Day 10: MCP Tools Explained: Building the Core Capabilities of an MCP Server

Why MCP Tools Are the Heart of Model Context Protocol

The primary purpose of an MCP server is to expose useful capabilities to AI applications. These capabilities are known as tools.

Without tools, an MCP server can establish connections, negotiate capabilities, and exchange protocol messages, but it cannot perform meaningful work.

Whether an AI assistant reads files, queries a database, automates a browser, creates Git commits, or sends Slack messages, every action is performed through an MCP tool.

A simplified interaction looks like this:

User

↓

Host

↓

LLM

↓

MCP Client

↓

tools/call

↓

MCP Server

↓

Tool

↓

External System

↓

Response

Understanding how tools are designed, registered, discovered, validated, and executed is one of the most important skills when building production-ready MCP servers.

What Is an MCP Tool?

An MCP tool is a callable capability exposed by an MCP server.

Unlike a traditional API endpoint, a tool represents a specific action that an AI model can invoke after understanding its purpose and required parameters.

Examples include:

• Reading a file
• Creating a Git branch
• Running Playwright tests
• Querying a database
• Sending an email
• Searching documentation
• Creating Jira issues
• Calling an internal REST API

Each tool is designed to perform one well-defined task.

Instead of exposing low-level implementation details, the server presents the tool as a structured capability that the client and language model can understand.

MCP Tools vs Traditional APIs

Although MCP tools often interact with existing APIs, they are fundamentally different.

This self-describing nature is one of the biggest advantages of the Model Context Protocol.

Anatomy of an MCP Tool

Every tool contains several pieces of information that allow AI models to understand how it should be used.

A typical tool consists of:

• Tool name
• Human-readable description
• Input schema
• Parameter definitions
• Required fields
• Optional fields
• Execution handler
• Return structure

Conceptually:

Tool

├── Name

├── Description

├── Input Schema

├── Validation Rules

├── Execution Logic

└── Response

The metadata is just as important as the implementation because it guides the language model in selecting and invoking the tool correctly.

Naming MCP Tools

Tool names should be clear, descriptive, and action-oriented.

Good examples include:

• read_file
• write_file
• create_issue
• search_repository
• run_playwright_tests
• execute_sql
• send_email

Poor examples include:

• action1
• utility
• helper
• execute
• toolA

A language model relies heavily on names and descriptions when deciding which tool to call. Ambiguous names reduce accuracy and increase the likelihood of incorrect tool selection.

Writing Effective Tool Descriptions

The description tells the AI model when the tool should be used.

Compare the following descriptions.

Poor description:

Reads files.

Better description:

Reads the contents of a file from the local project workspace. Use this tool whenever the user asks to open, inspect, summarize, or analyze an existing file.

The second description provides:

• Purpose
• Scope
• Expected use cases
• Decision guidance

Well-written descriptions significantly improve tool selection quality.

Designing Single-Responsibility Tools

An MCP tool should perform one specific task.

Good examples:

• read_file
• delete_file
• copy_file
• rename_file

Poor example:

manage_file()

What does it do?

• Read?
• Write?
• Delete?
• Rename?
• Copy?

Combining multiple unrelated behaviors into a single tool makes parameter validation more complex and increases the chance of incorrect AI decisions.

Following the Single Responsibility Principle (SRP) results in simpler, more maintainable tools.

Organizing Tool Collections

As servers grow, the number of available tools can increase significantly.

A filesystem server might expose:

Filesystem Tools

├── read_file

├── write_file

├── delete_file

├── rename_file

├── create_directory

├── delete_directory

├── copy_file

└── move_file

A Git server could expose:

Git Tools

├── clone_repository

├── create_branch

├── commit_changes

├── checkout_branch

├── merge_branch

├── push_changes

└── pull_changes

Grouping related tools makes server implementation easier and improves maintainability.

How Clients Discover Tools

Clients never assume which tools a server provides.

Instead, they request tool metadata during the discovery phase.

Client

↓

tools/list

↓

Server

↓

Tool Registry

↓

Tool Metadata

↓

Client Cache

The response includes metadata for every registered tool.

This allows new tools to become immediately available without modifying the client.

Why Dynamic Discovery Matters

Suppose version 1 of a server exposes:

• read_file
• write_file

Version 2 introduces:

• summarize_directory
• search_files
• compare_files

Because discovery occurs dynamically, existing clients automatically learn about these new capabilities during initialization.

No client update is required.

This flexibility is one of the defining strengths of MCP.

Choosing the Right Tool

Language models often encounter multiple tools with similar purposes.

For example:

search_repository()

search_documentation()

search_database()

search_logs()

The model decides which tool to invoke based on:

• Tool name
• Description
• Input schema
• User intent
• Conversation context

The better these elements are designed, the more accurately the model can select the correct tool.

Production Considerations for MCP Tools

Production-ready tools should be designed with consistency in mind.

Recommended practices include:

• Use descriptive names.
• Keep each tool focused on one task.
• Write detailed descriptions.
• Maintain consistent parameter naming.
• Return predictable response structures.
• Avoid hidden side effects.
• Validate all inputs before execution.
• Keep tools independent whenever possible.

These practices improve maintainability and make tools easier for AI models to understand.

Common Tool Design Mistakes

MCP Tool Architecture

User Request

↓

LLM

↓

Tool Selection

↓

tools/call

↓

MCP Server

↓

Tool Registry

↓

Execution Handler

↓

External System

↓

Structured Response

A well-designed MCP tool is more than a function—it is a self-describing capability that enables AI systems to interact safely and intelligently with external systems. Clear naming, focused responsibilities, rich descriptions, and predictable behavior make tools easier for language models to understand and significantly improve the reliability of AI-powered workflows. In the next part, you’ll learn how MCP tools are defined using input schemas, how parameter validation works, and why structured schemas are fundamental to safe and accurate tool execution.

MCP Tool Schemas: Input Validation and Parameter Design

Why Schemas Are Essential for MCP Tools

Every MCP tool accepts input from an AI model. Before the tool executes, the server must know:

• What parameters are required?
• Which parameters are optional?
• What data types are allowed?
• Which values are valid?
• Which values should be rejected?

The component responsible for answering these questions is the tool schema.

A schema acts as the contract between the MCP client and the MCP server.

User Request

↓

LLM

↓

Generate Parameters

↓

Tool Schema

↓

Validation

↓

Tool Execution

Without schemas, servers would receive unpredictable input, making reliable tool execution impossible.

What Is a Tool Schema?

A tool schema formally describes the input expected by an MCP tool.

It defines:

• Available parameters
• Required parameters
• Optional parameters
• Data types
• Validation rules
• Parameter descriptions

Instead of relying on documentation alone, the schema becomes machine-readable metadata that both clients and language models can understand.

Anatomy of a Tool Schema

Most tool schemas contain several common components.

Tool Schema

├── Tool Name

├── Description

├── Parameters

│      ├── Name

│      ├── Type

│      ├── Description

│      ├── Required

│      └── Constraints

└── Validation Rules

Every registered tool should expose a complete schema during discovery.

Parameter Types

Schemas define the expected type for every parameter.

Common parameter types include:

For example, a file-reading tool might require:

The client uses this information before sending requests.

Required vs Optional Parameters

A good schema clearly distinguishes required input from optional configuration.

Example:

read_file

Required

✔ path

Optional

• encoding

• line_limit

Required parameters must always be supplied.

Optional parameters allow the tool to provide additional flexibility without increasing complexity.

Parameter Descriptions

Parameter names alone are rarely sufficient.

Consider:

path

What kind of path?

A much better schema includes descriptions.

path

Absolute or relative path inside the allowed workspace.

Descriptions help language models generate correct values.

Every parameter should explain:

• Purpose
• Expected format
• Valid values
• Usage constraints

Validation Rules

Schemas define much more than data types.

Validation rules may include:

• Minimum length
• Maximum length
• Allowed values
• Numeric ranges
• Regular expressions
• Required formats

Example:

branch_name

Minimum Length: 3

Maximum Length: 50

Letters, numbers and hyphens only

The server validates these rules before tool execution begins.

Why Validation Happens Before Execution

Imagine a database tool expecting:

table_name

query

Instead, the client sends:

table_name = 12345

Without validation:

Receive Request

↓

Execute Query

↓

Database Error

↓

Failure

With validation:

Receive Request

↓

Validate Schema

↓

Reject Invalid Input

↓

Return Error

Early validation produces clearer error messages and protects downstream systems.

Schema-Driven Tool Selection

Schemas influence more than validation.

Language models also use them during tool selection.

Imagine two tools.

Tool A:

search()

Tool B:

search_repository

Parameters

repository

filename

keyword

The second tool provides much richer context.

As a result, the language model can make a more accurate decision.

Detailed schemas improve both correctness and usability.

Reusing Common Parameters

Large MCP servers often expose dozens of related tools.

Instead of designing parameters independently, use consistent naming.

Good examples:

Avoid inconsistent alternatives such as:

• filename
• filepath
• file
• location
• source

Consistency reduces confusion for both developers and AI models.

Avoiding Overly Complex Schemas

A schema should describe exactly what the tool needs.

Poor example:

create_issue

↓

30 Parameters

Simple alternative:

create_issue

↓

title

description

priority

assignee

Additional fields can often use sensible defaults.

Smaller schemas generally improve tool selection and reduce generation errors.

Nested Objects

Some operations naturally require structured input.

Example:

Database Configuration

├── Host

├── Port

├── Username

├── Password

└── Database

Nested objects keep related information together instead of creating dozens of unrelated top-level parameters.

Enumerated Values

When only specific values are valid, define them explicitly.

Example:

Restricting allowed values prevents invalid requests from reaching execution.

Schema Evolution

As MCP servers evolve, schemas also change.

Version 1:

create_user

name

email

Version 2:

create_user

name

email

department

Because clients perform discovery dynamically, they automatically receive updated schemas during initialization.

This makes extending tools significantly easier than modifying traditional APIs.

Production Best Practices

When designing schemas for MCP tools, follow these practices:

• Keep parameter names consistent.
• Make required fields truly required.
• Use optional parameters sparingly.
• Add clear descriptions to every parameter.
• Validate before execution.
• Prefer simple schemas over deeply nested structures.
• Use enumerations where appropriate.
• Keep schemas backward compatible whenever possible.

Common Schema Design Mistakes

Schema Validation Flow

Client Request

↓

Generate Arguments

↓

Load Tool Schema

↓

Validate Parameters

├── Valid

│      ↓

│ Execute Tool

│

└── Invalid

       ↓

Return Validation Error

A well-designed MCP tool schema serves as the foundation for reliable tool execution. It enables language models to understand how a tool should be used, helps clients validate requests before transmission, and allows servers to reject invalid input before reaching business logic. By combining clear parameter definitions, strong validation rules, and consistent schema design, developers can build MCP tools that are easier to discover, safer to execute, and more dependable in production AI systems.

MCP Tool Registration, Discovery and Execution Workflow

Registering MCP Tools

Before an MCP tool can be used, it must be registered with the server during startup.

Registration makes the tool part of the server’s capability registry, allowing clients to discover it during initialization.

The registration process typically includes:

• Tool name
• Description
• Input schema
• Execution handler
• Optional metadata

A simplified registration flow is shown below.

Server Startup

↓

Create Tool

↓

Register Tool

↓

Store in Tool Registry

↓

Server Ready

Only registered tools become available to connected clients.

Internal Tool Registry

The server maintains a registry containing every available tool.

Tool Registry

├── read_file

├── write_file

├── search_files

├── execute_sql

├── create_issue

├── run_playwright_tests

└── generate_report

The registry acts as the central source of truth for:

• Discovery
• Validation
• Tool lookup
• Execution routing

Rather than scanning the application repeatedly, the server simply queries this registry whenever a request arrives.

Registering Execution Handlers

Each registered tool is associated with executable logic.

Conceptually:

Tool

↓

Execution Handler

↓

Business Logic

↓

External System

For example:

The registry stores both metadata and the function responsible for executing the requested operation.

Discovery Begins

After client initialization, discovery starts.

Instead of assuming available capabilities, the client explicitly asks the server.

Client

↓

tools/list

↓

Server

↓

Tool Registry

↓

Tool Metadata

↓

Client Cache

The response contains every registered tool together with its schema and description.

Information Returned During Discovery

Discovery does not return executable code.

Instead, it returns metadata describing each tool.

Typical information includes:

• Tool name
• Description
• Input schema
• Required parameters
• Optional parameters
• Parameter descriptions

The language model later uses this information when deciding which tool to invoke.

Dynamic Discovery

One of MCP’s greatest strengths is dynamic discovery.

Imagine Version 1 of a server exposes:

Filesystem Server

├── read_file

└── write_file

Several months later, Version 2 adds:

Filesystem Server

├── read_file

├── write_file

├── compare_files

├── search_files

└── summarize_directory

Existing clients automatically discover these new capabilities during initialization.

No client update is required.

How the LLM Selects a Tool

After discovery, the language model has complete knowledge of the available tools.

Suppose the user asks:

Compare these two configuration files.

Available tools:

read_file

compare_files

search_files

delete_file

The model analyzes:

• User intent
• Tool names
• Descriptions
• Schemas
• Conversation context

It determines that compare_files is the most appropriate choice.

The client itself does not make this decision.

Constructing the Tool Request

Once the tool has been selected, the client creates a JSON-RPC request.

The request contains:

• Request identifier
• Tool name
• Tool arguments

Conceptually:

Tool Request

↓

Request ID

↓

Tool Name

↓

Arguments

↓

Server

The server then begins validation.

Server-Side Validation

Before execution, the server verifies:

• Tool exists
• Schema matches
• Required parameters supplied
• Data types correct
• Authorization successful

Receive Request

↓

Find Tool

↓

Validate Schema

↓

Authorize

↓

Ready to Execute

Every validation step protects the execution engine from invalid or malicious requests.

Executing the Tool

Once validation succeeds, execution begins.

Tool Request

↓

Execution Handler

↓

Business Logic

↓

External System

↓

Result

Depending on the tool, execution may involve:

• Reading files
• Querying databases
• Running browser automation
• Calling REST APIs
• Accessing cloud services
• Performing calculations

The protocol remains identical regardless of the underlying technology.

Returning the Result

After execution finishes, the handler returns structured data.

Execution Result

↓

Response Generator

↓

JSON-RPC Response

↓

Client

The response is matched to the original request using the request identifier.

This mechanism supports concurrent execution without ambiguity.

Handling Execution Errors

Tool execution may fail for several reasons.

Examples include:

• Invalid input
• File not found
• Database unavailable
• Authentication failure
• Permission denied
• Timeout
• Internal exception

Instead of terminating the session, the server generates a structured error.

Execute Tool

↓

Error

↓

Create JSON-RPC Error

↓

Return Response

The client remains connected and may continue issuing new requests.

Parallel Tool Execution

Modern AI assistants frequently execute several tools simultaneously.

Example:

User Request

↓

LLM

↓

Tool A

+

Tool B

+

Tool C

↓

Collect Results

↓

Generate Answer

Each tool executes independently while maintaining:

• Separate request ID
• Independent execution state
• Individual timeout
• Separate error handling

Parallel execution improves responsiveness for complex workflows.

Tool Execution Pipeline

The complete execution workflow is:

Register Tool

↓

Client Discovery

↓

LLM Chooses Tool

↓

Create Request

↓

Validate Parameters

↓

Execute Tool

↓

Generate Response

↓

Return Result

This pipeline remains consistent regardless of whether the tool accesses a filesystem, browser, database, cloud API, or another external service.

Designing High-Quality MCP Tools

Production-ready MCP tools should exhibit several characteristics.

Common Registration and Execution Mistakes

Complete MCP Tool Workflow

Server Startup

↓

Register Tool

↓

Client Initialization

↓

Tool Discovery

↓

LLM Selects Tool

↓

JSON-RPC Request

↓

Validation

↓

Authorization

↓

Execution

↓

Generate Response

↓

Client Receives Result

The complete MCP tool workflow transforms a simple function into a discoverable, AI-friendly capability. Registration makes the tool visible to clients, discovery provides rich metadata to language models, validation ensures safe execution, and structured responses allow clients to process results consistently. By following this lifecycle, developers can build MCP tools that are scalable, secure, and reliable for production-grade AI applications.

MCP Tool Security, Error Handling and Production Best Practices

Why Tool Security Is Critical

An MCP tool is capable of interacting with real systems such as files, databases, cloud platforms, browsers, payment services, and internal APIs.

Unlike traditional applications where users interact through carefully designed interfaces, MCP tools are invoked by AI models. Although the language model decides which tool to call, the server remains responsible for ensuring every execution is safe.

A secure execution pipeline looks like this:

User Request

↓

LLM

↓

Tool Selection

↓

Authorization

↓

Input Validation

↓

Execute Tool

↓

Audit Log

↓

Response

Security should never rely on the language model making the correct decision.

The server must enforce every security rule independently.

Principle of Least Privilege

Every tool should receive only the permissions required to perform its task.

Consider a file-reading tool.

Correct permissions:

Allowed

↓

Read Project Files

Incorrect permissions:

Read

Write

Delete

Rename

Execute Programs

Granting unnecessary privileges increases security risks and makes accidental misuse more damaging.

Authorization Before Execution

Authorization should occur before any business logic executes.

Typical authorization checks include:

• User identity
• Project membership
• Workspace restrictions
• Allowed directories
• Database permissions
• API scopes
• Organization policies

Receive Request

↓

Validate Schema

↓

Authorization

↓

Execute Tool

Execution should never begin before authorization succeeds.

Restricting Filesystem Access

Filesystem tools require special attention.

A secure server should restrict access to approved directories.

Workspace

├── src

├── tests

├── docs

└── config

Attempts to access locations outside the approved workspace should be rejected immediately.

This prevents tools from reading sensitive operating system files or unrelated user data.

Preventing Dangerous Operations

Not every technically possible action should be exposed as an MCP tool.

Examples of high-risk operations include:

• Deleting system files
• Executing arbitrary shell commands
• Modifying operating system settings
• Accessing unrestricted network resources
• Reading confidential credentials

Before creating a new tool, ask:

• Does this capability need to exist?
• Can permissions be reduced?
• Can the scope be restricted?
• Can the action be audited?

Removing unnecessary capabilities is often the strongest security control.

Sanitizing Tool Input

Even after schema validation, parameters should be treated as untrusted input.

Examples requiring additional validation include:

• File paths
• SQL statements
• URLs
• Search queries
• Command arguments
• User-generated text

Client Input

↓

Schema Validation

↓

Input Sanitization

↓

Business Logic

Sanitization protects downstream systems from malformed or unexpected input.

Handling Execution Errors

Every production server encounters execution failures.

Examples include:

• Missing files
• Invalid parameters
• Network failures
• API rate limits
• Database outages
• Permission denied
• Timeout

Instead of exposing internal implementation details, the server should return structured protocol errors.

Execution

↓

Failure

↓

Structured Error

↓

Client

Clear error reporting improves debugging while protecting sensitive implementation details.

Recoverable and Non-Recoverable Errors

Not every failure should be handled the same way.

Understanding which failures are recoverable prevents unnecessary retries and reduces system load.

Logging Tool Execution

Every important event should be recorded.

Typical audit events include:

• Tool selected
• Parameters validated
• Authorization completed
• Execution started
• Execution completed
• Error generated
• Execution duration

Receive Request

↓

Authorize

↓

Execute

↓

Respond

↓

Log Event

Structured logs simplify troubleshooting and operational monitoring.

Monitoring Tool Performance

Production systems continuously measure tool performance.

Useful metrics include:

Monitoring identifies slow or unreliable tools before users notice problems.

Tool Versioning

As applications evolve, tools may require new functionality.

Version 1:

create_issue

↓

title

description

Version 2:

create_issue

↓

title

description

priority

labels

Whenever possible:

• Add optional parameters instead of changing existing ones.
• Preserve backward compatibility.
• Avoid breaking existing clients.
• Deprecate features gradually.

Careful versioning reduces disruption while allowing tools to evolve.

Testing MCP Tools

Every tool should be tested independently before deployment.

Recommended testing includes:

• Unit testing
• Schema validation testing
• Authorization testing
• Integration testing
• Error handling testing
• Performance testing
• Concurrency testing

Tool

↓

Unit Tests

↓

Integration Tests

↓

Load Tests

↓

Production

Testing individual tools simplifies debugging and improves deployment confidence.

Production Deployment Checklist

Before deploying an MCP server, verify every tool satisfies the following requirements.

Common Production Mistakes

Complete MCP Tool Lifecycle

Design Tool

↓

Define Schema

↓

Register Tool

↓

Client Discovery

↓

LLM Selects Tool

↓

Validate Input

↓

Authorize Request

↓

Execute Tool

↓

Generate Response

↓

Log Execution

↓

Monitor Performance

↓

Maintain and Evolve

Key Takeaways

An MCP tool is much more than executable code. It is a protocol-defined capability that must be carefully designed, securely exposed, accurately described, thoroughly validated, properly authorized, consistently monitored, and continuously maintained. Production-ready tools follow the principles of least privilege, structured schemas, predictable responses, robust error handling, and comprehensive observability. By treating each tool as a secure, discoverable, and maintainable service, developers can build MCP servers that remain reliable, scalable, and safe for enterprise AI applications.

What’s Next

The next lesson introduces MCP Resources, explaining how servers expose structured information to AI models without executing actions. You’ll learn how resources differ from tools, how they are registered and discovered, how clients access them efficiently, and the best practices for designing scalable resource catalogs in production MCP servers.

Internal Links

• Day 1: What is MCP?
• Day 2: Why MCP Matters for AI Agents
• Day 3: MCP vs REST APIs vs Plugins
• Day 4: MCP Architecture Deep Dive
• Day 5: Build Your First Production-Ready MCP Development Environment (Python & VS Code)
• Day 6: Build Your First MCP Server in Python: A Production-Ready Guide for Beginners
• Day 7: Master the 4 MCP Transport Layer Options: STDIO vs HTTP vs SSE vs WebSockets
• Day 8: MCP Client Lifecycle: From Initialization to Tool Execution
• Day 9: MCP Server Lifecycle: From Startup to Graceful Shutdown

External Links

• WebSocket API: https://developer.mozilla.org/docs/Web/API/WebSockets_API
• Model Context Protocol: https://modelcontextprotocol.io
• JSON-RPC Specification: https://www.jsonrpc.org/specification
• Python asyncio: https://docs.python.org/3/library/asyncio.html
• Python subprocess: https://docs.python.org/3/library/subprocess.html
• Python asyncio: https://docs.python.org/3/library/asyncio.html
• Python logging: https://docs.python.org/3/library/logging.html
• Python multiprocessing: https://docs.python.org/3/library/multiprocessing.html

What makes a good MCP Tool?

A high-quality MCP Tool should have:

• A descriptive name
• A clear purpose
• A well-defined schema
• Strong input validation
• Proper authorization
• Predictable responses
• Structured error handling
• Comprehensive logging
• Performance monitoring

MCP Tool Execution Flow

• Register the tool
• Advertise capabilities
• Client performs discovery
• LLM selects the appropriate tool
• Validate parameters
• Authorize execution
• Execute business logic
• Return structured response
• Log execution
• Monitor performance

Production Checklist

Before deploying MCP Tools, verify the following:

• ✅ Clear tool naming
• ✅ Detailed descriptions
• ✅ JSON Schema validation
• ✅ Consistent parameter naming
• ✅ Input sanitization
• ✅ Authorization checks
• ✅ Least privilege permissions
• ✅ Standardized responses
• ✅ Structured error handling
• ✅ Audit logging
• ✅ Performance metrics
• ✅ Backward compatibility
• ✅ Unit tests
• ✅ Integration tests
• ✅ Load testing

Frequently Asked Questions

What is an MCP Tool?

An MCP Tool is a discoverable and executable capability exposed by an MCP server that enables AI models to perform actions using structured JSON-RPC requests.

Why are tool schemas important?

Schemas define expected input parameters, validation rules, required fields, and descriptions, allowing both clients and language models to invoke tools safely and accurately.

How does an AI model choose an MCP Tool?

The language model evaluates tool names, descriptions, schemas, user intent, and conversation context before selecting the most appropriate tool to invoke.

Why should tools follow the Single Responsibility Principle?

Single-purpose tools are easier to discover, maintain, validate, test, and invoke correctly than tools performing multiple unrelated operations.

Why is authorization necessary if schemas already validate input?

Schemas verify data structure and types, while authorization determines whether the client is permitted to execute the requested operation. Both are required for secure execution.

Conclusion

MCP Tools are the executable capabilities of the Model Context Protocol. They enable AI applications to interact with external systems through structured, discoverable, and validated operations. A production-ready MCP Tool includes a descriptive name, detailed schema, robust validation, secure authorization, standardized responses, structured error handling, and comprehensive monitoring. Understanding MCP Tools is fundamental to building reliable AI agents and enterprise automation platforms.

Continue Your MCP Zero to Hero Journey

From here onward, every lesson will build on this environment as we move from concepts to production-ready implementations.

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