MCP Tools: The Complete Guide to Finding & Using Tools in 2025

By Chester Beard • December 10, 2025

#mcp#tools#guide#tutorial#adk#claude#vscode#cursor#database#api

MCP Tools: The Complete Guide to Finding & Using Tools in 2025

AI agents are becoming increasingly powerful, but they’re limited by what they can actually do. Without tools, an AI model is just a text generator—it can’t query your database, call your APIs, execute workflows, or interact with real systems. MCP tools bridge that gap.

This guide covers everything you need to understand, find, and implement MCP tools in your AI applications. Whether you’re building with Claude, VS Code, Cursor, Google ADK, or another platform, you’ll find practical guidance and examples throughout.

What Is MCP in the AI Context?

MCP stands for Model Context Protocol. It’s an open standard that allows AI agents to discover and use tools in a standardized way, regardless of which AI model or platform you’re using.

Think of MCP as a universal translator between AI models and the tools they need to access. Instead of each AI platform creating its own tool integration system, MCP provides a common language that any AI agent can understand.

MCP ≠ Other Definitions

You might also encounter “MCP” in other contexts:

Manufacturing Control Plan (supply chain/quality management)
Microsoft Certified Professional (legacy IT certification)
Master Control Program (science fiction/Tron)
Managed Service Provider (IT services—more commonly MSP)

This article focuses exclusively on Model Context Protocol in AI contexts. If you’re searching for something else, you’ll want to search for the specific domain term instead.

Why MCP Tools Matter Right Now

The MCP ecosystem is growing explosively. In 2024-2025, search interest in MCP has increased 300-700% year-over-year. Major AI platforms are racing to integrate MCP support:

Claude (Anthropic) has native MCP support
VS Code + GitHub Copilot integrated MCP tools
Cursor IDE recently added MCP compatibility
Google ADK (Agent Development Kit) is building MCP integration
Amazon Bedrock is exploring MCP implementations

This isn’t a niche protocol anymore—it’s becoming the standard for AI-tool interaction.

Key Concepts: Understanding the MCP Hierarchy

Before diving into tools specifically, you need to understand how MCP concepts relate to each other. Confusion here is the #1 reason people struggle with MCP implementations.

The Three Core MCP Concepts

1. The MCP Protocol (The Framework)

MCP itself is the communication protocol—the standardized way clients and servers exchange information. It defines how messages are formatted, how discovery works, and how the conversation happens. Think of it like HTTP: the underlying protocol that everything else uses.

You rarely interact with the protocol directly. It just works in the background.

2. Tools (The Focus of This Article)

Tools are specific functions that an MCP server exposes. Tools allow AI agents to take actions—execute code, call APIs, query databases, modify files, trigger workflows, send notifications.

Examples of MCP tools:

query_database (execute SQL)
create_github_issue (create issues in GitHub)
send_slack_message (post to Slack)
read_file (access documents)
execute_python (run Python code)

Tools are what make AI agents useful beyond conversation.

3. Resources (The Difference People Forget)

Resources are what MCP servers provide for AI agents to read—documentation, knowledge bases, file contents, API schemas. Unlike tools, resources don’t let the AI take action; they provide information the AI can use to decide what tools to call.

Example resources:

database_schema.json (tells AI what tables/columns exist)
api_documentation.txt (guides the AI on API usage)
company_policies.md (provides context for decisions)

4. Prompts (System Instructions)

Prompts are instructions to the AI itself about how to think and behave. They’re separate from both tools and resources.

Tools = what the AI can do
Resources = what the AI can read
Prompts = how the AI should think

Visual Hierarchy

MCP Protocol (the standard)
├── Tools (actions the AI can take)
│   ├── Data access (query databases, read APIs)
│   ├── Execution (run code, trigger workflows)
│   └── Integration (connect services)
├── Resources (information for the AI)
│   ├── Schemas (database structures)
│   ├── Documentation (API guides)
│   └── Context (knowledge, policies)
└── Prompts (AI behavior instructions)
    ├── System prompts
    └── Role definitions

Key distinction: When you search “MCP tools,” you’re asking about the Tools layer—what AI agents can do. This is different from asking about the MCP protocol itself or other MCP components.

How MCP Tools Actually Work

Understanding the mechanism helps explain why MCP exists and why it’s valuable.

The Client-Server Architecture

Every MCP setup has two components:

MCP Client: The application using the tools (Claude, VS Code, Cursor, ADK, your custom agent)
MCP Server: The application providing the tools (database connector, API wrapper, file manager, Notion integrator, etc.)

The client doesn’t know what tools the server provides. It just knows how to ask. The server doesn’t know who’s using it. It just knows how to respond.

The Tool Discovery Flow

Here’s what happens when you start an MCP-enabled application:

1. Client connects to MCP server
2. Client says: "What tools do you have?"
3. Server responds: "I have these tools: [list with descriptions]"
4. Client makes tools available to AI model
5. AI can now call any of those tools

This is why MCP is powerful: tools are discovered automatically. The client doesn’t need pre-built integrations or hard-coded knowledge. It just asks the server what’s available.

The Tool Invocation Flow

When an AI model needs to use a tool:

1. AI says: "I want to use the query_database tool"
2. Client sends request: "Please call query_database with these parameters"
3. Server executes: Runs the actual database query
4. Server responds: "Here are the results"
5. Client gives results back to AI: "Here's what happened"
6. AI continues reasoning with this new information

This happens in real-time, allowing the AI to get actual information instead of just predicting what might be true.

Concrete Example: AI Querying a Database

Let’s say you’re using Claude with a database MCP server, and you ask: “How many users signed up yesterday?”

Here’s what happens under the hood:

YOU: "How many users signed up yesterday?"

CLAUDE: "I need to query the database. Let me use the query_database tool."

MCP CLIENT → MCP SERVER:
{
  "tool": "query_database",
  "params": {
    "query": "SELECT COUNT(*) FROM users WHERE created_at >= DATE(NOW() - INTERVAL 1 DAY)"
  }
}

MCP SERVER executes the actual query and responds:
{
  "result": 247
}

CLAUDE: "Based on the actual database query, 247 users signed up yesterday."

Without MCP tools, Claude would say something like “I can’t access your database, but typically signups vary between 100-500 per day.” With MCP tools, Claude gives you the actual answer.

Why Standardization Matters

Before MCP, each platform (Claude, GPT, Anthropic’s SDK, LangChain, etc.) had its own way of defining tools. A database integration for Claude wouldn’t work with GPT. Tool definitions weren’t consistent.

MCP solves this: one tool definition works everywhere. You define your database MCP server once, and any MCP-compatible client can use it. This is massive for:

Developers: Write tool integrations once, use everywhere
Enterprises: Standardize on one tool format
Tool creators: Broader audience, more adoption

Types of MCP Tools

MCP tools generally fall into five categories. Understanding these helps you figure out what tools you need.

1. Data Access Tools

Tools that let AI read from data sources without modifying them.

Examples:

Query databases (PostgreSQL, MySQL, MongoDB)
Read files (documents, spreadsheets, code)
Call read-only APIs (weather data, public records)
Search vector databases (RAG/semantic search)
Access knowledge bases (wikis, docs)

MyMCPShelf category: Database Tools — Browse tools for SQL databases, NoSQL, vector DBs, and data connectors.

2. Action/Execution Tools

Tools that let AI do things—take actions that change the world.

Examples:

Execute code (Python, JavaScript, shell)
Create/modify files
Run workflows/scripts
Send notifications (email, Slack, SMS)
Control systems

MyMCPShelf category: Development Tools — Find tools for code execution, DevOps, CI/CD, and automation.

3. Integration Tools

Tools that connect your AI to third-party services via their APIs.

Examples:

GitHub integration (create issues, read repos, deploy)
Notion integration (query databases, create pages)
Slack integration (send messages, read channels)
Stripe integration (query transactions, manage subscriptions)
Google Workspace (Gmail, Sheets, Drive)

MyMCPShelf category: Web Services — Browse integrations with popular SaaS platforms.

4. AI-Specific Tools

Tools designed specifically for AI/ML workflows.

Examples:

Vector database tools (embed text, semantic search)
RAG tools (retrieve relevant documents)
Embedding models
Vision tools (image analysis)
Audio processing

MyMCPShelf category: AI & Machine Learning — Tools for machine learning pipelines and AI workflows.

5. Development & Utility Tools

Tools that help with development, testing, and infrastructure.

Examples:

Docker tools (manage containers)
Kubernetes management
Testing frameworks
Monitoring and logging
Configuration management

MyMCPShelf category: Development Tools — Full suite of developer-focused tools.

Specifications & Limits: What You Need to Know

When implementing MCP tools, you’ll need to understand practical constraints and specifications.

How Many Types of MCP Are There?

One. There’s one MCP protocol. But it supports different types of integrations:

MCP for LLMs (Claude, in-context tool discovery)
MCP for IDEs (VS Code, real-time code assistance)
MCP for agent frameworks (ADK, multi-step reasoning)
MCP for agent orchestration (tools composed together)

The protocol is the same; the context differs.

What’s the Maximum Number of Tools in MCP?

There’s no hard limit, but practical considerations apply:

5-15 tools: Optimal for single-purpose servers (great UX)
15-50 tools: Medium servers (databases with many tables, complex APIs)
50+ tools: Large enterprise servers (comprehensive system integration)

Why the limits matter: Each additional tool increases the tokens needed to describe what’s available, which increases latency and API costs. More tools also increase the complexity the AI must navigate (decision paralysis).

Best practice: Group related operations into single tools with parameters. Instead of create_user, create_admin, create_guest, create one create_user tool with a role parameter.

Tool Input Schema Limits

Each tool has a JSON schema describing its parameters:

{
  "name": "query_database",
  "description": "Query the database with SQL",
  "input_schema": {
    "type": "object",
    "properties": {
      "query": { "type": "string" },
      "timeout": { "type": "number" }
    }
  }
}

Specifications:

Keep descriptions under 200 characters
Limit to 10-15 parameters per tool
Use enums for fixed options (reduces AI confusion)
Provide examples in parameter descriptions

Execution Timeouts

Different platforms have different timeout limits:

Claude (via API): 5 minute default
VS Code extension: 30 second timeout typical
Cursor: 10-30 seconds
Google ADK: 60 second default

Plan your tools for quick execution (< 5 seconds ideal). For long-running operations, return a job ID and provide a separate “check status” tool.

Concurrency & Rate Limits

MCP doesn’t enforce limits, but your server should:

Rate limit concurrent requests from the same client
Connection pooling for database tools (10-20 connections typical)
Request queuing for high-volume scenarios
Error handling for timeouts and failures

Response Size Limits

There’s no hard limit, but practical concerns:

Small responses (< 1 KB): Instant, ideal
Medium responses (1-100 KB): Usually fine
Large responses (100+ KB): Can cause latency
Very large responses (1+ MB): Will hit token limits

For large data, use pagination or return a reference instead of the full dataset.

How to Use MCP Tools: Platform-Specific Guides

This is the question people ask most often: “How do I actually use MCP tools?” Here’s platform-specific guidance.

Using MCP Tools in Claude

Claude supports MCP tools via the Claude.ai desktop app and the Anthropic API.

Quickest start: Claude desktop app

Create an MCP server (or use an existing one)
Edit Claude’s config file:
- Mac/Linux: ~/.claude/claude_desktop_config.json
- Windows: %APPDATA%\Claude\claude_desktop_config.json
Add your MCP server:

{
  "mcpServers": {
    "my-database": {
      "command": "node",
      "args": ["~/my-database-mcp-server/dist/index.js"],
      "env": {
        "DATABASE_URL": "postgresql://..."
      }
    }
  }
}

Restart Claude desktop app
Start using tools in conversation:
- “Query my database for users created today”
- Claude will automatically discover and use your tools

For the API:

from anthropic import Anthropic

client = Anthropic()

# Define your MCP server
mcp_server = {
    "type": "stdio",
    "command": "node",
    "args": ["path/to/mcp-server.js"]
}

# Tools are exposed to Claude automatically
response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    tools=[...],  # Define your tools
    messages=[{"role": "user", "content": "Query the database"}]
)

Common error: “Tool not found”

Solution: Make sure server is running and returning tools correctly
Check: console.log the tools list before sending to Claude

Using MCP Tools in VS Code + GitHub Copilot

GitHub Copilot integrates MCP tools for code completion and generation.

Setup:

Install the latest GitHub Copilot extension for VS Code
Configure MCP server in VS Code settings:

{
  "github.copilot.advanced": {
    "mcpServers": {
      "my-api": {
        "command": "python",
        "args": ["~/my-api-mcp/server.py"]
      }
    }
  }
}

Reload VS Code
Use in comments or chat:
- Press Ctrl+I and ask: “Call my API to fetch users”
- Copilot discovers available tools automatically

Why VS Code is popular: IDE integration means tools are available while you code. You can query your database directly from a code comment.

Using MCP Tools in Cursor

Cursor is a GitHub Copilot-alternative IDE that’s rapidly gaining adoption (note the growth in AlsoAsked questions about Cursor).

Setup:

Configure in Cursor settings (~/.cursor/cursor_config.json):

{
  "mcpServers": {
    "database": {
      "command": "node",
      "args": ["path/to/database-mcp.js"]
    }
  }
}

Restart Cursor
Use in Cursor composer or chat:
- ”@” mention MCP tools in Cursor Composer
- Type: “Query the database for…” and tools auto-complete

Cursor advantage: Better at understanding context across files, so MCP tools are more useful for larger refactoring tasks.

Using MCP Tools in Google ADK

Google’s Agent Development Kit is built with MCP in mind from the start.

Setup (using ADK CLI):

# Create new ADK project
adk create my-agent

# Add MCP server dependency
adk add mcp-server-database

# Configure in agent config
cat > agent.yaml << EOF
tools:
  - name: query_database
    mcp_server: database
    config:
      url: postgresql://...
EOF

# Run agent
adk run

ADK-specific consideration: ADK treats MCP as first-class, not bolted-on. All tool discovery and invocation happens through ADK’s native agent reasoning engine.

Security Essentials for Any Platform

Before deploying tools in production:

1. Input Validation

# Validate parameters before passing to your system
if not validate_sql_query(query):
    raise ValueError("Invalid SQL query")

2. Rate Limiting

# Limit calls per minute
from ratelimit import limits

@limits(calls=100, period=60)
def query_database(query):
    ...

3. Audit Logging

# Log all tool invocations for security review
log_tool_call(tool_name, parameters, user_id, timestamp)

4. Error Handling

try:
    result = execute_tool(parameters)
except Exception as e:
    return {"error": "Tool execution failed", "safe_message": "Permission denied"}
    # Never expose internal errors to AI

Quick-Start Checklist

Choose your platform (Claude, VS Code, Cursor, ADK)
Decide which MCP server you need (database, API, file, etc.)
Add config to the appropriate config file
Restart the application
Test with a simple query: “What tools are available?”
Use a single tool: “Execute [tool name] with [parameters]”
Monitor for errors and optimize timeouts

MCP vs. Alternatives: When to Use MCP Tools

Before choosing MCP, you should understand your alternatives and when each makes sense.

MCP Tools vs. ADK (Google Agent Development Kit)

This comparison is crucial right now. Google released ADK in 2024-2025, and it’s gaining adoption rapidly.

MCP (Model Context Protocol)

Open standard, not tied to any vendor
Protocol for tool discovery and invocation
Works with Claude, VS Code, Cursor, ADK, others
Requires more setup but maximum flexibility
Focus: Universal compatibility

ADK (Google Agent Development Kit)

Google’s framework for building AI agents
Built-in tool integration (uses MCP internally)
Integrates with Google’s AI services (Gemini, Vertex AI)
Faster to get started (opinionated defaults)
Focus: Agent-centric development

Comparison Table:

Aspect	MCP	ADK
Vendor tie-in	None (open standard)	Google services
Setup complexity	Medium	Low (opinionated)
Flexibility	High (any tools)	High (Google tools)
Learning curve	Steep	Gentle
Enterprise support	Growing	Strong
Best for	Multi-platform, custom tools	Google stack users

Can you use both? Yes. ADK can use MCP servers. Many teams are combining them: ADK for agent framework, MCP for tool standardization.

Choose MCP if:

You need tools across multiple platforms (Claude, VS Code, Cursor)
You have custom proprietary tools
You want vendor independence
Your team knows JSON/protocol details

Choose ADK if:

You’re already in Google’s ecosystem (Vertex AI, Gemini)
You want fastest time-to-value
You need agent-level features beyond tool calling
Your team prefers opinionated frameworks

MCP Tools vs. Function Calling

Older LLM platforms use “function calling” (OpenAI, some Anthropic docs).

Function Calling

{
  "type": "function",
  "function": {
    "name": "query_database",
    "parameters": {...}
  }
}

MCP Tools (more structured)

{
  "name": "query_database",
  "description": "...",
  "input_schema": {...}
}

Key differences:

Function calling is model-specific, requires redefining per platform
MCP tools are standardized, works across platforms
Function calling is simpler for single-platform setups
MCP is better for multi-platform, scalable approaches

Choose function calling if:

You only use one LLM provider
You have simple tool needs (< 5 tools)
You want minimal overhead

Choose MCP if:

You use multiple platforms
You have growing tool needs
You want portability

MCP Tools vs. Traditional APIs

You could just give AI direct REST API access instead of using MCP.

REST API approach:

AI gets API docs, makes HTTP requests itself
No standardization, each API is different
Security concerns (API keys, rate limiting)
Harder for AI to discover available operations

MCP approach:

Standard tool format, consistent experience
Server-side security and rate limiting
Tool discovery built in
Easier for AI to reason about what’s available

Choose REST APIs if:

You’re already exposing REST APIs to humans
You want the AI to have complete API access
You need external API integration (stripe, GitHub)

Choose MCP if:

You want AI-specific security and control
You have complex business logic to encapsulate
You want consistent tool interface

MCP Tools vs. Custom Code Integration

Some teams just write custom code to connect AI to systems.

# Custom integration without MCP
if user_question == "query database":
    results = database.query()
    return results

Custom code:

Works for simple cases
No standardization, different for each tool
Not portable to other platforms
Scales poorly as tools grow

MCP:

Standardized, portable, discoverable
Scales with tool growth
Works across platforms
More upfront setup cost

Choose custom code if:

You have just 1-2 simple tools
You’re prototyping
You have tight time constraints

Choose MCP if:

You have 5+ tools
You plan to scale
You want to reuse tools across projects

Decision Matrix: Which Approach?

Scenario	Best Choice	Why
Single-tool prototype	Custom code	Fastest setup
5+ tools, single platform	Function calling	Simpler than MCP
5+ tools, multiple platforms	MCP	Standardized, portable
Google ecosystem	ADK + MCP	ADK handles orchestration, MCP tools
Enterprise, mixed stack	MCP	Vendor neutral, scalable
API marketplace	REST API	Designed for external access
Internal AI system	MCP	Control, security, standardization

How to Find & Evaluate MCP Tools

Now that you understand what MCP tools are and how they work, you need to find the right ones for your use case.

The Discovery Challenge

The problem: MCP is new. Tools are scattered across:

GitHub (hard to filter, no quality signals)
NPM/PyPI (mixed with non-MCP packages)
Company documentation (proprietary, hard to find)
Reddit discussions (unverified)
No central, curated directory

This is why MyMCPShelf exists.

Official Resources (Limited)

Anthropic’s MCP registry (github.com/modelcontextprotocol)

Official examples and documentation
Limited to ~20 reference implementations
Good for learning, not comprehensive

NPM/PyPI package searches (npm search mcp)

Returns hundreds of results
No quality filtering
Hard to find what you need

MyMCPShelf: Curated Tool Discovery

MyMCPShelf.com is a comprehensive directory of 600+ MCP servers with:

Structured metadata:

Real-time GitHub star count
Freshness (last update date)
Programming language
Category (database, web services, development, etc.)
Server status (maintained, archived, etc.)

Ratings and reviews:

Community adoption signals
Usage patterns
Performance indicators

Easy filtering:

By category (Database Tools, Web Services, etc.)
By language (Python, JavaScript, Go, Rust)
By status (maintained vs. archived)
By GitHub stars (popularity signal)

Comparison tools:

Side-by-side tool evaluation
Features comparison
Integration checklist

Tool Evaluation Checklist

When evaluating any MCP tool/server, ask:

Maturity:

Is the project actively maintained? (check last commit date)
Does it have 50+ GitHub stars? (adoption signal)
Are there recent bug fixes? (quality signal)
Is there a changelog? (transparency)

Documentation:

Clear setup instructions
Example code that actually works
Tool list with descriptions
Troubleshooting guide

Security:

No hardcoded credentials in examples
Clear configuration instructions
Error handling (doesn’t expose internals)
Version pinning recommended

Performance:

Reasonable startup time (< 5 seconds)
Tool invocation latency (< 2 seconds for I/O)
Resource usage (memory, CPU)
Concurrent request handling

Compatibility:

Works with Claude (your primary platform?)
Works with VS Code/Cursor (if needed)
Compatible with your language stack
Supports your MCP version

Support:

Active maintainer (check issues)
Community discussions (Discord, GitHub)
Commercial support option (for enterprises)

Where to Find Evaluated Tools

Browse by category:

Database Tools — PostgreSQL, MySQL, MongoDB, vector DBs
Web Services — Notion, Slack, GitHub, Stripe
Development Tools — Code execution, Docker, DevOps
AI & Machine Learning — Vector DBs, embeddings, RAG
File Management — File systems, cloud storage

Browse all 600+ tools: MyMCPShelf.com

Best MCP Tools for Common Use Cases

Here are practical recommendations for typical scenarios. Each links to relevant tools in the MyMCPShelf directory.

For Data Analysis & Business Intelligence

You need to query databases and analyze data.

Top tools:

PostgreSQL MCP Server — Query PostgreSQL databases, analyze data
MongoDB MCP Server — Work with MongoDB collections
Vector Database Connector — Semantic search on your data
Google Sheets MCP — Analyze spreadsheet data

Browse all: MyMCPShelf Database Tools

Typical flow:

Ask Claude: “What’s the revenue trend?”
Claude uses database tool to query data
Claude analyzes results and explains trends

For Software Development & Deployment

You need to integrate with GitHub, code execution, DevOps tools.

Top tools:

GitHub MCP Server — Read repos, create issues, manage PRs
Python Code Execution — Execute Python scripts safely
Docker Integration — Manage containers
Git Operations — Clone, commit, branch management

Browse all: MyMCPShelf Development Tools

Typical flow:

Ask Claude: “Create a feature branch and scaffolding”
Claude uses GitHub tool to create branch
Claude uses code execution tool to generate boilerplate
You review and iterate

For Business Workflow Automation

Connect to Slack, Notion, Stripe, and other business tools.

Top tools:

Notion MCP Server — Query/create pages, manage databases
Slack Integration — Send messages, read channels
Google Workspace (Gmail, Sheets, Drive)
Stripe MCP — Query transactions, manage subscriptions

Browse all: MyMCPShelf Web Services

Typical flow:

Message Claude in Slack: “Summarize this week’s sales”
Claude uses Stripe tool to fetch transactions
Claude uses Google Sheets tool to create report
Claude posts summary to Slack

For Content & Knowledge Work

Search documents, access knowledge bases, process files.

Top tools:

Web Scraper MCP — Extract content from websites
PDF/Document Reader — Extract text from files
Vector Search — Find similar documents
Knowledge Base Query — Access internal docs

Browse all: MyMCPShelf File Management

Typical flow:

Ask Claude: “Summarize our competitor analysis docs”
Claude uses document reader to access files
Claude uses vector search to find related docs
Claude synthesizes into comprehensive summary

The Future of MCP Tools

MCP is in explosive growth phase right now. Here’s what’s coming.

Growing Ecosystem & Enterprise Adoption

2025 will see:

More tools created — As MCP gains adoption, tool creators prioritize it
Enterprise integrations — Salesforce, SAP, Oracle planning MCP support
Better discovery — Improved registries, package managers
Tool marketplaces — Paid MCP tool services emerging

Emerging Patterns

Tool composition: Chaining multiple tools together (read database → call API → send Slack message)

Agentic workflows: MCP tools enabling true multi-step agent reasoning, not just tool calling

Tool versioning: Managing tool evolution as APIs change

Performance optimization: Smarter caching, batch operations, parallel execution

What’s Coming Next

Better discovery mechanisms: Think “NPM for MCP tools” — central registry with quality signals

Performance standards: Benchmarks and recommendations (< 2 second latency ideal)

Certification programs: Enterprise teams can certify tools as production-ready

Security standards: Best practices, vulnerability scanning, permission models

The MCP ecosystem is following the same path as Kubernetes, Docker, and other successful standards. Discovery and standardization are the next frontiers.

Frequently Asked Questions

Q: What is MCP an acronym for?

A: MCP stands for Model Context Protocol. It’s an open standard for AI agents to discover and use tools. (Note: MCP can also mean Manufacturing Control Plan in supply chain contexts, or Microsoft Certified Professional in legacy IT certifications, but this article focuses on Model Context Protocol in AI.)

Q: What is the MCP full form?

A: Model Context Protocol. It defines how AI clients (Claude, VS Code, etc.) communicate with servers that provide tools and resources.

Q: What is the difference between tools and MCP?

A: MCP is the protocol (the communication standard). Tools are specific functions that MCP servers expose. It’s like HTTP (protocol) and REST APIs (what use the protocol).

Q: What is the difference between MCP tools and prompts?

A: Tools let AI agents take actions (execute code, call APIs, query databases). Prompts are instructions to the AI about how to think and behave. Tools extend capability; prompts guide behavior.

Q: What is the difference between MCP tools and resources?

A: Tools are actions (query database, send email). Resources are information (database schema, documentation). Tools change the world; resources inform decisions.

Q: What is the maximum number of tools in MCP?

A: There’s no hard limit, but practical considerations apply. 5-50 tools per server is optimal. More tools increase token usage (cost/latency) and decision complexity. Best practice: group related operations into single tools with parameters rather than creating separate tools.

Q: How many types of MCP are there?

A: One protocol, many integration contexts. MCP works with:

LLMs (Claude, Gemini)
IDEs (VS Code, Cursor)
Agent frameworks (ADK)
Custom applications

Q: What is the difference between MCP and ADK?

A: MCP is a communication protocol (the “language” for tools). ADK (Google’s Agent Development Kit) is a framework for building AI agents. They’re complementary — ADK agents can use MCP tools.

Choose MCP if: you need cross-platform tool compatibility Choose ADK if: you’re building within Google’s ecosystem

Q: Are AZA and MCP the same?

A: AZA isn’t a standard AI term in common use. You might be thinking of:

A2A (Agent-to-Agent) — agents calling each other
AZA in another domain — manufacturing, military?

If you meant something specific, search for the full term.

Q: Will MCP replace APIs?

A: No. APIs are general-purpose infrastructure. MCP is specifically designed for AI-tool interaction. Both will coexist:

APIs remain for traditional web/mobile applications
MCP layers on top for AI-specific optimization

Q: Where is MCP used?

A: MCP is integrated in:

Claude (Anthropic) — primary platform
VS Code + GitHub Copilot — code assistance
Cursor IDE — AI-enhanced code editor
Google ADK — agent development framework
Amazon Bedrock — emerging support
Custom applications — via open-source SDKs

Q: What is MCP good for?

A: MCP tools allow AI agents to:

Query databases and get real answers
Call APIs and integrate services
Execute code and scripts
Modify files and documents
Trigger workflows and automations
Control systems and infrastructure

In other words: MCP makes AI agents useful beyond conversation.

Q: How to use MCP tool?

A: See the “How to Use MCP Tools” section above for platform-specific guides:

Claude setup
VS Code setup
Cursor setup
ADK setup

Q: What does MCP do?

A: MCP standardizes how AI agents discover and invoke tools. It replaces the need for custom integrations with a universal protocol that any AI platform can understand.

Q: What is MCP in manufacturing/engineering?

A: In manufacturing, MCP typically means “Manufacturing Control Plan” — a supply chain quality document. This article focuses on Model Context Protocol in AI contexts.

Conclusion: From Understanding to Implementation

MCP tools are becoming the standard way AI agents interact with real systems. The ecosystem is young but growing explosively, with major platforms racing to integrate support.

Key Takeaways

MCP tools bridge the gap between AI’s reasoning capability and real system access
Tools are discovered automatically — no hardcoding required
The protocol is standardized — tools work across platforms
You have many options — 600+ existing tools in the MyMCPShelf directory
Implementation is straightforward — configuration usually takes minutes

Next Steps

If you’re just starting:

Choose your platform (Claude is easiest)
Pick a tool category (database, web services, development)
Browse MyMCPShelf.com for recommendations
Follow the quick-start guides above
Test with a simple tool first

If you’re building a tool:

Use FastMCP (Python) or the official SDK
Define your tools clearly (good descriptions matter)
Test with Claude desktop app first
Submit to MyMCPShelf for visibility
Iterate based on user feedback

If you’re evaluating for your team:

Start with small proof-of-concept
Pick one domain (database, API, workflow)
Benchmark against function calling
Calculate cost/benefit
Scale to enterprise deployment

Stay Updated

The MCP ecosystem is moving fast. Subscribe to:

MyMCPShelf updates for new tool announcements
Anthropic blog for protocol updates
Official MCP documentation for specification changes

MCP Tools: The Complete Guide to Finding & Using Tools in 2025

MCP Tools: The Complete Guide to Finding & Using Tools in 2025

What Is MCP in the AI Context?

MCP ≠ Other Definitions

Why MCP Tools Matter Right Now

Key Concepts: Understanding the MCP Hierarchy

The Three Core MCP Concepts

Visual Hierarchy

How MCP Tools Actually Work

The Client-Server Architecture

The Tool Discovery Flow

The Tool Invocation Flow

Concrete Example: AI Querying a Database

Why Standardization Matters

Types of MCP Tools

1. Data Access Tools

2. Action/Execution Tools

3. Integration Tools

4. AI-Specific Tools

5. Development & Utility Tools

Specifications & Limits: What You Need to Know

How Many Types of MCP Are There?

What’s the Maximum Number of Tools in MCP?

Tool Input Schema Limits

Execution Timeouts

Concurrency & Rate Limits

Response Size Limits

How to Use MCP Tools: Platform-Specific Guides

Using MCP Tools in Claude

Using MCP Tools in VS Code + GitHub Copilot

Using MCP Tools in Cursor

Using MCP Tools in Google ADK

Security Essentials for Any Platform

Quick-Start Checklist

MCP vs. Alternatives: When to Use MCP Tools

MCP Tools vs. ADK (Google Agent Development Kit)

MCP Tools vs. Function Calling

MCP Tools vs. Traditional APIs

MCP Tools vs. Custom Code Integration

Decision Matrix: Which Approach?

How to Find & Evaluate MCP Tools

The Discovery Challenge

Official Resources (Limited)

MyMCPShelf: Curated Tool Discovery

Tool Evaluation Checklist

Where to Find Evaluated Tools

Best MCP Tools for Common Use Cases

For Data Analysis & Business Intelligence

For Software Development & Deployment

For Business Workflow Automation

For Content & Knowledge Work

The Future of MCP Tools

Growing Ecosystem & Enterprise Adoption

Emerging Patterns

What’s Coming Next

Frequently Asked Questions

Q: What is MCP an acronym for?

Q: What is the MCP full form?

Q: What is the difference between tools and MCP?

Q: What is the difference between MCP tools and prompts?

Q: What is the difference between MCP tools and resources?

Q: What is the maximum number of tools in MCP?

Q: How many types of MCP are there?

Q: What is the difference between MCP and ADK?

Q: Are AZA and MCP the same?

Q: Will MCP replace APIs?

Q: Where is MCP used?

Q: What is MCP good for?

Q: How to use MCP tool?

Q: What does MCP do?

Q: What is MCP in manufacturing/engineering?

Conclusion: From Understanding to Implementation

Key Takeaways

Next Steps

Stay Updated

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