clarify aspects like authentication needs, rate limits, error handling, or the format of returned data. For a tool with zero annotation coverage, this leaves significant gaps in understanding its operational behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that efficiently conveys the core purpose without unnecessary words. It is front-loaded with the main action and resource, making it easy to parse and understand quickly, with no wasted information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks details on behavioral traits, usage context, and output format. Without annotations or an output schema, the agent must rely on the description alone, which is incomplete for fully informed tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting both parameters ('login' as GitHub username and 'days' with a default). The description adds no additional semantic details beyond what the schema provides, such as explaining what 'schedule and upcoming work' entails or how the 'days' parameter affects the output. This meets the baseline score when schema coverage is high.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('schedule and upcoming work for a specific team member'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_calendar_events' or 'get_team_status', which might also involve scheduling or team-related data, leaving some ambiguity about its unique scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose it over sibling tools like 'analyze_workload' or 'find_best_assignee', nor does it specify any prerequisites or exclusions for usage, leaving the agent to infer context without explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get current status of the development team including active issues, due items, and recent completions for each team member
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes a read operation ('Get') but doesn't cover critical aspects like permissions required, data freshness, rate limits, or response format. This leaves significant gaps for a tool that aggregates team data.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that efficiently conveys the tool's function without redundancy. It front-loads the core action and details the scope and data types concisely, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (aggregating team data) and lack of annotations or output schema, the description is minimally adequate. It specifies what data is retrieved but omits behavioral context and output details, leaving the agent with incomplete information for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately adds no parameter details, focusing instead on the tool's purpose. This meets the baseline for zero-parameter tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get current status') and resources ('development team'), detailing what information is retrieved (active issues, due items, recent completions per team member). It distinguishes itself from siblings by focusing on team-wide status rather than individual schedules or analysis, though it doesn't explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance is provided on when to use this tool versus alternatives like analyze_workload or get_person_schedule. The description implies usage for team status overviews but lacks context on prerequisites, timing, or exclusions, leaving the agent to infer appropriate scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
analyze_workloadfind_best_assigneeget_calendar_eventsget_person_scheduleget_team_statusMost tools have distinct purposes, but analyze_workload and get_team_status overlap in providing team-level insights, which could cause minor confusion. The other tools (find_best_assignee, get_calendar_events, get_person_schedule) are clearly differentiated.
All tool names follow a consistent verb_noun pattern (e.g., analyze_workload, get_calendar_events) with clear, descriptive verbs and nouns. There are no deviations in style or convention.
With 5 tools, the server is well-scoped for managing team workload and schedules in a GitHub context. Each tool serves a specific, non-trivial function, making the count appropriate for the domain.
The toolset covers key aspects of team management (analysis, assignment, event retrieval, schedules, and status), but lacks CRUD operations for calendar events (e.g., create or update events), which is a minor gap agents might need to work around.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
Calendar API for AI agents: events, availability, Google/Microsoft setup, scheduling, and iCal.
GDPR-compliant calendar access for AI assistants. Google, Microsoft 365, Apple & more. EU-hosted.
Task management for people and AI agents, with scoped OAuth access to issues, projects, and docs.
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