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Mcp Integration

Official
anthropics·vlatest·agents

Official anthropics skill covering MCP tools, integration for orchestrating AI agents and multi-step workflows.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

Installs under a different name. Its SKILL.md names it MCP Integration, not the name it is listed under, so the command below uses that name.
  1. 1

    Run this in your project

    terminal
    $ npx skills add anthropics/claude-code --skill "MCP Integration" -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

anthropics

Matched to the vendor's own GitHub organisation.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 2 hosts that are neither well-known infrastructure nor declared in the skill's frontmatter: mcp.asana.com, mcp.github.com.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    mcp.asana.comundeclaredmcp.github.comundeclared

    What it found

    • plugins/plugin-dev/skills/mcp-integration/examples/sse-server.json:5contacts mcp.asana.com
      "url": "https://mcp.asana.com/sse"
    • plugins/plugin-dev/skills/mcp-integration/examples/sse-server.json:9contacts mcp.github.com
      "url": "https://mcp.github.com/sse"

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned anthropics/claude-code at 3deb873687.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
3deb873687
Manifest
plugins/plugin-dev/skills/mcp-integration/SKILL.md
Scanned
7 files, 2,043 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The MCP Integration skill enables AI agents to interact with and leverage the Model Control Plane (MCP) system, allowing for dynamic model selection, monitoring, and orchestration. It provides tools for managing multiple models within a single agent workflow, ensuring seamless switching between models based on task requirements.

When to use it

  • When you need to switch between different AI models during a single task or conversation.
  • When optimizing performance by selecting the most appropriate model for specific subtasks.
  • When monitoring and logging model usage across multiple AI agents in a system.

Key capabilities

  • Dynamic model switching based on context or user input
  • Integration with Model Control Plane (MCP) for centralized management
  • Monitoring of model performance and usage metrics

Example prompts

  • "Switch to the Claude model for this response."
  • "Use the most efficient model available for text summarization."
  • "Log which models were used during this session."

Tips & gotchas

  • Ensure that your MCP system is properly configured before using this skill.
  • Model switching may introduce latency, so consider performance implications in real-time applications.