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Mcp Chaining

Checked
parcadei·vlatest·agents·MIT

Provides MCP tools guidance and assistance 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.

  1. 1

    Run this in your project

    terminal
    $ npx skills add parcadei/continuous-claude-v3 --skill mcp-chaining -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

parcadei

Taken from the source listing. We haven't verified who controls this account.

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 passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • 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 undeclared network calls: passed

    Makes no network calls.

    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.

  • 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 parcadei/continuous-claude-v3 at d07ff4b06b.

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

Commit
d07ff4b06b
Manifest
.claude/skills/mcp-chaining/SKILL.md
Scanned
1 file, 149 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

This skill enables chaining multiple Claude Prompt Chains (MCPs) together, allowing complex tasks to be broken down into sequential steps. It facilitates passing data and context between these chained prompts, creating a more sophisticated workflow than using individual MCPs in isolation. This allows agents to perform multi-stage reasoning or generate outputs that require iterative refinement.

When to use it

  • Complex Task Decomposition: When a task is too large or intricate for a single prompt chain to handle effectively.
  • Iterative Refinement: To build upon previous results, refining an output through multiple stages of prompting and processing.
  • Multi-Stage Reasoning: When the agent needs to perform reasoning steps sequentially, using the output of one step as input for the next.
  • Data Transformation Pipelines: To create a series of prompts that transform data from one format or structure into another.

Key capabilities

  • Chaining multiple MCPs together.
  • Passing data between chained MCPs.
  • Sequential execution of prompt chains.
  • Support for complex workflows involving iterative refinement and reasoning.

Example prompts

  • "Chain these two MCPs: [MCP 1 details] then [MCP 2 details]"
  • "Execute the following sequence of MCPs, passing the output of each as input to the next."
  • "Combine MCP 'Summarize' with MCP 'Translate', ensuring the summary is translated after generation."

Tips & gotchas

  • Carefully consider the data types and formats expected by each chained MCP to ensure seamless data transfer.
  • Debugging can be challenging; break down complex chains into smaller, testable segments.