SKU: 9665581320

F-3298C FreshenOPT Mercedes Benz Premium Cabin Air Filter [2238352300] (SETS)

Name: F-3298C FreshenOPT Mercedes Benz Premium Cabin Air Filter [2238352300] (SETS)
Brand: martinetexier.com
SKU: 9665581320
Price: 47.00 USD
Availability: InStock
Rating: 4.6201 (10 reviews)

$42.30 $47.00

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Description

F-3298C FreshenOPT Mercedes Benz Premium Cabin Air Filter [2238352300] (SETS)Available in 1, 2, or 3 pack choose from the dropdown to get the best value! Overview: Dimension: 19 X 24 X 3. 5 cm Item model number: F 3298C OE#: 2238350000 ; 2238351300 ; 2238352300 ; 223835400 Premium three layer design filter with efficient purification We suggest changing the cabin air filter in every 10,000km or 1 year Features: Advanced 3 Layer Filtration Captures pollen, dust, vehicle emissions, PM2. 5 particles, pet dander, mold spores, and

📦 Available in 1, 2, or 3-pack – choose from the dropdown to get the best value!

Overview:

Dimension: 19 X 24 X 3.5 cm
Item model number: F-3298C
OE#: 2238350000 ; 2238351300 ; 2238352300 ; 223835400
Premium three-layer design filter with efficient purification
We suggest changing the cabin air filter in every 10,000km or 1 year

Features:

Advanced 3-Layer Filtration – Captures pollen, dust, vehicle emissions, PM2.5 particles, pet dander, mold spores, and other airborne particles—while maintaining efficient airflow for optimal HVAC performance.
Dual Activated Carbon Layers – Two enhanced carbon layers help neutralize odors from smoke, pets, and other sources, supporting a fresher cabin environment.
Anti-Mold & Bacteria-Resistant Coating – Specialized surface treatment inhibits the growth of mold and odor-causing bacteria, helping preserve air quality.
OEM-Quality Fit – Engineered to meet OE specifications for a hassle-free installation, reliable seal, and proper fit.
Durable Construction – Rigid frame design resists warping and deformation, ensuring consistent performance and long-term reliability.

Cross-reference:

CUK25034-2 ; PC99893C ; XC12120C ; WP10782 ; CAF15074C

2026	Mercedes-Benz	EQE 320	Electric
2026	Mercedes-Benz	EQE 320 SUV	Electric
2026	Mercedes-Benz	EQE 320+	Electric
2026	Mercedes-Benz	EQE 320+ SUV	Electric
2023-2025	Mercedes-Benz	EQE 350	Electric
2023-2025	Mercedes-Benz	EQE 350 SUV	Electric
2023-2025	Mercedes-Benz	EQE 350+	Electric
2023-2025	Mercedes-Benz	EQE 350+ SUV	Electric
2023-2025	Mercedes-Benz	EQE 500	Electric
2023-2025	Mercedes-Benz	EQE 500 SUV	Electric
2023-2026	Mercedes-Benz	EQE AMG	Electric
2024-2026	Mercedes-Benz	EQE AMG SUV	Electric
2026	Mercedes-Benz	EQS 400 SUV	Electric
2023-2026	Mercedes-Benz	EQS 450	Electric
2023-2025	Mercedes-Benz	EQS 450 SUV	Electric
2022-2026	Mercedes-Benz	EQS 450+	Electric
2023-2025	Mercedes-Benz	EQS 450+ SUV	Electric
2023	Mercedes-Benz	EQS 53 AMG	Electric
2026	Mercedes-Benz	EQS 550 SUV	Electric
2022-2026	Mercedes-Benz	EQS 580	Electric
2023-2025	Mercedes-Benz	EQS 580 SUV	Electric
2022-2025	Mercedes-Benz	EQS AMG	Electric
2024-2026	Mercedes-Benz	Maybach EQS 680 SUV	Electric
2021-2026	Mercedes-Benz	Maybach S580	V8 4.0L
2022-2026	Mercedes-Benz	Maybach S680	V12 6.0L
2023	Mercedes-Benz	S450L	L6 3.0L
2021-2026	Mercedes-Benz	S500	L6 3.0L
2021-2026	Mercedes-Benz	S580	V8 4.0L
2023-2026	Mercedes-Benz	S580e	L6 3.0L
2024-2026	Mercedes-Benz	S63 AMG E Performance	V8 4.0L

🔍 Not sure about compatibility?

We’ve got you covered. Message us online with your vehicle year / make / model, and our team will help confirm the correct filter.

🏪 Prefer in-store assistance?

If you’d rather not purchase online or want extra peace of mind, visit our store.
Our experienced staff can help you match, verify, and choose the right filter in person.
📍 FreshenOPT Auto Parts, Unit 14–85 Ferrier St, Markham, ON L3R 2Y9

🔄 Easy exchange if needed

Ordered the wrong filter? We’ve got you covered.
As long as the item is unopened, you can exchange it in-store quickly and easily, or email our support team — we’ll help verify compatibility and take care of the exchange as quickly as possible.

🎁 Extra perks & surprises

Both online and in-store orders may include pop-up offers, surprise gifts, or exclusive bonuses — don’t forget to check at checkout.

Clean air should come with confidence.
That’s the FreshenOPT way.

https://www.freshenopt.ca/pages/join-our-membership

Shipping Notes

Free Standard Shipping on $100+ Orders to the USA.
Except Preorder products are shipped in 48 hours.
Delivery to the USA:

Standard Shipping : 3-10 business days

If time is of the essence, please consider selecting expedited delivery for faster service.

Exchange/Return Notes

We offer a 30-day return/exchange service after receiving.
Final sale items are not eligible for returns or exchanges.
To process your return/exchange, please contact us at [email protected]
Please click here for more details>>> Return & Exchange Policy

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Product Reviews

Brahmananda Reddy

Lake Worth, US

★★★★★ 5

Practical AI Engineering Beyond Prompts — One of the Better Books on Agentic Coding

Format: Paperback

This book is not another “AI coding hype” book. A lot of books talk about agents at a very high level. This one actually explains how things work when you try to use them inside real development workflows. That was the biggest difference for me. What I liked most was the focus on context engineering, memory, MCP, hooks, subagents, and workflow orchestration instead of just “prompt better.” The author spends time explaining why long-running agent systems fail, how context grows over time, and why most AI coding setups become messy without structure. The examples also feel practical — The HookHub project, Next.js setup, GitHub workflows, Claude memory files, and MCP integrations make it easier to connect theory with actual implementation. From my retail domain experience perspective, I could immediately connect this to forecasting and pricing workflows. For example: * agents helping analysts generate specs before model development * automated code review for promo forecasting pipelines * isolated subagents for pricing, promotions, assortment * persistent memory for business rules across teams * MCP integrations to pull context from internal systems safely The section around context isolation and subagents especially stood out because that is very similar to how enterprise forecasting teams already operate in reality. Different teams own different decision spaces. One thing I appreciated: the author does not oversell AI. There is a strong focus on constraints, context pollution, hallucinations, performance degradation, and workflow reliability. That makes the book feel grounded instead of marketing-heavy. This is not for complete beginners though. If someone has never worked with Git, APIs, coding agents, or LLM workflows, parts of the book may feel overwhelming early on. The author clearly says this is not beginner-level content. Overall, probably one of the more practical books I have read recently on agentic coding systems. Good for: * software engineers * AI engineers * enterprise architecture teams * technical product teams * analytics leaders trying to operationalize AI development workflows Especially useful if your organization is trying to move from “AI demos” into actual production workflows.

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Reviewed in the United States on May 20, 2026

Port Orchard, US

★★★★★ 5

A Good Reality Check on How AI Agents Actually Work in Enterprise Systems

Format: Paperback

Most AI books stop at prompts. This one goes deeper into how agent systems actually behave once you try to use them inside large workflows with memory, tools, permissions, automation, and multiple agents working together. That part felt very relevant for healthcare and enterprise environments. The book does a good job explaining why context engineering matters and how poor context handling creates hallucinations, inconsistent outputs, and degraded performance over time. Honestly, that is one of the biggest problems organizations underestimate right now. In healthcare workflows, context matters a lot: * prior interactions * business rules * auditability * escalation logic * safety constraints * tool permissions * workflow boundaries The sections on persistent memory, scoped context, subagents, and structured workflows connected strongly to that reality. I work in enterprise analytics, and while reading this book I kept thinking about use cases like: * pharmacy workflow automation * prior authorization support systems * coding assistants for healthcare engineering teams * AI copilots for operational analytics * agent-based escalation systems * claims and workflow orchestration The MCP chapters were also useful because they explain integration challenges clearly instead of treating tooling as magic. What made this book stand out for me was the balance between implementation and architecture. The author explains: * why long contexts fail * how context poisoning happens * why isolation matters * when parallel agents help * when they actually create more complexity That level of honesty is missing in many AI books right now. Another thing: the examples are not overly academic — The Next.js project setup, GitHub automation, Claude desktop workflows, memory systems, hooks, and subagents make the learning process feel practical and hands-on. One limitation: this book assumes technical background. Someone completely new to coding agents, LLMs, Git, or development workflows may struggle in the first few chapters. But for engineers, AI teams, enterprise architects, and technical leaders trying to understand where agentic coding is actually going, this book is worth reading. Especially for organizations trying to operationalize AI safely instead of just experimenting with chatbots.

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Reviewed in the United States on May 20, 2026

Christopher West

Dallas, US

★★★★★ 5

Great book! Practical and for developers that already use AI!

Format: Paperback

I purchased "Agentic Coding" by Claude Code due to my desire for an alternative to generic "Prompt Template" type resources related to AI-based development. This book accomplishes just that. As opposed to merely viewing Claude Code as a "magic box", the author has explained how to utilize it in conjunction with other actual development processes. The authors' emphasis on "context engineering" (i.e., structuring data/information; managing knowledge in a project; guiding an AI agent to produce consistent results vs. producing random/unknown results) represents the strongest component of the book. It should be noted that the book appears to be intended primarily for experienced developers with prior experience in software development and/or familiarity with AI-based development tools. Should you be familiar with Git, the command-line interface, and/or modern development processes, you may find this resource very helpful. Conversely, I did appreciate the fact that there were no novice-oriented descriptions provided throughout the book. The aspect of the book that I found most valuable, however, is the extremely pragmatic nature of the material contained within. The examples illustrated through developing/maintaining CLAUDE.md files; utilizing Claude Code in combination with GitHub Workflows; employing MCP Servers; and creating multi-agent or sub-agent workflows all seemed to reflect a clear focus on "real world usage" rather than theoretical constructs. In addition, each chapter builds upon previous chapters in such a manner as to provide a logical progression through which the reader can easily understand and ultimately implement the concepts learned. I also appreciated that the author included guidance on responsible utilization of the tool(s), as well as maintaining control over what changes are made by the agent. While numerous books regarding AI focus solely on what AI tools can accomplish, this book addresses both how to utilize these tools effectively in a real codebase, as well as responsibility and safety considerations. In summary, this is not a book for individuals completely inexperienced in either programming or generative AI. However, if you are currently experimenting with tools such as Claude, Cursor, GitHub Actions, or MCP, this is likely one of the more useful and practical books available on the subject. Recommended for software engineers seeking to transition from simply "prompting an AI" into establishing a repeatable/professional workflow process surrounding agentic coding.

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Reviewed in the United States on April 11, 2026

Paul Pollock

Port Orchard, US

★★★★★ 4

⭐⭐⭐⭐ (so far)

Format: Paperback

I'm maybe a third of the way through this and already rethinking how I talk to coding agents. The reframe from "prompt engineering" to "context engineering" sounds like semantics until Marco walks you through why context poisoning, context clash, the Goldilocks zone for system prompts. That chapter alone reorganized something in my head. I keep going back to the line about garbage in, garbage out being the real reason agentic systems underperform. The hands-on stuff lands well too. Building the HookHub project from scratch, wiring up Playwright MCP, watching Claude generate a CLAUDE.md file and then not automatically loading a memory file you just created — that moment where you expect magic and get silence instead? That's the kind of honest teaching I appreciate. It made the "why" behind memory hierarchies click.

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Reviewed in the United States on May 12, 2026

Jonathan Reeves

Chelsea, US

★★★★★ 5

Essential Reading for Developers Serious About Agentic AI Workflows with Claude Code

Format: Paperback

Agentic Coding with Claude Code is easily one of the most practical and forward-thinking AI development books I’ve read. Instead of treating Claude Code like a simple chatbot, this book shows how to turn it into a true agentic development platform capable of handling real-world engineering workflows. What I appreciated most was how actionable the content is. The explanations around slash commands, hooks, persistent memory files, and MCP servers are incredibly clear and immediately useful. The author does an excellent job balancing foundational concepts with hands-on implementation, making advanced topics like multi-agent orchestration and hierarchical delegation approachable for experienced developers. The chapters on MCP and context engineering were especially valuable. Most AI books stay at the surface level, but this one dives deep into structured context sharing, workflow automation, and scalable AI-assisted development practices that actually matter in production environments. I also liked that the book focuses heavily on maintainability and control. It doesn’t just show flashy demos—it teaches how to safely integrate AI agents into existing terminal and IDE workflows while enforcing coding standards and keeping projects organized. The examples using Claude Code with Next.js projects were practical and helped connect the concepts to real software engineering scenarios. The sections on subagents, planning workflows, and reusable automation patterns opened my eyes to entirely new ways of approaching AI pair programming and development productivity. If you are a developer, AI engineer, or technical lead looking to move beyond basic prompt engineering and build reliable, scalable AI-assisted workflows, this book is absolutely worth reading. Highly recommended for anyone serious about modern agentic coding and AI-powered software development.

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Reviewed in the United States on May 9, 2026