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Customers are using general-purpose AI assistants such as Claude, Gemini, ChatGPT, and so on to undertake most of their work. In fact, 55% of technical communicators already use AI regularly or semi-regularly in their workflows. Technical writers are using AI assistants to perform various activities as part of their technical writing practice. The AI assistants are good at performing tasks, and it largely depends on the context you have given them. Many documentation teams are connecting AI assistants to their knowledge base platforms via MCP servers. This approach helps technical writers to perform various content-related tasks via the MCP server if the knowledge base vendor provides a set of tools. Documentation teams are hitting limits with the MCP server when the AI assistants do not know the right workflow.
AI assistants sometimes waste a lot of tokens to assess the right workflow via the MCP server and undertake it. Skills are markdown files consisting of the right set of workflows, context, and guidelines that aid AI agents in performing specific tasks. Skills utilize MCP servers under the hood. MCP servers and Skills solve different problems, and both are needed.
What MCP Servers Give AI: Access to Your Documentation Stack
Model Context Protocol (MCP) servers are a standardized access layer between AI and your business applications. MCP server comes with a strict governance model, auditability, and security that help enterprises manage access to their content and data. MCP tools can be governed, and thus, accessing content via API endpoints via MCP can comply with existing enterprise policies. One of the key advantages of the MCP server is that every tool call is traceable. Auditability helps to enforce strict enterprise documentation workflows.
If AI assistants are connected to many MCP servers, then AI assistants need to load the full tool schema, and the context for AI assistants grows faster, and uses a lot of tokens. Another problem with the MCP server is that AI assistants do not know the right sequence of tool calls to complete a workflow. They might not know your internal business processes and thus may not align with calling the right tools at every step. However, the MCP server aims to complete a given goal via multiple non-linear tool calls. It is thus important to note that the MCP server solves the access problem but fails to solve the workflow problem.
💡 Did You Know?
Every tool call made through an MCP server is traceable, meaning your AI-driven documentation workflows can meet the same auditability standards as manual processes. This makes MCP a strong fit for enterprise environments with strict governance policies.
Skills: The Missing Workflow Intelligence MCP Can’t Provide
Skills encode the tribal knowledge of your domain. Skills files contain what, why, and how for each component of your framework that you use for your job role. For technical writers, these components span across all modules as part of DDLC. Skills contain detailed steps on how to complete a certain workflow that complies with your established business processes. Skills explicitly also cover naming conventions, content type decisions, review steps, and any platform-specific quirks.
Skills are written by human experts without any engineering overhead, and they are basically written in markdown format. Unlike the MCP server, skills are used for AI’s reasoning before it calls any tools. Skills are reusable and portable. They can be referenced across many tasks and documentation workflows.
✏️ Tip
Skills files don’t require any engineering effort; they’re written in plain Markdown by domain experts. Start small: document one recurring workflow (like creating a new KB article) and turn it into a skills file. You’ll immediately notice fewer AI missteps on that task.
Access vs. Guidance: How MCP and Skills Operate at Different Layers
MCP server and Skills operate at different layers of the AI workflow. While the MCP server dictates how the AI can do in terms of capabilities, Skills addresses what the AI should do and when it should do it. Skills focus on judgment. Without skills, an AI assistant with an MCP server will still make wrong tool calls, miss certain steps, or apply generic logic to a domain-specific work. Without an MCP server, skills are instructions with no way to act. MCP server and skills are thus complementary to each other, and they close the loop.
For example, if a technical writer is tasked with creating a new knowledge base article in the Document360 project, then the Document360 MCP server gives the AI assistants access to Document360 via tools such as create article, create category, and so on. However, skills file guides AI assistants on which content type to use, which style guide to follow, what metadata to set, and how to structure the draft.
🔑 Key Takeaway
MCP servers tell AI what it can do. Skills tell AI what it should do. Without both, you either have an agent that can’t act or one that acts incorrectly. The combination is what closes the loop.
Why Do MCP Server and Skills Combinations Matter for Technical Documentation?
In technical writing, practice has strong conventions on content type, information architecture, style guide, business terminology, writing standards, image format, and associated content metadata. These conventions do not live in any tool schema. If a technical writer is just using the MCP server, the AI-generated content will be produced, but may not comply with the style guide and established business terminologies. Skills encode the writing standards that make technical documentation consistent at scale.
Figure: Technical writing skills
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MCP vs. Skills: What’s the Difference
MCP servers and skills files operate at different layers of your AI documentation workflow. Here’s how they compare:
Dimension |
MCP Server |
Skills File |
|
Primary role |
Gives AI access to business applications |
Gives AI workflow judgment and domain knowledge |
|
Layer |
Action layer: what AI can do |
Reasoning layer: what AI should do |
|
Format |
Tool schema defined by the vendor |
Markdown file written by a domain expert |
|
Who creates it |
Engineering team/tool vendor |
Technical writer or documentation lead |
|
Setup effort |
Moderate – requires server connection and authentication |
Low – plain markdown, no engineering overhead |
|
Governs |
API access, tool calls, data permissions |
Content type decisions, style guide, naming conventions, metadata |
|
Reusability |
Tied to a specific platform (e.g., Document360, Confluence) |
Portable – reusable across tasks and platforms |
|
Token usage |
High – loads full tool schema into context |
Low – focused markdown loaded on demand |
|
Auditability |
High – every tool call is traceable |
Low – reasoning layer, not logged at the tool level |
|
Failure without it |
AI has no access to create, update, or retrieve content |
AI acts but makes wrong decisions – wrong content type, wrong category, missing metadata |
|
Enterprise governance |
Strong – complies with access control policies |
Complementary – encodes internal process compliance |
|
Maintenance |
Managed by the vendor or the engineering |
Owned by the technical writing team |
|
Works without the other |
No – can act but without guidance |
No – can reason but cannot act |
Neither MCP nor skills alone is sufficient. MCP gives AI the ability to act. Skills give AI the judgment to act correctly. Together, they close the loop.
💡 Think of MCP as the hands and skills as the brain. Hands without a brain act randomly. A brain without hands can’t act at all.
Technical Writing Skills File: A Real Example
Suppose you would like to create a documentation article based on research done through Claude.ai. If you have written skills on how the article should be written in terms of writing standards, workflows, and sections, Claude.ai invokes the skills before writing the content.
Figure: Example of technical writing skills
Skills files are written in plain markdown. Any technical writer can author, edit, and maintain it. There are many free skills available online; either you can refer to those skills and create new ones as per your needs, or use the same skills.
How AI Creates a KB Article Using MCP + Skills
Here is what the complete workflow looks like when a technical writer uses an AI assistant to publish an article in a knowledge base; you need to do both a Document360 MCP server to Claude, and a skills file is loaded containing the KB article creation workflow such as including content type rules, style guide references, naming conventions, and metadata requirements, etc., before you start this process.
Step 1: The Trigger – Writer Gives the AI a Task
The technical writer sends a single prompt:
“Create a new KB article on password reset under the Account Management category.”
Example of loading skills
Before the AI touches a single MCP tool, it reads the skills file. The skills file tells the AI:
- Which content type to use – Procedural how-to, not a reference or concept article
- Where to place it – Account Management category, correct subcategory level
- What tone to apply – Second person, active voice, imperative mood per the style guide
This reading step costs almost nothing. Skipping it costs everything.
Step 2: Skills Reasoning Happens First
No MCP tool is called yet. The AI applies workflow judgment entirely from the skills file:
- Article type – How-to (task-based, user-facing)
- Category placement – Account Management (confirmed against naming conventions in skills file)
- Metadata decisions – audience role: end user, product version: current, review flag: required
This is the reasoning layer in action. The AI is following documented tribal knowledge that the skills file encodes. Without this step, the AI would proceed directly to tool calls with no context about your documentation standards.
Step 3: MCP Tools Fire in the Right Sequence
Only after reasoning is complete does the AI begin calling Document360 MCP tools in the correct order:
- get_categories – confirms the Account Management category exists and retrieves its ID
- create_article – drafts the article under the correct category with the right content structure
- update_article – applies metadata, tags, audience role, and product version fields
Each tool call is purposeful and traceable. There is no trial-and-error, no redundant calls, and no backtracking because the skills file resolved all decisions before the first tool was invoked.
Step 4: Output – Published Article
The result is not a raw AI-generated dump handed back to the writer for cleanup. What gets produced is:
- An article created in the correct category with the right structural hierarchy
- A style guide-compliant draft – correct tone, voice, and terminology from the first pass
- Metadata populated correctly – role, version, tags applied without manual entry
- A draft ready for human review – the technical writer’s job is to verify and approve, not rewrite from scratch
This is the difference between MCP and skills. MCP gave the AI the ability to act in Document360. Skills gave the AI the judgment to act correctly.
Next Step for Technical Writers Embracing AI
MCP server handles access to content in business applications, while skills handle judgment. Together, they make AI assistants useful in technical documentation at a professional level. As documentation teams scale AI usage, skills are crucial to the integration layer. Technical writers who aspire to be GenAI-native should build good skills.md file and utilize MCP servers of respective tool vendors to integrate with their knowledge base.
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