Context engineering with MCP

Who this is for and what you will be able to do

This guide is for developers who already use language models and MCP tools and are running into problems with long context, poorly chosen information and contradictory model behavior. After reading it you should be able to describe what context engineering covers, see which challenges MCP's design responds to, pick one or two approaches to test in your own project, and plan a way to measure whether they help.

An emerging idea, not a settled practice

Context engineering is a new term in AI. It concerns how information is structured, delivered and maintained between users, applications and AI models^[1]. It differs from prompt engineering, and practitioners are still working out its definition. Much of what follows is a set of directions to explore rather than established best practices^[1]. Treat this guide as a list of ideas to test, not a how-to manual.

What context engineering covers

The possible scope of context engineering splits into five areas^[1]:

1. Context selection: deciding which information is relevant to the task.
2. Context structuring: organizing information so the model can understand it.
3. Context delivery: deciding how and when information reaches the model.
4. Context maintenance: managing the state of context and how it changes over time.
5. Context evaluation: measuring and improving how well the context works.

These areas matter for MCP because the protocol gives applications a standard way to supply context to LLMs^[1].

A useful way to picture this is as a journey. User input arrives, and the system assembles context by combining that input with system context, conversation history and other retrieved information. The model processes that context and generates a response. The system then updates its state for the next interaction^[1]. Each step can fail, so each is worth checking on its own.

Three principles that are taking shape

The first principle is to share context as completely as possible rather than splitting it across several agents or processes. Once context is fragmented, a decision in one part can conflict with a decision in another^[1].

The second principle is that every action a model takes carries an implicit interpretation of the context. When components act on different contexts, those interpretations can clash and produce inconsistent results^[1]. For MCP applications, this suggests preferring linear processing for complex tasks over parallel work on fragmented context, and letting later steps see the full context of earlier decisions^[1].

The third principle is to balance depth against window limits. As conversations and processes grow, the context window eventually fills up. Approaches under exploration include compressing context, loading it progressively by relevance, and summarizing history while keeping key decisions and facts^[1].

How MCP's design responds to these challenges

Five challenges are named below, each with MCP's design response^[1]:

• Window limits: a model's context window has a fixed size. MCP supports structured, resource-based context that can be referenced efficiently, and resources can be paginated and loaded progressively^[1].
• Relevance: it is hard to decide which information belongs in context. MCP's tooling allows retrieval on demand, and structured prompts help keep organization consistent^[1].
• Persistence: keeping state across many interactions requires careful tracking. MCP standardizes session management and defines interaction patterns for how context evolves^[1]. The 2026-07-28 revision, however, removes protocol-level session IDs and the initialize handshake, so current code should follow that version^[2].
• Multi-modal content: text, images and structured data each need different handling. MCP accommodates several content types and offers a standard representation for multi-modal information^[1].
• Security and privacy: context often contains sensitive data. MCP keeps clear boundaries between client and server responsibilities and offers local processing options that reduce exposure^[1].

The value of this section is that it explains the reasons behind the protocol design. It does not promise that any particular usage will work.

Four approaches to try

The first is single-threaded linear processing. Some practitioners find it gives more consistent results than multi-agent designs that distribute context. It can look less efficient than parallel work, but each step builds on a full understanding of earlier decisions, which tends to make results more coherent^[1].

The second is chunking and prioritization. Large documents are split into pieces, each piece is scored for relevance to the question, and only the top few go into context. A conceptual sketch shows the idea; its helper functions are not implemented:

# Conceptual Example: Context Chunking and Prioritization
def process_with_chunked_context(documents, query):
    # 1. Break documents into smaller chunks
    chunks = chunk_documents(documents)
    # 2. Calculate relevance scores for each chunk
    scored_chunks = [(chunk, calculate_relevance(chunk, query)) for chunk in chunks]
    # 3. Sort chunks by relevance score
    sorted_chunks = sorted(scored_chunks, key=lambda x: x[1], reverse=True)
    # 4. Use the most relevant chunks as context
    context = create_context_from_chunks([chunk for chunk, score in sorted_chunks[:5]])
    # 5. Process with the prioritized context
    return generate_response(context, query)

To run something like this, you would implement chunk_documents and calculate_relevance yourself. The relevance score can come from vector similarity or from keywords^[1].

The third is progressive loading. Start with the smallest useful context and add more only when needed. For simple questions this can noticeably reduce token use, and complex questions can still be handled^[1].

The fourth is compression and summarization. Remove redundant information, condense long content, extract key facts and keep critical elements, while saving tokens. This helps keep long conversations and large documents within the window^[1].

Layers and retrieval

Four conceptual layers are suggested: a core layer of information the model always needs, a situational layer for the current interaction, a supporting layer of helpful extras, and a fallback layer read only when needed^[1].

Retrieval options can be combined. They include semantic search with embeddings, keyword search for specific facts, hybrid approaches, and metadata filtering by category, date or source^[1].

Use multiple agents with care

Multi-agent architectures are common, but they bring several problems for context management. Fragmented context leads to inconsistent decisions. Parallel work can create conflicts that are hard to reconcile. Communication between agents adds overhead that can cancel out speed gains. Keeping state coherent takes complex management^[1]. In many cases, one agent with full context may produce more reliable results than several specialized agents that each see part of the picture^[1].

Measuring results: no mature framework yet

No established framework exists yet for measuring context effectiveness, but these dimensions are worth considering^[1]:

• Input efficiency: the ratio of context to response, how much of the context affects the answer, and how well information is compressed.
• Performance: the effect on latency, whether token use is efficient, how relevant retrieved information is, and what compute is needed.
• Quality: whether responses address the question, whether facts are more accurate, whether similar questions get consistent answers, and whether hallucinations decline.
• User experience: how often users ask follow-up questions, whether they finish their tasks, and how satisfied they are.

In practice, start with a simple context as a baseline and then test more complex methods. Change one thing at a time. Combine numbers with user feedback, study the cases where a strategy fails, and weigh efficiency, quality and experience together^[1]. A/B testing different context structures and monitoring token use and response times are also worth trying^[1].

Where to start in your own MCP project

The steps below keep each change small^[1]:

1. Write down the goal: which information the model needs to succeed, which of it is essential, and what latency, token and cost limits apply.
2. Build a baseline with the simplest context, and record token use, response times and the failures you see.
3. Change one thing, such as adding progressive loading or changing retrieval, and compare against the baseline.
4. If your project uses several agents, check whether they are making decisions from different context.

Limits and common questions

• This is an area in flux. These ideas are early thinking, so every strategy needs testing on your own data.
• More context is not always better. Irrelevant material can distract the model and add cost and latency.
• Summaries lose information. Keep key decisions and facts in them, or later steps will lack the basis for their work.
• Some notes on MCP design in this guide do not match the latest protocol version, so check the current specification before relying on them^[2].

Next step

The next section in the chapter covers MCP custom transport. For the protocol details behind context, review the core concepts of resources, prompts and tools first^[2].

Adapted from the context engineering lesson of Microsoft's open-source course *MCP for Beginners*^[1], released under the MIT License.