GitHub MCP Server Gets an Upgrade: Now Supports GitHub Projects and Comes with a Plethora of Enhancements

The world of software development is changing fast. Developers are no longer just writing code. They're working alongside AI assistants that understand context, automate repetitive tasks, and help manage entire projects. But here's the thing: getting these AI tools to talk to your development environment has always been a bit messy.
That's where Model Context Protocol comes in. Think of it as a universal translator between AI models and the tools developers use every day. GitHub just made this connection stronger with their October 2024 update to the GitHub MCP Server. The update brings GitHub Projects support and several refinements that make the whole system run better.
This isn't just about adding another feature. It's about making AI assistants genuinely useful for project management, not just code generation. When your AI can see your project boards, understand your sprint planning, and help organize your backlog, the possibilities expand dramatically.
We're going to walk through what changed, why it matters, and how you can start using these new capabilities. Whether you're a solo developer or managing a team of fifty, these updates have something for you.

1. GitHub MCP Server Gets an Upgrade:
Now Supports GitHub Projects and
Comes with a Plethora of Enhancements

A New Chapter in AI-Assisted Development
The world of software development is changing fast. Developers are no longer just writing
code. They're working alongside AI assistants that understand context, automate repetitive
tasks, and help manage entire projects. But here's the thing: getting these AI tools to talk to
your development environment has always been a bit messy.
That's where Model Context Protocol comes in. Think of it as a universal translator between
AI models and the tools developers use every day. GitHub just made this connection
stronger with their October 2024 update to the GitHub MCP Server. The update brings
GitHub Projects support and several refinements that make the whole system run better.
This isn't just about adding another feature. It's about making AI assistants genuinely useful
for project management, not just code generation. When your AI can see your project
boards, understand your sprint planning, and help organize your backlog, the possibilities
expand dramatically.
We're going to walk through what changed, why it matters, and how you can start using
these new capabilities. Whether you're a solo developer or managing a team of fifty, these
updates have something for you.

2. What Exactly Is Model Context Protocol?
Before we get into the new features, let's talk about what MCP actually does. Anthropic
created this protocol to solve a specific problem: AI models are powerful, but they need a
standardized way to connect with external data sources and tools.
You might have heard of Language Server Protocol (LSP). It standardized how code editors
communicate with language tools, giving us features like autocomplete and inline error
checking across different editors. MCP does something similar for AI assistants.
The protocol creates a bridge. On one side, you have AI models that can reason and
generate responses. On the other side, you have your actual work: repositories, issues, pull
requests, CI/CD pipelines, security alerts. MCP lets these two worlds talk to each other
properly.
How GitHub MCP Server Fits In
GitHub's MCP Server is their official implementation of this protocol. They built it in
collaboration with Anthropic, initially developing it in Go. The server acts as a middleman
that understands both GitHub's API and the MCP specification.
You can run it two ways: as a remote server hosted by GitHub, or locally on your own
machine using Docker. Both options have their place depending on your security
requirements and customization needs.
The server came out in public preview earlier this year and has been gaining traction among
developers who want their AI assistants to do more than just suggest code completions.
What Could It Do Before?
Even before this update, the GitHub MCP Server was pretty capable. It could automate
issue creation and updates. It understood pull requests and could help with code reviews. It
connected with CI/CD workflows to give AI assistants context about build failures. It even
tapped into GitHub's security features to help track vulnerabilities.
But project management? That was missing. You could work with individual issues and PRs,
but organizing them into projects, moving them through workflow stages, and managing
backlogs at a higher level wasn't possible.
GitHub Projects Integration Changes Everything
GitHub Projects has become the go-to project management tool for teams building on
GitHub. It's more than just a fancy issue tracker. You get Kanban boards, custom fields,
automated workflows, and views that span multiple repositories.
The new projects toolset brings all of this into the MCP ecosystem. Your AI assistant can
now interact with projects just like it interacts with code and issues.

3. What You Can Do With Projects Toolset
The functionality covers the full range of project management tasks. You can list all projects
in your organization or repository. You can retrieve specific project details including all items,
fields, and their current status.
Need to update an item? The AI can move issues from "To do" to "In progress" based on
context. Working on sprint planning? It can add and remove issues from projects based on
your conversation.
Here's a real scenario: You're discussing a feature with your AI assistant. It understands
you're working on authentication improvements. It can check which authentication-related
issues exist, see which ones are already in your current sprint project, and suggest adding
overlooked items. You approve, and it makes the changes.
Why It's Not Enabled By Default
GitHub made a smart choice here. The projects toolset is opt-in. You need to explicitly
enable it in your configuration.
Why? Because not everyone uses GitHub Projects. Some teams use external project
management tools. Others work on small projects where formal project management is
overkill. Loading unnecessary toolsets would just slow things down and give the AI more
options to choose from, potentially making tool selection less accurate.
To enable it locally, you'll modify your GitHub MCP Server configuration file to include
"projects" in the toolsets list, then rebuild and restart the server. It's straightforward once you
know it's there.
Practical Uses That Make Sense
Sprint management becomes less manual. During sprint planning meetings, your AI
assistant can help populate the sprint board by understanding which issues relate to your
current goals. It can suggest items based on dependencies, priority, and team capacity.
Backlog grooming gets easier. The AI can categorize issues, identify which ones haven't
been assigned to any project, and suggest organization schemes based on themes it
identifies in your issue descriptions.
Cross-repository coordination is where things get interesting. Large projects often span
multiple repositories. GitHub Projects can include items from different repos, and now your
AI assistant can help manage these complex, multi-repo projects without you manually
jumping between repositories.
Technical Details That Matter
The implementation uses GitHub's GraphQL API for project operations. This means you
need a Personal Access Token with appropriate permissions. The token needs project read
and write access, along with repository access to the repos you want to include.

4. Security is built in through repository allowlisting. You can specify exactly which repositories
the MCP Server is allowed to access. This prevents accidental or malicious actions on
repositories you didn't intend to expose.
Leaner Default Configuration Improves Performance
The second major change addresses a problem that crops up with any tool that grows over
time: bloat. The GitHub MCP Server was getting heavy. It included dozens of toolsets by
default, most of which most people never used.
Why Less Is More
When an AI assistant has too many tools available, a few things happen. First, it takes
longer to decide which tool to use for any given task. Second, the context window fills up
with tool definitions, leaving less room for actual conversation and reasoning. Third,
performance suffers because the system is tracking and managing capabilities it rarely
needs.
GitHub analyzed usage patterns and found that five toolset groups covered the vast majority
of workflows. So they changed the default configuration to include only those five.
The New Default Toolsets
Context: This provides information about the current repository, branch, and working
environment. Your AI needs this to understand where it's operating.
Repos: Repository operations like listing files, reading file contents, and understanding
repository structure. This is fundamental for any code-related task.
Issues: Creating, reading, updating, and closing issues. Since issue management is central
to most workflows, this stays in.
Pull Requests: Everything related to PRs, from creation to review to merging. Again, this is
too central to leave out.
Users: Understanding who's working on what, checking user profiles, and managing
team-related queries.
That's it. No code security toolset by default. No experimental features. No actions toolset.
Just the core capabilities most people use most of the time.
How This Helps
Tool selection gets more accurate. When the AI has fewer options, it's less likely to pick the
wrong tool or get confused about which tool handles a particular task.
Response times improve. Less context means faster processing. The AI can focus on
reasoning about your actual request rather than parsing through dozens of tool definitions.

5. Resource consumption drops. Both memory and processing power requirements decrease
when you're not loading unnecessary functionality.
Getting The Other Tools Back
Nothing disappeared. The other toolsets still exist. You just need to explicitly request them.
There are several ways to do this. You can modify your server configuration file to add
specific toolsets. You can use the
--toolsets flag when starting the server to specify
exactly which toolset groups you want. You can set the
GITHUB_TOOLSETS environment
variable for persistent configuration across restarts.
For Docker deployments, you'd pass the toolset specification as part of your container
configuration. The documentation explains each method clearly.
Finding Your Balance
Most developers should start with the default configuration and see if anything is missing
from their workflow. If you work heavily with GitHub Actions, you'll want to add the actions
toolset. If security scanning is part of your daily routine, add code_security.
Teams should coordinate their configuration choices. If half your team has one set of toolsets
and the other half has different ones, you'll have inconsistent experiences when sharing
MCP configurations or troubleshooting.
Pull Request Tools Got Simpler
The third major change tackles a specific case of tool sprawl: pull requests. Before this
update, the GitHub MCP Server had six separate tools just for reading different aspects of a
pull request.
The Problem With Too Many Specialized Tools
When you had get_pull_request, get_pull_request_files,
get_pull_request_status, get_pull_request_diff,
get_pull_request_reviews, and get_pull_request_review_comments all sitting
there as separate tools, the AI had to decide which one to use. Often it would need
information from multiple tools, requiring several calls and more complex reasoning.
Configuration files got cluttered. If you wanted to allow PR operations, you had to list all six
tools. Documentation became more verbose. Testing became more involved.
Meet pull_request_read
GitHub consolidated all six tools into one: pull_request_read. This single tool handles all
read operations for pull requests through a
method parameter.

6. Need basic PR info? Call pull_request_read with method="get". Want to see the files
changed? Same tool, method="get_files". Checking CI status? method="get_status".
Looking at the diff? method="get_diff". Reading reviews or review comments?
method="get_reviews" or method="get_review_comments".
Same functionality, cleaner interface. The AI now thinks "I need PR information" and reaches
for one tool instead of six.
The Benefits Show Up Everywhere
AI reasoning becomes clearer. When you look at logs showing how the AI decided to
accomplish a task, you see simpler chains of thought. It doesn't deliberate between six
similar-sounding tools.
Configuration files shrink. You list pull_request_read once instead of six separate tools.
Your config becomes easier to read and maintain.
Performance improves slightly. The AI spends less time on tool selection and more time on
actual reasoning about your request.
Labels Got The Same Treatment
Pull requests weren't the only toolset to receive consolidation. The label toolset now follows
a similar pattern with
get_label, list_label, and label_write (which uses method
parameters for create, update, and delete operations).
This pattern will likely expand to other toolsets in future updates as GitHub refines the server
based on usage patterns and feedback.
Migrating Is Straightforward
If you had custom configurations referencing the old tools, you'll need to update them. Check
your config files for the old tool names and replace them with the consolidated versions.
The good news is that all the functionality still exists. You're not losing capabilities, just
changing how they're accessed. Test your workflows after making the changes to ensure
everything still works as expected.
Setting Up and Configuring Your Server
Getting the GitHub MCP Server running isn't complicated, but there are some details worth
understanding.
Installation Requirements
You'll need Docker installed if you're going the container route, which most people do. You'll
also need a GitHub Personal Access Token with the right permissions. The token needs
repository access at minimum, plus any additional scopes for the toolsets you plan to use.

7. If you prefer building from source, you can clone the repository and build it with Go. This
gives you more control but adds complexity.
Understanding Toolset Categories
Toolsets group related functionality. The repos toolset handles repository operations. The
issues toolset covers issue management. The pull_requests toolset deals with PRs.
Then you have code_security for security scanning and alerts. Actions covers CI/CD
workflows. Experiments contains features that are still being tested and refined.
And now, projects handles project management operations.
You mix and match these based on what you actually need. A frontend developer might want
repos, issues, and pull_requests. A security engineer might add code_security. A project
manager would definitely want projects.
IDE Integration Options
VS Code has native support. If you're using GitHub Copilot in VS Code, the MCP Server can
enhance what Copilot sees and understands about your repository.
Claude Desktop and Claude Code work well with the MCP Server. This combination is
particularly powerful since Anthropic designed the protocol specifically for Claude's
architecture.
JetBrains IDEs and Visual Studio support is available through community plugins and
extensions. The MCP ecosystem is growing fast, so check for the latest integration options.
Remote vs Local: Making The Choice
The remote server option means GitHub hosts it for you. Setup is simpler. You don't manage
infrastructure. Updates happen automatically. This works great for individuals and small
teams who want to get started fast.
Local server deployment gives you complete control. You can customize toolsets, adjust rate
limits, and ensure all data stays within your network. Larger organizations with strict security
policies often prefer this route.
Both options support the same features. Your choice depends on your requirements for
control, security, and convenience.
Security Configuration
Your Personal Access Token is the key to everything. Protect it like a password. Use the
minimum scopes necessary for your workflows. Don't give the token unnecessary
permissions.

8. Repository-level permissions matter. Even if your token has broad access, you can configure
the MCP Server to only access specific repositories through allowlisting.
Organizational security policies might restrict what you can do. Check with your security
team before deploying MCP Servers across your organization. They'll want to understand
what data the server can access and how it's protected.
Real Applications In Daily Development
Enough theory. How do people actually use this stuff?
Project Management Workflows
A team lead starts their day by asking their AI assistant for a status update on the current
sprint. The AI queries the sprint project, checks which issues are marked as "In progress,"
and reports on blockers based on recent comments.
During standup, someone mentions they finished a task but forgot to update the project
board. The team lead asks the AI to move that issue to "Done." Takes five seconds.
Sprint planning becomes a conversation. "Which high-priority authentication issues aren't in
any sprint yet?" The AI lists them. "Add the top three to next sprint." Done.
Code Review Assistance
A developer opens a pull request. Their AI assistant automatically checks if the PR has any
files that frequently cause problems (based on history of reverts and bug fixes). It notices a
particular file and suggests being extra careful with testing.
The assistant can scan recent review comments across multiple PRs to identify common
feedback patterns. "You've been asked to add more error handling in three PRs this month.
Want me to check this PR for similar issues?"
When CI fails, the AI can correlate the failure with recent changes in dependencies or other
PRs that touched the same code paths. "This test also failed in PR #847 yesterday. The fix
there might apply here."
Security and Compliance
A security engineer asks to see all open security alerts across their repositories. The AI
compiles the list, groups them by severity, and highlights which ones have been open
longest.
"Are there any critical vulnerabilities in dependencies we're using for the authentication
service?" The AI can answer this by combining code security data with repository file
analysis.

9. Dependabot creates a lot of update PRs. The AI can help triage them by checking which
updates fix actual vulnerabilities versus routine maintenance updates.
Team Collaboration
New team member has questions about why certain architectural decisions were made. The
AI can search through closed issues, PR discussions, and project notes to find relevant
conversations and summarize the reasoning.
Cross-functional coordination gets easier. "What are the design team's blockers right now?"
The AI checks the design project, identifies issues marked as blocked, and summarizes
each one.
Documentation stays current. The AI can identify when code changes affect documented
behavior and suggest documentation updates based on the PR description and changed
files.
CI/CD and Release Management
Build failures happen. When they do, the AI can help track which tests are flaking, which
ones consistently fail after certain types of changes, and which PRs introduced breaking
changes.
Release planning becomes less manual. "Which bug fixes have been merged since the last
release?" The AI compiles the list, helping you write changelog entries.
Deployment coordination across services requires tracking multiple repositories and their
states. The AI can monitor deployment projects that span repos and alert you to
inconsistencies or blockers.
Community Response and What's Next
GitHub actively asks for feedback on the MCP Server. They maintain discussion forums
where developers share their experiences, report issues, and suggest improvements.
How Developers Are Responding
Early adopters are finding creative uses beyond what GitHub documented. Some teams use
the project management features to automatically organize technical debt. Others have built
custom workflows that trigger project updates based on code metrics.
Common requests include expanding the projects toolset with more automation options,
adding support for GitHub Discussions, and creating toolsets for GitHub Packages and
Container Registry.
Some developers want finer-grained control over tool permissions. Right now, enabling a
toolset gives the AI full access to those capabilities. There's interest in allowing read-only
access for certain operations.

10. Where MCP Servers Stand
Several other platforms now offer MCP servers. There are servers for Slack, Google Drive,
Postgres databases, and more. GitHub's implementation is one of the most mature and
well-documented.
What makes GitHub's version special is the tight integration with an existing, widely-used
platform. Developers are already on GitHub. The MCP Server enhances what they're
already doing rather than asking them to adopt something new.
The open source nature helps too. Developers can contribute improvements, report bugs
directly, and see exactly how the server works.
Future Possibilities
GitHub hasn't announced specific plans, but some directions seem likely. Deeper Copilot
integration would make sense. Expansion of the projects toolset with more automation
capabilities. Support for GitHub's newer features as they roll out.
The community is experimenting with chaining multiple MCP servers together. Someone
might use the GitHub MCP Server alongside a database MCP server and a documentation
MCP server to create an AI assistant that understands their entire development ecosystem.
Performance improvements will continue. As more people use the server, GitHub collects
data about which operations are slow and where optimizations would have the biggest
impact.
Contributing Back
The GitHub MCP Server is open source. If you're comfortable with Go, you can contribute
code. Even if you're not, you can create issues for bugs or feature requests.
Good contributions include clear bug reports with reproduction steps, feature requests that
explain the use case and benefit, and documentation improvements for areas you found
confusing.
The team has guidelines for pull requests. Follow their coding standards, write tests for new
features, and update documentation when you change behavior.
Making It Work For Your Team
Reading about features is one thing. Making them work in practice takes some thought.
Configuration Strategy
Start simple. Use the default toolsets for a few weeks. Track what you wish you could do but
can't. That tells you which additional toolsets to enable.

11. Document your configuration choices. When someone new joins the team, they should
understand why you enabled certain toolsets and what each one does.
Version control your MCP Server configuration just like you version control your code. When
something breaks, you can see what changed.
Security Best Practices
Rotate your Personal Access Tokens regularly. Every few months, generate a new token and
update your configuration. This limits the damage if a token ever leaks.
Use separate tokens for different purposes. One token for local development, another for
CI/CD integrations. If you need to revoke one, you don't break everything.
Monitor token usage through GitHub's token activity view. If you see unexpected activity,
investigate immediately.
Implement audit logging for MCP Server operations, especially in organizational
deployments. You want to know who did what and when.
Workflow Design
Teaching your AI assistant to use these tools well requires good prompting. Be specific
about what you want. "Check the status of all issues in the current sprint project" works
better than "how's the sprint going?"
Combine multiple toolsets strategically. One query can involve repository file analysis, issue
checking, and project board updates if you structure the request well.
Handle errors gracefully. The AI will sometimes pick the wrong tool or misunderstand what
you asked. When this happens, rephrase rather than repeating the same request.
Scaling Considerations
Multi-repository management requires careful allowlist configuration. You don't want to
accidentally give the AI access to repositories it shouldn't touch.
Organization-wide deployments need coordination. Decide which toolsets everyone should
have access to, document the setup process, and provide support for team members who
need help.
Watch your rate limits. GitHub's API has limits on requests per hour. Heavy MCP Server
usage can hit these limits. If you do, consider upgrading your GitHub plan or implementing
request caching.
Testing Your Setup

12. After configuring the MCP Server, test each toolset you enabled. Create a checklist: can it
read repository files? Can it create issues? Can it update project boards? Work through the
list systematically.
Integration tests help catch problems before they affect real work. Set up a test repository
and project where you can experiment without worrying about breaking production
workflows.
Monitor effectiveness over time. Are you actually using all the toolsets you enabled? Are
response times acceptable? Is the AI choosing the right tools for tasks? Adjust based on
what you learn.
Why These Updates Matter
The October 2024 enhancements represent more than incremental improvements. They
signal a shift in how we think about AI in development workflows.
Project Management Meets AI
Adding GitHub Projects support bridges a gap between code and coordination. Developers
write code, but projects succeed when teams coordinate effectively. Now your AI assistant
can help with both.
This reduces context switching. You don't need to leave your conversation with the AI to
update a project board. You don't need a separate tool to organize your backlog. It happens
in the flow of work.
For teams, this means shared understanding. When the AI helps organize projects,
everyone benefits from its view of priorities, dependencies, and status.
Performance and Usability Balance
The reduced default configuration shows GitHub thinking carefully about user experience.
More features isn't always better. Sometimes simplification improves usability more than
adding capabilities.
Tool consolidation follows the same principle. Developers don't want to learn six ways to
check pull request data. They want one clear, powerful tool that handles all the cases.
These choices make the system more maintainable over time. Fewer tools mean fewer
things to document, test, and support.
Who Gets The Most Value
Team leads and project managers gain a powerful assistant for organizing work. They can
delegate routine project management tasks and focus on strategic decisions.

13. Individual developers benefit from reduced friction. They can work in one place instead of
jumping between code editor, GitHub web interface, and project boards.
DevOps and security professionals get better visibility. The AI can help them track security
issues, coordinate deployments, and monitor CI/CD pipelines across many repositories.
Getting Started
New users should start with the remote server option. It's the fastest way to see what MCP
can do. Follow GitHub's quick start guide, get your Personal Access Token configured, and
try some basic queries.
Existing users should review the changelog and update their configurations if needed. Check
if any of your workflows relied on the old, separate pull request tools. Test that everything still
works.
Learning resources are available in GitHub's documentation. They have examples, tutorials,
and reference material for all toolsets. The community discussions also contain valuable
real-world examples.
Where AI-Assisted Development Is Heading
The GitHub MCP Server updates fit into a larger trend. Development is becoming more
conversational. Instead of memorizing commands and clicking through interfaces,
developers increasingly describe what they want and let AI figure out the details.
The Role of Standards
Model Context Protocol matters because it creates interoperability. Different AI models can
work with the same servers. Different tools can implement the same protocol. This prevents
vendor lock-in and encourages innovation.
GitHub's adoption of MCP validates the protocol. When major platforms standardize on a
protocol, it gains momentum. Other platforms watch and often follow.
The parallel to Language Server Protocol is intentional. LSP transformed how code editors
work by creating a standard interface. MCP could do the same for AI assistants.
GitHub's Position
GitHub hosts over 100 million developers. Their tools and APIs set expectations for the
industry. When they invest in something like the MCP Server, it influences where the
ecosystem goes.
They're not just implementing someone else's protocol. They're actively contributing to its
development through feedback and collaboration with Anthropic.

14. The open source approach means anyone can benefit, learn from, or improve their
implementation. This accelerates adoption and refinement.
Agent-Native Development
We're moving toward what some call "agent-native" development. Tools aren't just designed
for human use anymore. They're designed to be used by both humans and AI agents.
The MCP Server represents this thinking. It exposes GitHub's functionality in a way that AI
assistants can use naturally. The interface is designed for agents, not just humans clicking
buttons.
This shift will affect how we build all developer tools. APIs will need to be AI-friendly.
Documentation will need to explain not just how to use features, but how to help AI use them
effectively.
As more developers work this way, the patterns and practices will evolve. We'll learn what
works and what doesn't. Early adopters who engage with these tools now will shape how the
next generation of development tools work.
The GitHub MCP Server is part of this evolution. The October updates make it more
practical, more performant, and more capable. That matters for anyone building software
today and thinking about how they'll build software tomorrow.
Learn more about the GitHub MCP Server updates and start experimenting with these
capabilities in your own workflows.
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