Model Context Protocol (MCP): Zero-to-Production Guide

As large language models become more capable, the challenge shifts from “can they reason?” to “can they act?” The Model Context Protocol (MCP) is Anthropic’s answer to this question—a standardized way for LLMs to discover, understand, and safely use tools, data, and actions in the real world.

Before MCP, every AI application required custom integrations, hard-coded tool definitions, and fragile glue code. MCP changes this by providing a universal protocol that any LLM can use to interact with external systems.

Think of MCP as:

USB-C for AI tools — one standard interface for everything
OpenAPI for LLM reasoning — machine-readable specifications for capabilities
A contract between models and the outside world — clear boundaries and expectations

This guide will take you from understanding the fundamentals to building production-ready MCP servers.

Official Resources:

1. Why MCP Exists (The Real Problem)

Before MCP

Tool use in LLM applications looked like this:

Hard-coded function schemas embedded in prompts
Prompt-stuffed instructions that were brittle and verbose
One-off integrations built for each specific model
Fragile tool calling logic that broke with model updates

The problems this created

1. No discoverability

Models couldn’t learn what tools were available at runtime
Every new tool required updating prompts and code

2. No standard lifecycle

No consistent way to initialize, validate, or shut down tools
Error handling was ad-hoc and inconsistent

3. Tight coupling to specific LLMs

Tools written for GPT-4 didn’t work with Claude
Switching models meant rewriting integrations

4. Unsafe or ambiguous actions

Unclear boundaries between what models could and couldn’t do
No standard way to gate dangerous operations

What MCP fixes

MCP introduces a standardized protocol with:

1. Standard tool discovery

Models can query available tools at runtime
No hard-coded function lists

2. Explicit schemas

Machine-readable tool definitions
Clear input/output specifications
JSON Schema validation

3. Separation of reasoning vs execution

Models decide what to do
MCP servers execute safely
Clean boundary between thought and action

4. Composable, reusable tools

Write once, use with any MCP-compatible model
Tools can be combined and orchestrated
Easy to version and update independently

The result

You write a tool once, and any MCP-aware model can use it.

This is the same revolutionary shift that USB brought to hardware, or that Docker brought to deployment.

2. Core MCP Mental Model

The architecture (three components)

Model  <——>  MCP Client  <——>  MCP Server

This simple architecture has three clear roles:

1. Model (the brain)

Decides what action to take
Chooses which tools to use
Interprets results
Examples: Claude, GPT-4, LLaMA

2. MCP Client (the translator)

Translates between model and server
Manages the protocol conversation
Handles tool discovery
Validates schemas
Examples: Claude Desktop, custom applications

3. MCP Server (the executor)

Exposes tools and resources
Validates inputs
Executes actions safely
Returns structured outputs
Examples: Filesystem server, database server, API server

What MCP is NOT

It’s crucial to understand what MCP doesn’t do:

NOT a framework

MCP doesn’t prescribe how to build your application
It’s a protocol layer, not application scaffolding

NOT an agent system

MCP doesn’t include planning, memory, or decision loops
Those are agent concerns, built on top of MCP

NOT a model API

MCP doesn’t expose model inference
It’s the opposite—it exposes tools TO models

What MCP IS

MCP is a protocol + contract.

Think of it like HTTP:

HTTP doesn’t care what web framework you use
HTTP doesn’t decide what your website does
HTTP just standardizes how browsers and servers communicate

Similarly:

MCP doesn’t care what agent framework you use
MCP doesn’t decide what your tools do
MCP just standardizes how models and tools communicate

3. MCP Building Blocks (Deep Dive)

MCP has three core primitives: Tools, Resources, and Prompts.

3.1 Tools (Actions)

Tools are executable actions that modify state or perform operations.

Examples:

run_sql_query — Execute a database query
call_api — Make an HTTP request
read_file — Read from filesystem
trigger_deploy — Start a CI/CD pipeline
search_vectors — Query a vector database
send_email — Send a notification
create_ticket — Create a Jira issue

Every tool has four required components:

Name — Unique identifier (e.g., get_user_by_id)
Description — Human and LLM-readable explanation
Input schema — JSON Schema defining required/optional parameters
Output schema — Expected return format

Tool schema example:

{
  "name": "get_user_by_id",
  "description": "Fetch user information from the database by their unique ID",
  "inputSchema": {
    "type": "object",
    "properties": {
      "user_id": {
        "type": "string",
        "description": "The unique identifier for the user"
      }
    },
    "required": ["user_id"]
  }
}

Key insight: The model reads the description and schema to understand when and how to use the tool.

3.2 Resources (Context)

Resources are readable context (not actions). They provide information but don’t modify state.

Examples:

Files (code, configs, logs)
Database rows (read-only views)
Documents (PDFs, markdown, wikis)
API responses (cached data)
System status (metrics, health checks)

The distinction:

Tools  → DO things (write, execute, modify)
Resources → INFORM (read, observe, context)

Why the separation?

Safety: Resources can’t cause damage
Performance: Resources can be cached aggressively
Clarity: Models know when they’re just reading vs. acting

Example resource:

{
  "uri": "file:///app/config.yaml",
  "name": "Application Configuration",
  "description": "Current production configuration file",
  "mimeType": "application/x-yaml"
}

3.3 Prompts (Templates)

Prompts are reusable, structured prompt templates that guide model behavior.

Types:

System instructions — Behavior guidelines
Task templates — Pre-built workflows
Tool usage guidance — How to use specific tools
Domain knowledge — Context about the problem space

Think of prompts as:

“A library of pre-written instructions that models can reference”

Example prompt:

{
  "name": "code_review_assistant",
  "description": "Guide for performing thorough code reviews",
  "arguments": [
    {
      "name": "language",
      "description": "Programming language",
      "required": true
    }
  ]
}

Why prompts matter:

Consistency: Same guidelines across sessions
Composability: Mix and match prompt templates
Versioning: Update prompts without changing tools

4. MCP Server Architecture (Production-Ready)

Minimal MCP server structure

/server
 ├── tools/
 │    ├── database.py      # Database operations
 │    ├── github.py        # GitHub API tools
 │    └── search.py        # Search capabilities
 ├── resources/
 │    ├── files.py         # File system resources
 │    └── docs.py          # Documentation resources
 ├── prompts/
 │    └── templates.py     # Reusable prompt templates
 ├── server.ts            # Main server (TypeScript)
 └── server.py            # Main server (Python)

Responsibilities of an MCP server

Every MCP server must handle five core responsibilities:

1. Declare capabilities

Advertise available tools, resources, and prompts
Provide schemas for discovery
Version your API

2. Validate inputs

Check JSON Schema compliance
Validate types, ranges, and constraints
Reject malformed requests early

3. Execute tools

Run the requested action safely
Handle errors gracefully
Implement timeouts

4. Return structured outputs

Always return valid JSON
Include status codes
Provide error details when things fail

5. Enforce safety boundaries

Implement permission checks
Rate limit requests
Audit sensitive operations
Sandbox execution when possible

Transport layers

MCP supports three transport mechanisms:

1. STDIO (Standard Input/Output)

Use case: Local tools, CLI integration
Pros: Simple, low overhead, easy debugging
Cons: Local only, no remote access

2. HTTP

Use case: Remote tools, microservices
Pros: Standard protocol, firewall-friendly, scalable
Cons: Higher latency, stateless

3. WebSockets

Use case: Real-time tools, streaming responses
Pros: Bidirectional, low latency, stateful
Cons: More complex, connection management

Choosing the right transport:

Factor	STDIO	HTTP	WebSockets
Latency	Lowest	Medium	Low
Remote access	No	Yes	Yes
Streaming	Limited	No	Yes
Complexity	Simple	Medium	High
Security	Local only	TLS	TLS + auth

General guidance:

Start with STDIO for prototyping
Use HTTP for production services
Use WebSockets for real-time needs

5. Tool Design Best Practices (Critical)

Building great MCP tools requires following key design principles.

5.1 Atomic tools (single responsibility)

Bad approach:

deploy_and_notify
  ├── Deploy application
  ├── Run health checks
  ├── Send Slack notification
  └── Update status page

Good approach:

deploy_app              # Deploy only
check_health            # Health check only
send_slack_message      # Notify only
update_status_page      # Status update only

Why atomic tools are better:

Composability: Models can chain tools in different orders
Reusability: Each tool serves multiple use cases
Error handling: Failures are isolated and clear
Testing: Easier to test individual components

Key insight: LLMs compose better than they improvise. Give them building blocks, not Swiss Army knives.

5.2 Deterministic outputs (structured data)

Bad: Ambiguous string responses

{
  "result": "User created successfully, ID is 12345"
}

Problems:

Model has to parse human text
No guaranteed structure
Hard to validate
Prone to breaking

Good: Structured JSON responses

{
  "status": "success",
  "data": {
    "user_id": "12345",
    "created_at": "2025-12-28T02:10:00Z"
  },
  "metadata": {
    "execution_time_ms": 45
  }
}

Benefits:

Machine-readable
Strongly typed
Easy to validate
Consistent structure

Always include:

status field (success, error, partial)
data object (the actual result)
error object when applicable (code, message, details)

5.3 Safe defaults (security first)

Principle: Tools should be read-only by default.

Read-only first:

list_files          # Safe
read_file           # Safe
search_database     # Safe (read-only)

Destructive operations marked explicitly:

delete_file         # Dangerous flag
write_database      # Requires confirmation
deploy_to_prod      # Multiple safeguards

Implementation strategies:

1. Permission tiers:

class ToolPermission(Enum):
    READ = "read"           # No confirmation needed
    WRITE = "write"         # Confirmation recommended
    DESTRUCTIVE = "destructive"  # Requires explicit approval

2. Confirmation flags:

{
  "name": "delete_database",
  "dangerous": true,
  "requires_confirmation": true,
  "description": "DESTRUCTIVE: Permanently deletes database"
}

3. Environment gating:

if tool.is_destructive and env == "production":
    require_manual_approval()

4. Rate limiting:

@rate_limit(calls_per_minute=10)
def expensive_operation():
    pass

6. MCP vs Function Calling vs Plugins

How does MCP compare to other tool-use approaches?

Feature	MCP	Function Calling	Plugins
Standardized	Yes	No	No
Discoverable	Yes	No	Partial
Model-agnostic	Yes	No	No
Tool lifecycle	Yes	No	No
Production-safe	Yes	Partial	Partial
Versioning	Yes	Manual	Manual
Security model	Built-in	DIY	Varies
Separation of concerns	Clear	Mixed	Mixed

Function Calling (OpenAI, Anthropic)

What it is:

Model-specific feature for calling functions
Functions defined in prompts or API calls
No standard protocol

Limitations:

Tightly coupled to specific model APIs
No discovery mechanism
Manual schema management
Switching models = rewriting code

When to use:

Simple, single-model applications
Prototyping
When MCP overhead isn’t justified

Plugins (ChatGPT, older systems)

What it is:

Application-specific extensions
Often uses OpenAPI specs
Ecosystem fragmentation

Limitations:

No standard interface
Model-specific implementations
Limited composability
Unclear boundaries

When to use:

Existing plugin ecosystems
When targeting specific platforms

MCP Advantages

MCP is the first tool system designed for scale:

Write once, use everywhere
- Same tools work with Claude, GPT-4, local models
- No model-specific code
Runtime discovery
- Models learn available tools dynamically
- No hard-coded function lists
Clear boundaries
- Separation between reasoning (model) and execution (server)
- Easier to secure and audit
Production-ready
- Built-in versioning
- Transport flexibility
- Security primitives
Ecosystem benefits
- Community can share MCP servers
- Standardized tooling
- Easier testing and debugging

7. Top Most Useful MCP Tools (Real-World)

Here are the highest-value MCP servers to build or use.

7.1 Filesystem MCP

Use cases:

Code analysis and navigation
Configuration file editing
Log file inspection
Documentation browsing

Example tools:

list_directory     # List files in a directory
read_file          # Read file contents
write_file         # Write/update files
search_files       # Search across files
get_file_info      # Get metadata (size, modified, etc.)

Why it’s essential:

Grounds LLMs in reality (actual code, not assumptions)
Enables agent workflows (read, analyze, modify, test)
Foundation for code assistance tools

Security note: Always sandbox file access. Never allow .. path traversal.

7.2 Database MCP (SQL + NoSQL)

Use cases:

Ad-hoc analytics and reporting
Debugging data issues
Data validation and quality checks
Schema exploration

Example tools:

query_database     # Execute SELECT query
get_schema         # Describe table structure
explain_query      # Get query execution plan

Best practices:

Read-only by default — no INSERT/UPDATE/DELETE without explicit opt-in
Parameterized queries only — prevent SQL injection
Query timeouts — prevent runaway queries
Row limits — cap result set sizes

Why it’s high ROI:

Enables self-service analytics
Reduces back-and-forth with engineers
Instant data exploration

7.3 GitHub MCP

Use cases:

Pull request reviews and comments
Issue triage and labeling
Code search across repositories
Repository analysis and metrics

Example tools:

list_pull_requests     # Get PRs for a repo
get_pr_diff            # Get PR changes
comment_on_pr          # Add review comment
search_code            # Search code across repos
get_issue              # Fetch issue details
create_issue           # Create new issue

Why this is one of the highest ROI MCP tools:

Automates repetitive code review tasks
Helps with issue triage at scale
Enables intelligent code search
Foundation for AI-powered development workflows

Implementation tip: Use GitHub’s GraphQL API for efficiency.

7.4 Web / HTTP MCP

Use cases:

API aggregation (call multiple services)
Service monitoring and health checks
SaaS automation (Stripe, Twilio, etc.)
Web scraping (with appropriate permissions)

Example tools:

http_get           # GET request
http_post          # POST request with body
fetch_json_api     # Typed API call
check_endpoint     # Health check

Critical tip:

Wrap APIs instead of exposing raw HTTP

Bad:

http_request(url="https://api.stripe.com/v1/charges", method="POST", ...)

Good:

create_stripe_charge(amount, currency, source)

Why wrapping is better:

Type safety
Business logic validation
Easier to audit
Clear intent

7.5 Search MCP (Web + Internal)

Use cases:

RAG (Retrieval-Augmented Generation) pipelines
Fact checking and verification
Research agents
Internal knowledge base search

Example tools:

search_web            # Web search (Google, Bing)
search_documentation  # Internal docs
search_code           # Code search
semantic_search       # Vector similarity search

Often combined with:

Vector Database MCP (for embeddings)
Document MCP (for document retrieval)
Web MCP (for fetching full content)

Implementation considerations:

Cache results aggressively
Rate limit external APIs
Track citation sources
Consider cost per query

7.6 Vector Database MCP

Use cases:

Semantic search
Long-term memory for agents
Retrieval-Augmented Generation (RAG)
Similar document finding

Example tools:

embed_text          # Create embedding
search_similar      # Find similar vectors
store_embedding     # Save to vector DB
list_collections    # List vector collections

Popular backends:

FAISS — Facebook’s similarity search (local)
Pinecone — Managed vector DB
Weaviate — Open-source vector search
Chroma — Lightweight embeddings database

Why it matters:

Enables memory across conversations
Powers RAG systems
Improves answer accuracy with context

Cost consideration: Embeddings generation can be expensive at scale.

7.7 CI/CD MCP

Use cases:

Triggering builds from natural language
Running test suites on demand
Deployment orchestration
Build failure analysis

Example tools:

trigger_build      # Start CI pipeline
get_build_status   # Check build status
run_tests          # Execute test suite
deploy_to_env      # Deploy to environment
rollback           # Revert deployment

CRITICAL: Requires strict permissioning

Multi-factor auth for prod deployments
Audit logging for all operations
Dry-run mode for testing
Rollback capabilities

Security model:

@require_approval(environments=["production"])
@audit_log
@rate_limit(calls_per_hour=5)
def deploy_to_production(service, version):
    # ... deployment logic

8. MCP + Agents (How They Actually Combine)

Important: MCP does not replace agents.

Instead, they work together:

Agent = Decision logic (planning, reasoning, memory)
MCP   = Execution layer (tools, actions, data)

The typical agent + MCP flow

User Goal
    ↓
┌─────────────────┐
│ Agent Reasoning │  ← Agent framework (LangChain, AutoGPT, custom)
└─────────────────┘
    ↓
Select MCP tool
    ↓
┌─────────────────┐
│  MCP Server     │  ← Execute tool safely
└─────────────────┘
    ↓
Observe result
    ↓
Update state/memory
    ↓
Next action or complete

Example: Research agent

Goal: “Analyze our competitors’ pricing strategies”

Agent reasoning:

“I need to gather competitor information”
→ Uses Search MCP to find competitor websites
→ Uses Web MCP to fetch pricing pages
→ Uses Database MCP to store findings
→ Uses Document MCP to generate report

MCP’s role:

Provides the tools (search, fetch, store, generate)
Ensures safe execution
Returns structured results

Agent’s role:

Decides which tools to use and when
Interprets results
Plans next steps
Maintains context and memory

Key insight

Agents think, MCP acts.

This separation makes both more powerful:

Agents can focus on reasoning
MCP servers can focus on safe execution
Both can evolve independently

9. Common MCP Anti-Patterns

Avoid these mistakes when building MCP tools:

1. One giant “do everything” tool

Anti-pattern:

def do_work(action, params):
    if action == "deploy":
        # deploy logic
    elif action == "test":
        # test logic
    elif action == "notify":
        # notify logic
    # ... 50 more actions

Why it’s bad:

Unclear purpose
Hard to maintain
Poor error handling
Model struggles to use it correctly

Better: Break into atomic tools

2. Letting models execute shell commands directly

Anti-pattern:

def run_command(cmd: str):
    return subprocess.run(cmd, shell=True)

Why it’s catastrophically bad:

Arbitrary code execution
No validation
Security nightmare
Models will inevitably try rm -rf /

Better: Whitelist specific commands, wrap in safe functions

3. Returning unstructured text

Anti-pattern:

return "User 12345 was created successfully"

Why it’s bad:

Model has to parse text
Fragile and brittle
Hard to compose with other tools

Better: Always return structured JSON

4. No versioning

Anti-pattern:

Change tool schemas without versioning
Break existing integrations
No migration path

Why it’s bad:

Production systems break unexpectedly
No way to support old and new simultaneously

Better: Version your tools (get_user_v1, get_user_v2)

5. No auth boundaries

Anti-pattern:

All tools available to all users
No permission checks
No rate limiting

Why it’s bad:

Security breach waiting to happen
Abuse potential
No accountability

Better: Implement RBAC, audit logs, rate limits

10. Security Model (Non-Optional)

Security is not optional for production MCP servers. Here’s your checklist:

Required safeguards

1. Tool allowlists

ALLOWED_TOOLS = {
    "read_file",
    "list_directory",
    "search_code"
}

def validate_tool(tool_name):
    if tool_name not in ALLOWED_TOOLS:
        raise PermissionError(f"Tool {tool_name} not allowed")

2. Environment isolation

# Use Docker containers, VMs, or sandboxes
docker run --rm --read-only --network none \
  --cpus=0.5 --memory=512m \
  mcp-server:latest

3. Input validation

from jsonschema import validate

def execute_tool(tool_name, params):
    schema = get_tool_schema(tool_name)
    validate(params, schema)  # Throws if invalid
    # ... execute

4. Output filtering

def sanitize_output(result):
    # Remove sensitive data
    if "password" in result:
        result["password"] = "[REDACTED]"
    return result

5. Audit logging

def log_tool_execution(user, tool, params, result, error=None):
    log.info({
        "timestamp": now(),
        "user": user,
        "tool": tool,
        "params": hash(params),  # Don't log sensitive data
        "success": error is None,
        "error": str(error) if error else None
    })

The golden rule

Assume the model will try everything.

Models are curious, creative, and will test boundaries. Your job is to make it safe for them to explore.

Defense in depth

Layer multiple security controls:

User intent
    ↓
Rate limiting        ← Prevent abuse
    ↓
Authentication       ← Verify identity
    ↓
Authorization        ← Check permissions
    ↓
Input validation     ← Sanitize inputs
    ↓
Sandboxed execution  ← Isolate actions
    ↓
Output filtering     ← Remove sensitive data
    ↓
Audit logging        ← Track everything

Don’t rely on any single layer.

Final Chapter — Learning Path & Resources

Step-by-Step Mastery Plan

Phase 1: Conceptual understanding (1-2 days)

Goals:

Understand the difference between tools, resources, and prompts
Learn the discovery flow (how models find and use tools)
Grasp the mental model (Model ↔ Client ↔ Server)

Activities:

Read this guide completely
Review the official MCP specification
Study example MCP servers in the wild

Key question to answer: Why is MCP better than hard-coded function calling?

Phase 2: Build your first server (2-3 days)

Goals:

Write a functional MCP server from scratch
Implement 3-5 atomic tools
Handle errors and edge cases

Recommended first tools:

get_current_time — Simple, no external dependencies
calculate — Takes input, returns structured output
search_files — Real filesystem interaction

Frameworks to consider:

Python: Use the official MCP Python SDK
TypeScript: Use the official MCP TypeScript SDK
Other languages: Implement the JSON-RPC protocol

Success criteria: Your MCP server passes all protocol conformance tests

Phase 3: Integration (3-5 days)

Goals:

Connect your server to an actual LLM
Observe tool selection behavior
Debug misunderstandings

Integration options:

Claude Desktop — Easiest, built-in MCP support
Custom client — Use MCP client SDK
Agent framework — Integrate with LangChain/LangGraph

What to observe:

Does the model understand when to use each tool?
Are your tool descriptions clear enough?
Are errors handled gracefully?

Iteration: Refine descriptions based on model behavior

Phase 4: Production readiness (1-2 weeks)

Goals:

Add authentication and authorization
Implement monitoring and logging
Version your schemas
Deploy safely

Checklist:

Auth: API keys, OAuth, or JWT
Rate limiting: Per-user, per-tool
Monitoring: Latency, error rates, usage
Logging: Audit trail for sensitive operations
Versioning: Tool schema versions
Testing: Unit tests, integration tests
Documentation: API docs, examples
Deployment: Docker, Kubernetes, or serverless

High-Quality MCP Resources

Official documentation

MCP Specification: https://modelcontextprotocol.io
MCP GitHub: https://github.com/modelcontextprotocol
Python SDK: https://github.com/modelcontextprotocol/python-sdk
TypeScript SDK: https://github.com/modelcontextprotocol/typescript-sdk

Tutorials & examples

Anthropic MCP examples: https://github.com/anthropics/mcp
Community MCP servers: https://github.com/modelcontextprotocol/servers
Building your first MCP server: Check official docs for walkthroughs

Architecture & integration

LangGraph + MCP: https://langchain-ai.github.io/langgraph/
Claude API docs: https://docs.anthropic.com
OpenAI tool use: https://platform.openai.com/docs

Production patterns

Secure tool execution: Sandboxing, isolation, least privilege
Sandboxed runtimes: Docker, gVisor, Firecracker
Capability-based access control: Grant minimal necessary permissions
Rate limiting strategies: Token bucket, sliding window
Observability: OpenTelemetry, Prometheus, Grafana

Summary: MCP in production

What you learned

MCP is a protocol — not a framework or agent system
Three primitives — tools (actions), resources (context), prompts (templates)
Atomic tools win — compose, don’t combine
Security is mandatory — assume models will test boundaries
MCP enables agents — provides the execution layer for reasoning systems

When to use MCP

Good fit:

Building tools for multiple LLMs
Need runtime tool discovery
Production AI applications
Agent systems
Multi-step workflows

Overkill for:

Simple prototypes
Single-model, single-tool applications
When function calling is sufficient

The big picture

MCP is doing for AI tools what USB did for hardware: creating a universal standard that “just works.”

As LLMs become more capable, the bottleneck shifts from reasoning to execution. MCP solves the execution problem, letting models safely interact with the real world.

The future: Every API, every service, every tool will have an MCP interface. Models will compose them naturally, just as we use APIs today.

Start building. The ecosystem is early, and the opportunities are massive.

1. Why MCP Exists (The Real Problem)#

Before MCP#

The problems this created#

What MCP fixes#

The result#

2. Core MCP Mental Model#

The architecture (three components)#

What MCP is NOT#

What MCP IS#

3. MCP Building Blocks (Deep Dive)#

3.1 Tools (Actions)#

3.2 Resources (Context)#

3.3 Prompts (Templates)#

4. MCP Server Architecture (Production-Ready)#

Minimal MCP server structure#

Responsibilities of an MCP server#

Transport layers#

5. Tool Design Best Practices (Critical)#

5.1 Atomic tools (single responsibility)#

5.2 Deterministic outputs (structured data)#

5.3 Safe defaults (security first)#

6. MCP vs Function Calling vs Plugins#

Function Calling (OpenAI, Anthropic)#

Plugins (ChatGPT, older systems)#

MCP Advantages#

7. Top Most Useful MCP Tools (Real-World)#

7.1 Filesystem MCP#

7.2 Database MCP (SQL + NoSQL)#

7.3 GitHub MCP#

7.4 Web / HTTP MCP#

7.5 Search MCP (Web + Internal)#

7.6 Vector Database MCP#

7.7 CI/CD MCP#

8. MCP + Agents (How They Actually Combine)#

The typical agent + MCP flow#

Example: Research agent#

Key insight#

9. Common MCP Anti-Patterns#

1. One giant “do everything” tool#

2. Letting models execute shell commands directly#

3. Returning unstructured text#

4. No versioning#

5. No auth boundaries#

10. Security Model (Non-Optional)#

Required safeguards#

The golden rule#

Defense in depth#

Final Chapter — Learning Path & Resources#

Step-by-Step Mastery Plan#

Phase 1: Conceptual understanding (1-2 days)#

Phase 2: Build your first server (2-3 days)#

Phase 3: Integration (3-5 days)#

Phase 4: Production readiness (1-2 weeks)#

High-Quality MCP Resources#

Official documentation#

Tutorials & examples#

Architecture & integration#

Production patterns#

Summary: MCP in production#

What you learned#

When to use MCP#

The big picture#

1. Why MCP Exists (The Real Problem)

Before MCP

The problems this created

What MCP fixes

The result

2. Core MCP Mental Model

The architecture (three components)

What MCP is NOT

What MCP IS

3. MCP Building Blocks (Deep Dive)

3.1 Tools (Actions)

3.2 Resources (Context)

3.3 Prompts (Templates)

4. MCP Server Architecture (Production-Ready)

Minimal MCP server structure

Responsibilities of an MCP server

Transport layers

5. Tool Design Best Practices (Critical)

5.1 Atomic tools (single responsibility)

5.2 Deterministic outputs (structured data)

5.3 Safe defaults (security first)

6. MCP vs Function Calling vs Plugins

Function Calling (OpenAI, Anthropic)

Plugins (ChatGPT, older systems)

MCP Advantages

7. Top Most Useful MCP Tools (Real-World)

7.1 Filesystem MCP

7.2 Database MCP (SQL + NoSQL)

7.3 GitHub MCP

7.4 Web / HTTP MCP

7.5 Search MCP (Web + Internal)

7.6 Vector Database MCP

7.7 CI/CD MCP

8. MCP + Agents (How They Actually Combine)

The typical agent + MCP flow

Example: Research agent

Key insight

9. Common MCP Anti-Patterns

1. One giant “do everything” tool

2. Letting models execute shell commands directly

3. Returning unstructured text

4. No versioning

5. No auth boundaries

10. Security Model (Non-Optional)

Required safeguards

The golden rule

Defense in depth

Final Chapter — Learning Path & Resources

Step-by-Step Mastery Plan

Phase 1: Conceptual understanding (1-2 days)

Phase 2: Build your first server (2-3 days)

Phase 3: Integration (3-5 days)

Phase 4: Production readiness (1-2 weeks)

High-Quality MCP Resources

Official documentation

Tutorials & examples

Architecture & integration

Production patterns

Summary: MCP in production

What you learned

When to use MCP

The big picture