What Is MCP? Common Rules for Making Claude “External Tool Aware”
MCP (Model Context Protocol) is a common protocol for connecting AI models like Claude to external tools and data sources. In simple terms, it’s like an USB-C-style connection standard for AI. It lets Claude handle files, GitHub, Slack, internal data, databases, and more through a unified approach rather than bespoke implementations.
Previously, integration methods differed wildly for each AI and each tool. Developers often had to rewrite API integrations and authentication logic every time. With MCP, Claude Desktop or an MCP-compatible client can invoke external capabilities via an MCP server. In other words, Claude becomes more than just a chat partner—it becomes an assistant that can use tools safely.
Three Roles in MCP
- Client: Claude Desktop, etc. It receives the conversation with the user and calls tools when needed.
- MCP Server: Provides capabilities such as file operations, GitHub lookups, and Slack integration.
- Model: Claude itself, which decides which tool to use and how, based on the conversation.
The key point is that Claude can’t do “everything” directly—its abilities are extended only through authorized MCP servers. This makes it easier to balance convenience and safety.
Why MCP Is Needed
As soon as you start using AI in real work, you run into the next set of barriers.
- You want to have it read local documents.
- You want it to summarize while looking at GitHub Issues or PRs.
- You want it to turn Slack conversations into meeting minutes.
- You don’t want to keep copy-pasting, and you want it to reference the latest information.
With normal chat alone, these tasks require humans to paste data into the conversation. With MCP, Claude can access the right tools whenever they’re needed—so you can work while preserving context.
Differences from Traditional Chat
| Item | Normal Claude | With MCP |
|---|---|---|
| Information source | Mostly the content you pasted into the chat | External tools and files can also be referenced |
| Freshness | You manually re-paste | It’s easier to fetch the latest data each time |
| Work efficiency | Copy-paste is assumed | Search, retrieval, and organization can be automated |
| Safety | Limited to what you pasted | Connection targets and permission management are crucial |
The value of MCP isn’t only about what you want the AI to think about—it’s also about what tools you allow the AI to have.
Representative MCP Servers
As of 2025, many MCP servers are available in both official and community ecosystems. Common ones include:
- Filesystem: Retrieves lists of local files, reads them, and saves them. Useful for organizing meeting notes, checking code, and editing drafts.
- GitHub: Fetches Issues, Pull Requests, and repository information. Great for review support and summarizing changes.
- Slack: Reads channel conversations, extracts key points, and organizes actions.
- Google Drive / Docs-type services: Finds documents, helps draft writing, and enables cross-referencing of internal documents.
- Database-type services: Runs SQL queries and references schemas. Helps with aggregation and report creation.
- Web/API integrations: Connects to internal APIs and SaaS to reduce manual effort for repetitive tasks.
For beginners, it’s recommended to start in this order: Filesystem → GitHub → Slack. Local file integration builds an intuition quickly and is easier to manage from a permissions standpoint.
How to Configure MCP in Claude Desktop
In Claude Desktop, you add MCP server information to the configuration file to use it. The exact formatting may vary by version, but the concept is the same: you register which server you want to start, and with which command.
Basic Steps
- Get an MCP server (official distribution, npm, uvx, local implementation, etc.)
- Open Claude Desktop’s configuration file
- Add a server definition under
mcpServers - Restart Claude Desktop
- Check whether the tools are recognized in Claude
Configuration Example
{
"mcpServers": {
"filesystem": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-filesystem", "/Users/yourname/Documents"]
},
"github": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-github"],
"env": {
"GITHUB_PERSONAL_ACCESS_TOKEN": "ghp_xxxxx"
}
}
}
}Please check each server’s documentation for the actual package names and startup methods. If it’s Node.js-based, you may use npx; if it’s Python-based, you might use uvx or a python command to start it.
Things to Watch Out for When Configuring
- Start with least privilege. Limit file access to only your working folders.
- Don’t store GitHub or Slack tokens in publicly shared files or on shared PCs.
- Start with a configuration close to read-only.
- If it doesn’t work, check Claude Desktop logs and the result of running the server startup command by itself.
Practical Use Cases
1. Read local documents and generate summaries/comparisons
For example, if your project folder contains specification documents, meeting notes, and past proposals, a Filesystem server alone can already be quite practical.
Review the meeting notes and specification documents in this folder,
and organize the three key changes in the latest policy.
Include the related file names as well.The trick is to have it show the supporting/source files, not just produce a summary. This makes it easier to catch anything the AI might miss.
2. Help with GitHub PR reviews
Check the latest PRs in this repository,
and output a bullet list of summaries, concerns, and test considerations.Even without pasting the entire code, Claude can read PR information and organize review angles. This is particularly well-suited for PMs (non-engineers) who need to understand what changed.
3. Turn Slack conversations into meeting minutes
Based on yesterday’s conversations in the #project-alpha channel,
summarize them into decisions, open items, and owner action items.Instead of reading the flowing chat verbatim, you get more value by extracting only the decision points. Slack has a lot of information, so it’s important to clearly specify the time period, the channel, and the purpose.
4. Cross-tool investigative research
The real power of MCP is cross-tool usage, not a single tool. For example: “Confirm requirement changes in Slack, find the related GitHub Issues, and summarize the differences against the local specification.” Copy-paste workflows like this are quite troublesome, but with MCP it’s easier to carry a single chain of context across steps.
How to Write Good Prompts for Using MCP
When connecting tools, it’s more stable to specify the target, output format, and judging criteria than to make vague requests.
Refer to GitHub issues #128 and #136, as well as the local docs/requirements.md,
and organize the requirement differences in a table format.
The columns should be: "Item", "Issue-side description", "requirements.md-side description", and "Proposed approach".- Specify which tools to look at.
- Make file names, issue numbers, and channel names concrete.
- Don’t stop at summaries—also specify a table, checklist, and next actions.
Also, for important tasks, you can add “List the information sources you used at the end” to make verification easier.
Limitations You Should Know When Adopting MCP
MCP is convenient, but it’s not magic. Differences in server implementations, authentication setup, response speed, and the complexity of permission management are realistic challenges. Especially for internal rollout, it’s important to design what can be accessed upfront.
Start small for personal use: build a success experience with Filesystem, then expand to GitHub and Slack. If you think of MCP not as a “hard-to-use extension,” but as a foundation for connecting Claude to the real work environment, it will be easier to understand.
Summary
With MCP, Claude evolves from an AI that only chats into a work assistant that can handle files, GitHub, Slack, and more. Especially in 2025-era AI adoption, outcomes depend less on differences in single-model performance and more on how effectively you can connect it to your actual business data. Start by connecting a Filesystem server in Claude Desktop and try “reading documents in a folder and summarizing them.” You should already be able to feel the value of MCP from that alone.
