Your AI coding assistant does not become powerful because you connected more tools.
It becomes powerful when those tools are connected to the right workflow, with the right permissions, and enough context to make useful decisions.
That distinction matters as AI moves beyond generating code and starts interacting with repositories, databases, documentation, APIs, CI systems, and other development infrastructure.
This is where MCP and agent plugins become genuinely useful.
But there is a problem: developers can easily turn integrations into a shopping list.
GitHub. Database. Browser. Slack. Documentation. Another MCP server. Another plugin.
More connections can look impressive while making the actual system harder to control.
The goal should not be maximum connectivity.
It should be useful connectivity.
MCP is a connection layer
The Model Context Protocol (MCP) provides a standardized way for AI applications to interact with external tools and data.
An MCP server can expose capabilities such as repository access, documentation retrieval, database operations, APIs, or other development tools.
The important separation is:
The model reasons. The tool performs the operation.
Instead of copying information from five different systems into a prompt, the AI application can interact with those systems through defined interfaces.
That makes tool use more structured and reusable.
But MCP itself does not magically create an intelligent developer agent.
The workflow around those tools still matters.
Plugins solve a different problem
MCP and plugins should not be treated as the same thing.
MCP is primarily a protocol for connecting AI applications with capabilities.
A plugin can package a broader capability or workflow, potentially combining skills, instructions, and MCP-based tools into something easier to distribute and reuse.
Think about a developer task such as:
«“Investigate why the production deployment failed.”»
That is rarely a single-tool operation.
The agent may need repository information, CI status, logs, recent commits, changed files, documentation, and issue history.
The useful capability comes from combining these pieces into a workflow.
Stop asking “Which tools should I install?”
This is probably the biggest mindset shift.
Don't start with:
«“Which MCP servers are available?”»
Start with:
«“Which developer workflow am I trying to improve?”»
For example:
CI debugging
GitHub → failed workflow → logs → recent commits → changed files → project context
Database investigation
Database → schema → relevant tables → query → result analysis
Documentation-aware development
Repository → official documentation → API reference → implementation → tests
Release preparation
Git → issues → pull requests → CI → changelog → release
Now every integration has a reason to exist.
What “using MCP like a pro” actually looks like
Imagine asking:
«“Why did my latest CI pipeline fail?”»
A basic AI workflow might ask you to paste the error.
A connected workflow can inspect the failed run, identify the failing job, retrieve relevant logs, inspect the latest commit, examine changed files, and compare the failure with the existing test structure.
The agent can then explain the likely root cause and propose the smallest safe fix.
If a modification is required, it should not automatically change production code simply because it has access to the repository.
It should propose the change, show the impact, run appropriate validation, and request approval when the action has meaningful consequences.
Automation should not mean unrestricted autonomy.
More tools can make agents worse
There is a common assumption that an agent becomes better as you give it more tools.
That is not necessarily true.
If an agent has access to 30 tools but needs only three for a specific task, the remaining capabilities add decision complexity and potentially irrelevant context.
There is also a security problem.
A tool that reads documentation is fundamentally different from one that can modify infrastructure or execute destructive database operations.
A production system should therefore use:
Least privilege
Give each workflow only the permissions it actually needs.
Read before write
Inspect and understand the system before modifying it.
Approval gates
Require appropriate confirmation for high-impact actions.
Traceability
Record important tool calls so developers can understand what the system did.
Build around workflows
A mature developer platform might eventually support dozens of integrations.
The developer should not have to manually manage all of them for every task.
Instead, the platform can activate capabilities according to the workflow.
Choose:
Debug CI
and expose the tools needed for CI investigation.
Choose:
Investigate a database issue
and expose database-related capabilities.
Choose:
Prepare a release
and activate the relevant Git, issue, CI, and documentation capabilities.
This creates a much cleaner architecture:
Intent → Workflow → Required capabilities → Context → Action → Verification
That is far more useful than:
User → giant toolbox → hope the agent chooses correctly
Treat every integration as a security boundary
Before connecting a new MCP server or plugin, ask:
- What data can it access?
- What actions can it perform?
- Does it really need write permission?
- What happens if the model chooses it incorrectly?
- Can its actions be audited?
- Can access be revoked?
- What happens when the external service fails?
The question is not:
«“Can my AI agent access this?”»
The better question is:
«“Should my AI agent access this, for this workflow, with this level of permission?”»
The bigger shift
MCP and plugins are interesting because they move AI development systems beyond isolated chat interfaces.
The next generation of developer tools will increasingly combine:
Models + context + tools + memory + verification + human control.
MCP can provide an important connection layer.
Plugins can make capabilities easier to package and reuse.
But neither automatically creates a reliable AI developer.
The difficult engineering work is designing the workflow around them.
Connect fewer capabilities.
Give them clear responsibilities.
Control their permissions.
Verify their outputs.
Measure whether they actually reduce developer effort.
The real question is no longer whether AI can use your tools.
If your AI coding agent could access every tool in your development stack tomorrow, which capability would you trust it to use autonomously—and which one would you still require it to ask permission for?
