This article is published in English.
Tools vs Skills vs MCP: Three Layers of an AI Agent
Tools expose actions, skills encode workflows, and MCP standardizes external connections. A clear mental model for designing agent architectures without mixing the layers.
Understanding the Building Blocks of AI Agents
The agent landscape keeps shifting underfoot.
Earlier debates mostly covered prompt craft, foundation models, and retrieval pipelines. Current designs expect agents to hit APIs, drive workflows, query data stores, operate on codebases, and talk to SaaS.
Three labels dominate those designs:
Tools. Skills. MCP.
Related labels, distinct jobs.
Clear boundaries make architectures simpler to draw and to debate.
The simple mental model
A compact comparison helps before the details:
| Concept | Primary purpose | Simple question |
| ---------- | ------------------------------------------------ | ----------------------------------------- |
| **Tools** | Give the agent capabilities/access | *What can the agent access or do?* |
| **Skills** | Give the agent instructions/workflows | *How should the agent perform a task?* |
| **MCP** | Standardize connections to external capabilities | *How can the agent connect to a service?* |
Or, shorter still:
Tools unlock operations.Skills steer the playbook.MCP bridges outside systems.
The rest of this piece unpacks each layer.
- What are Tools?
A tool is an operation surface the agent may call to change something or read something. Text generation alone is not enough; when work needs a side effect, the model selects a tool.
Picture a coding assistant with operations such as:
readFile()
writeFile()
runTests()
searchCode()
runCommand()
The model may conclude:
I need to inspect this file.
Then it invokes:
readFile("lib/features/login/login.dart")
The tool runs and returns its result to the model.
Tools can connect to internal systems
Inside a company, tools might surface:
- Private HTTP services
- Data stores
- Build and release pipelines
- Ticket trackers such as Jira
- Source-control hosts
- Observability stacks
- Ship and rollout platforms
- Knowledge bases
- Homegrown business apps
Examples:
getEmployeeDetails()
createJiraTicket()
triggerBuild()
checkDeploymentStatus()
queryCustomer()
The important part
Homegrown tools usually mean you keep the integration.
Expect to own:
- Implementation
- Authentication
- Authorization
- Security
- Error handling
- Monitoring
- Maintenance
- Versioning
Tools are powerful, and they also create ongoing engineering load.
2. What are Skills?
Skills answer a different question.
A Skill is coaching: it shows the agent a concrete procedure or workflow.
Think reusable runbooks rather than raw APIs.
Suppose one Skill is named:
releaseFlutterApp
It might encode steps like:
1. Check the current version.
2. Verify the changelog.
3. Run unit tests.
4. Run static analysis.
5. Build the release artifact.
6. Upload to the testing environment.
7. Verify the deployment.
8. Generate the release summary.
The Skill need not supply the raw capabilities. It tells the agent how to combine available capabilities toward a goal. That split matters.
Put differently: a Tool advertises an available operation; a Skill spells out the preferred sequence for finishing a job.
3. Skills aren’t integrations
Confusion often starts here.
Suppose an agent already has:
runCommand()
readFile()
writeFile()
searchCode()
Those entries are capabilities.
Now attach a Skill:
Flutter Release Workflow
That Skill might direct the agent to:
read project configuration
↓
run tests
↓
run analyzer
↓
build application
↓
verify artifact
↓
prepare release
Skills hold orchestration know-how.
They encode the runbook.
In shorthand:
Tool = what can run
Skill = how to sequence it
4. What is MCP?
The third layer is MCP (Model Context Protocol).
It standardizes how AI applications reach external systems and capability servers.
Instead of one-off adapters per product pair, MCP publishes a shared protocol for offering capabilities to clients.
A simplified layout looks like:
AI Application
│
│ MCP
▼
MCP Server
/ | \
/ | \
▼ ▼ ▼
GitHub DB Jira
The AI app speaks to an MCP server; that server surfaces capabilities from an outside service.
An MCP server might expose surfaces related to:
GitHub
PostgreSQL
Slack
Jira
Google Drive
Internal APIs
Exact surface area depends on the server.
- Why MCP matters
Absent a shared protocol, teams tended to hand-roll adapters from each AI app to each SaaS surface.
That pattern grows into:
AI Agent
│
├── Custom GitHub integration
├── Custom Jira integration
├── Custom Slack integration
├── Custom Database integration
└── Custom Internal API integration
More services means more bespoke glue.
A common protocol supplies one communication model.
Conceptually:
AI Client
│
MCP
│
┌─────────┴─────────┐
│ │
MCP Server MCP Server
│ │
GitHub Database
The AI client and the service implementation stay cleaner apart.
6. Tools vs Skills vs MCP
A side-by-side view that stays useful in design reviews:
| | Tools | Skills | MCP |
| ------------------ | -------------------- | ------------------------ | ---------------------------------------- |
| Main purpose | Provide capabilities | Provide procedures | Standardize external connections |
| Focus | **Action** | **Instructions** | **Integration protocol** |
| Answers | "What can I do?" | "How should I do it?" | "How do I connect?" |
| Example | `run_tests()` | Flutter release workflow | GitHub MCP server |
| Usually created by | Developers | Developers/teams | Service/integration providers |
| Maintenance | You may own it | You own the instructions | Often handled by the MCP server/provider |
Real systems blur edges, yet the mental model still helps when shaping agents.
7. A practical example: AI-powered Flutter development
Ground the model with a Flutter team assistant.
The desired user ask might be:
“Prepare the app for the next QA release.”
The agent might expose several Tools:
read_file()
search_code()
run_flutter_test()
run_flutter_analyze()
build_android()
upload_to_firebase()
Then define a Skill:
Flutter QA Release
The Skill could instruct:
1. Check the current branch.
2. Read pubspec.yaml.
3. Determine the current version.
4. Run flutter analyze.
5. Run tests.
6. Build the QA APK.
7. Upload the APK.
8. Verify the upload.
9. Generate a release summary.
An MCP connection could then reach Git hosting, tickets, a data store, or whichever MCP server the team publishes.
The resulting shape might be:
AI Agent
│
┌────────────┼────────────┐
│ │ │
Skills Tools MCP
│ │ │
▼ ▼ ▼
QA Release Flutter CLI External
Workflow Build/Test Services
The agent stops being a Q&A bot.
It can interpret a goal, obey a runbook, exercise local tools, and touch external systems.
That stack is where agentic product work starts to feel real.
8. Another way to remember the difference
Picture onboarding a new engineer.
Tools are their equipment
Laptop
Terminal
Git
Database
CI/CD
APIs
Equipment enables actions.
Skills are their knowledge
How to release an app
How to debug a production issue
How to investigate a crash
How to review Flutter code
How to troubleshoot CI/CD
Knowledge explains how to perform the work.
MCP is the standardized connection layer
It is the consistent channel for the AI application to reach external systems that publish MCP capabilities.
9. Why this distinction matters for developers
Builders who conflate the three layers invent accidental complexity.
A cleaner loop:
Step 1 — Identify the capability
Ask:
“What does the agent need to be able to do?”
That answer is a candidate Tool.
Step 2 — Identify the workflow
Ask:
“How should the agent accomplish the task?”
That answer is a candidate Skill.
Step 3 — Identify external systems
Ask:
“Does this require access to an external service?”
If yes, an MCP-based integration may fit.
10. The bigger picture
The common pattern is shifting away from:
Prompt
↓
LLM
↓
Response
toward layouts more like:
┌───────────────┐
│ AI Agent │
└───────┬───────┘
│
┌─────────────┼─────────────┐
│ │ │
Skills Tools MCP
│ │ │
▼ ▼ ▼
Workflows Actions External
Services
Combined, they nudge systems from producing prose toward performing structured work.
For engineering orgs, that is the meaningful change.
Final takeaway
Remember the trio this way: Tools unlock actions, Skills encode playbooks, and MCP standardizes how outside systems attach.
They do not substitute for one another.
A durable design usually stacks them: Skills describe the playbook, Tools perform the steps, and MCP can supply a uniform bridge to third-party systems.
That vocabulary gets more valuable as products leave pure chat and start doing agentic engineering work.