---
title: How to correctly use MCP servers with your AI Agents
link: https://www.philschmid.de/use-mcp-servers
source: philschmid-de
published: 2026-04-27T00:00:00Z
updated: 2026-04-27T00:00:00Z
first_seen: 2026-09-27T19:29:15.293681927Z
summary: MCP servers are not dead. Blindly enabling them bloats your context, which leads to higher cost and worse performance. Here are two proven patterns on how to correctly use MCP servers and avoid the bloat.
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---

MCP servers are not dead. But blindly enabling them bloats your context, which leads to higher cost and worse performance. Unlike Agent Skills, MCP servers don't come with progressive disclosure out of the box. It is your responsibility to select the tools needed for the task at hand. Here are two proven patterns on how to correctly use MCP servers and avoid the bloat.

## 1\. Explicit MCP Servers: Inline Tool Injection

MCP servers stay **opt-in**. Servers are referenced in your prompt with an `@mention`, and the agent resolves, fetches, and injects the tools before the model request.

![Explicit MCP Servers: Inline Tool Injection](https://www.philschmid.de/static/blog/use-mcp-servers/mcp-inline.png)

Writing `@github` or `@slack` in a prompt triggers the agent to:

1. **Resolve** the `@mention` to a registered MCP server URL
2. **Fetch** tool schemas from the server
3. **Inject** them into the `tools[]` array of the API request
4. **Forward** everything to the model, which decides what to call

Nothing loads unless requested. The tool surface stays small.

TypeScript

```typescript
// Pseudo-code
async function handlePrompt(prompt: string) {
  // Detect @mentions in the prompt
  const mentions = parseMentions(prompt); // ["@github", "@slack"]
  
  // Resolve each mention to an MCP server
  const servers = mentions.map(m => mcpServers.resolve(m));
  
  // Fetch tool schemas from each server
  const mcpTools = await Promise.all(
    servers.map(s => s.listTools())
  );
  
  // Inject into the API request alongside native tools
  const response = await llmCall({
    prompt,
    tools: [...nativeTools, ...mcpTools],
  });
  
  // handle response, call tools, etc ... 
}
```

**When to use this:** MCP usage is occasional and user-driven. Someone asks a question that needs Slack or GitHub data, they `@mention` the server, and it gets pulled in for that request only. Keeps costs low and avoids tool noise on tasks that don't need external integrations.

## 2\. Subagent MCP Servers

MCP servers are declared in subagents definition and automatically available at runtime alongside native tools like `read_file` or `run_command`.

![Subagent MCP Servers](https://www.philschmid.de/static/blog/use-mcp-servers/mcp-subagent.png)

Each subagent has its own configuration that declares its model, tools,... and optional MCP servers. You can scope MCP servers to specific tools using `allowed_tools`.

YAML

```yaml
# Pseudo-code: code_reviewer.md
---
name: code-reviewer
model: gemini-3-flash
mcp_servers:
  - url: https://github-mcp.example
    allowed_tools:
      - list_pulls
      - list_reviews
      - get_diff
---
You are a code reviewer. Review open PRs...
```

The main agent registers subagents. When invoked, each subagent connects to its declared MCP servers and merges those tools with native ones.

TypeScript

```typescript
// Pseudo-code
 
// Register subagents as tools for the orchestrator
const codeReviewer = createSubagent("./agents/code_reviewer.md");
const slackMonitor = createSubagent("./agents/slack_monitor.md");
 
async function handlePrompt(prompt: string) {
 
  // pre-processing ...
 
  const response = await llmCall({
    prompt,
    tools: [...nativeTools, codeReviewer, slackMonitor],
  });
 
  // handle response, call tools, etc ...
}
```

`allowed_tools` gives you least-privilege scoping without forking the MCP server.

**When to use this:** The use case dictates the tools. A code review agent always needs GitHub, a support agent always needs Zendesk. The MCP servers are part of what the agent *is*, not something a user opts into per request. `allowed_tools` keeps each agent scoped to what it actually needs.