--- title: "The Model Context Protocol (MCP) and its impact on technical documentation" id: "18480" type: "post" slug: "the-model-context-protocol-mcp-and-its-impact-on-technical-documentation" published_at: "2025-10-28T08:08:12+00:00" modified_at: "2025-10-28T16:59:31+00:00" url: "https://www.cherryleaf.com/2025/10/the-model-context-protocol-mcp-and-its-impact-on-technical-documentation/" markdown_url: "https://www.cherryleaf.com/2025/10/the-model-context-protocol-mcp-and-its-impact-on-technical-documentation.md" excerpt: "What is the Model Context Protocol (MCP)? The Model Context Protocol (MCP) is a newly introduced open standard that, in simple terms, provides a common interface for linking AI large language models (LLMs) with external data sources and services in..." taxonomy_category: - "AI" taxonomy_post_tag: - "AI" - "MCP" --- ## What is the Model Context Protocol (MCP)? The Model Context Protocol (MCP) is a newly introduced open standard that, in simple terms, provides a common interface for linking AI large language models (LLMs) with external data sources and services in a standardised way. ## The problem MCP solves MCP was developed to solve the complexity of connecting different AI models to different data sources (the “M×N integration problem”). If you wanted your AI assistant to pull information from your company’s documentation, search your codebase, or interact with your project management system, developers had to build and maintain separate integrations for each connection. MCP eliminates this complexity through a standardised architecture: - Servers expose data and functionality through a uniform interface - Clients (AI applications) connect to any compliant server using the same protocol This means AI assistants can connect with the systems where data lives. The goal of MCP is ultimately to break down silos and give AI models real-time access to relevant context, so they can produce better, more relevant responses using up-to-date information. ### Real-time, context-aware documentation In the past, we’ve blogged about the promise of personalised content. This is where a Help file or knowledge base provides information that specifically relates to each user’s configuration, abilities, experience, goals and context. MCP enables documentation that adapts to the user’s current state. A Technical Author can now design documentation systems where: - AI recognises which version of the software the user is running and provides version-specific guidance - Documentation automatically reflects the user’s permission level or role - Troubleshooting steps adjust based on the user’s environment and previous interactions ## Connecting AI to Knowledge Bases MCP allows AI assistants to retrieve specific documentation and data in real time at query time. In the context of technical documentation, this means: - Users get answers that are far more accurate and contextually relevant, because the AI can draw on the same sources that a human would consult for the latest information. The AI system doesn’t have to rely on the information stored in its training data. - The content Technical Authors create can directly power AI responses: the documentation becomes actionable data that the AI will quote or paraphrase when helping users. - You maintain consistency and a Single Source of Truth between answers from the Knowledge Bases and an AI assistant. There is less risk of information drift. The AI is not hallucinating content on its own; it’s grounded in the single source of truth you maintain. ## Implications for technical documentation ### Increased documentation value The value of technical documentation increases because: - It encourages organisations to keep their documentation comprehensive and up to date. This because both human readers and AI assistants will be using it. - Improvements in documentation quality immediately translate into better AI responses. - It amplifies the value of accuracy. If there are gaps or ambiguities in the documents, those will surface in the AI’s output. ### Evolving role of Technical Authors AI systems, in general, want the same as humans: well-structured, clear, and modular information. *Plus ça change, plus c’est la même chose*. What will change is that someone needs to become an architect of knowledge systems, designing how information flows from sources to users through AI intermediaries. The obvious person to do this is the Technical Author, as they have the communication skills and a user focus. ### Using MCP when creating content Document360 has suggested MCP could also be used to help the Technical Author when they are writing content. MCP could connect an AI assistant to internal document repositories and create draft content whenever there are changes to code or source documents. Of course, the writer would need to review the changes, but the heavy lifting of finding what changed and where to put it can be handled by the AI. ### Semantic enrichment MCP’s architecture emphasises structured data and clear interfaces. Content may need richer metadata to help AI systems understand context, relationships between topics, and the appropriate use cases for different information. Metadata is information about information. For example, it can help the AI system know which product (e.g. Cloud or on-premise) and version (e.g. version 1.1 or version 1.2) the information is describing. The best approach for doing this is currently up for debate: - HTML and XML offer semantic richness but consume more AI context memory, potentially increasing hallucinations - Markdown provides compactness but lacks robust metadata capabilities - AsciiDoc might gain popularity as a middle ground ### Anticipating what the AI system will ask Technical Authors might need to anticipate common questions and structure content to align with how MCP servers expose data through prompts and resources. ### Connecting more content together MCP makes it feasible to create AI systems that synthesise information from multiple sources. These might be unexpected sources. This could enable Technical Authors to: - Identify documentation gaps by analysing what questions require information from multiple sources - Create unified experiences that combine official documentation with community knowledge ## Managing the risks ### Data security concerns MCP functions like a master key that lets AI access many different information sources. Therein lies risks. If someone gets control of an AI that has MCP connections, they suddenly have access to everything that AI can reach. If you don’t carefully control what each MCP connection can access, you might accidentally give the AI (and anyone using it) more information than they should have. ### Third-party MCP servers Anyone can create an MCP server. But how do you know if that server is secure and trustworthy? If you connect your AI to a poorly secured or malicious MCP server: - That server might steal the queries you send (revealing what you’re working on) - It could send back malicious content (prompt injection attacks) - It might log and keep sensitive information you share - Your credentials could be compromised ### Quality Assurance challenges When users interact with documentation through AI rather than reading it directly, the organisation faces new quality challenges: - It needs to test not just whether their documentation is clear when read directly, but also whether AI systems interpret and synthesise it correctly when responding to queries. - If MCP servers are pulling from multiple documentation sources (such as product documentation, API specifications, and release notes), the writers must ensure consistency across all these sources, as AI will potentially combine them. - In traditional documentation, some ambiguity might be acceptable when humans can use judgement. With AI mediation, ambiguous phrasing could lead to inconsistent or incorrect responses. ### Attribution and transparency When users receive AI-generated responses that have been synthesised from the documentation, they might not know what source material informed the answer. Technical Authors should consider: - How to ensure proper attribution and version transparency (this relates back to semantic content) - When AI synthesis is appropriate, versus when users need to read original documentation - How to maintain user trust when they can’t easily verify the AI’s sources ### Editorial control How do you maintain style consistency, brand voice, and messaging control when content is synthesised on-the-fly? There will be a need for guardrails, templates, and other editorial quality criteria to address this. ## Looking forward The Model Context Protocol is still new technology. It illustrates where technical documentation is heading. Organisations will be designing documentation not just for human readers, but for the AI systems that will increasingly mediate between documentation and users. The goal remains the same: helping users accomplish their tasks. The methods, however, are evolving rapidly, and Technical Authors have an opportunity to shape how this evolution unfolds. If you need training or guidance on using AI in technical documentation, Cherryleaf can help. ### Share this: - [Share on Facebook (Opens in new window) Facebook](https://www.cherryleaf.com/2025/10/the-model-context-protocol-mcp-and-its-impact-on-technical-documentation/?share=facebook) - [Share on LinkedIn (Opens in new window) LinkedIn](https://www.cherryleaf.com/2025/10/the-model-context-protocol-mcp-and-its-impact-on-technical-documentation/?share=linkedin) - [Share on WhatsApp (Opens in new window) WhatsApp](https://www.cherryleaf.com/2025/10/the-model-context-protocol-mcp-and-its-impact-on-technical-documentation/?share=jetpack-whatsapp) - [Share on Bluesky (Opens in new window) Bluesky](https://www.cherryleaf.com/2025/10/the-model-context-protocol-mcp-and-its-impact-on-technical-documentation/?share=bluesky) - ### *Related* ### Leave a Reply[Cancel reply](/2025/10/the-model-context-protocol-mcp-and-its-impact-on-technical-documentation/#respond) This site uses Akismet to reduce spam. [Learn how your comment data is processed.](https://akismet.com/privacy/) #### Previous post [AI browsers and AI assistants: What they mean for Technical Writers](https://www.cherryleaf.com/2025/10/ai-browsers-and-ai-assistants-what-they-mean-for-technical-writers/) #### Next post [Documentation product management: Does the AI era demand a new discipline?](https://www.cherryleaf.com/2025/10/documentation-product-management-why-the-ai-era-demands-a-new-discipline/) #### Browse posts by month Browse posts by month Select Month October 2026 (1) September 2026 (12) August 2026 (8) July 2026 (7) June 2026 (14) May 2026 (8) April 2026 (6) March 2026 (14) February 2026 (9) January 2026 (9) December 2025 (7) November 2025 (9) October 2025 (10) September 2025 (7) August 2025 (6) July 2025 (1) June 2025 (3) May 2025 (2) April 2025 (5) March 2025 (3) February 2025 (3) January 2025 (2) November 2024 (8) October 2024 (3) September 2024 (2) August 2024 (3) July 2024 (4) June 2024 (1) May 2024 (2) April 2024 (4) March 2024 (1) February 2024 (2) January 2024 (1) December 2023 (2) November 2023 (9) October 2023 (8) September 2023 (5) August 2023 (3) July 2023 (1) June 2023 (1) May 2023 (2) April 2023 (3) March 2023 (2) January 2023 (2) December 2022 (4) November 2022 (1) October 2022 (1) September 2022 (1) August 2022 (1) July 2022 (1) June 2022 (1) May 2022 (2) April 2022 (1) March 2022 (1) February 2022 (1) January 2022 (2) December 2021 (1) November 2021 (2) October 2021 (1) August 2021 (1) July 2021 (2) June 2021 (3) May 2021 (5) April 2021 (3) March 2021 (3) February 2021 (1) January 2021 (2) December 2020 (4) November 2020 (4) August 2020 (3) February 2020 (3) January 2020 (1) November 2019 (3) October 2019 (1) September 2019 (1) August 2019 (3) July 2019 (2) June 2019 (3) May 2019 (4) April 2019 (1) November 2018 (2) October 2018 (1) July 2018 (2) June 2018 (4) May 2018 (2) April 2018 (3) March 2018 (4) February 2018 (2) January 2018 (3) December 2017 (2) October 2017 (3) September 2017 (1) August 2017 (3) July 2017 (2) June 2017 (7) May 2017 (4) April 2017 (5) March 2017 (1) February 2017 (7) January 2017 (1) December 2016 (3) November 2016 (4) October 2016 (1) September 2016 (5) July 2016 (1) June 2016 (3) May 2016 (1) April 2016 (2) February 2016 (2) January 2016 (5) December 2015 (1) November 2015 (4) October 2015 (2) August 2015 (1) July 2015 (1) June 2015 (2) May 2015 (3) February 2015 (2) January 2015 (6) December 2014 (1) October 2014 (1) September 2014 (4) June 2014 (4) May 2014 (1) April 2014 (2) March 2014 (3) February 2014 (4) January 2014 (4) November 2013 (2) October 2013 (5) September 2013 (3) July 2013 (3) June 2013 (11) May 2013 (4) April 2013 (6) March 2013 (7) February 2013 (4) January 2013 (4) December 2012 (9) November 2012 (23) October 2012 (14) September 2012 (8) August 2012 (4) July 2012 (12) June 2012 (12) May 2012 (10) April 2012 (8) March 2012 (12) February 2012 (3) January 2012 (6) December 2011 (2) October 2011 (3) August 2011 (1) June 2011 (5) May 2011 (2) April 2011 (1) January 2011 (1) December 2010 (1) November 2010 (3) October 2010 (4) September 2010 (8) August 2010 (5) May 2010 (2) April 2010 (3) March 2010 (3) February 2010 (2) January 2010 (3) December 2009 (2) November 2009 (6) September 2009 (1) July 2009 (6) June 2009 (7) April 2009 (8) March 2009 (3) February 2009 (3) January 2009 (3) December 2008 (1) November 2008 (3) October 2008 (3) September 2008 (4) August 2008 (1) July 2008 (3) June 2008 (5) May 2008 (2) April 2008 (2) March 2008 (3) February 2008 (2) January 2008 (1) November 2007 (3) October 2007 (2) September 2007 (1) July 2007 (3) November 2006 (1) October 2006 (2) September 2006 (3) August 2006 (1) April 2006 (1) March 2006 (3) #### Browse posts by category