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Agentskin MCP Server

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The Semantic Layer for AI Agents. Prunes noisy API data by 70%+ for token-efficient reasoning.

About

The Semantic Layer for AI Agents. Prunes noisy API data by 70%+ for token-efficient reasoning.

Security Report

4.0
Use Caution4.0High Risk

Valid MCP server (2 strong, 4 medium validity signals). 7 known CVEs in dependencies (0 critical, 5 high severity) Imported from the Official MCP Registry. 1 finding(s) downgraded by scanner intelligence.

7 files analyzed · 8 issues found

Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.

Permissions Required

This plugin requests these system permissions. Most are normal for its category.

env_vars

Check that this permission is expected for this type of plugin.

What You'll Need

Set these up before or after installing:

Brave Search API KeyRequired

Environment variable: BRAVE_SEARCH_API_KEY

Serper.dev Search API KeyRequired

Environment variable: SERPER_API_KEY

Tavily Search API KeyRequired

Environment variable: TAVILY_API_KEY

MediaStack API KeyRequired

Environment variable: APILAYER_KEY

How to Install

Add this to your MCP configuration file:

{
  "mcpServers": {
    "io-github-shawn5cents-agentskin": {
      "env": {
        "APILAYER_KEY": "your-apilayer-key-here",
        "SERPER_API_KEY": "your-serper-api-key-here",
        "TAVILY_API_KEY": "your-tavily-api-key-here",
        "BRAVE_SEARCH_API_KEY": "your-brave-search-api-key-here"
      },
      "args": [
        "-y",
        "agentskin-server"
      ],
      "command": "npx"
    }
  }
}

Documentation

View on GitHub

From the project's GitHub README.

AgentSkin

npm version License: MIT

Context middleware for AI agents. AgentSkin removes low-value API, JSON, and terminal noise before it enters model context.

The frozen AgentSkin 5.1 offline core evaluation also has an accepted Sparse Env execution path. AgentSkin preserves the workload correctness contract while Sparse Env owns cold/warm environment selection and lifecycle; see agentskin/docs/SPARSE_ENV.md.

What it does

AgentSkin has three primary workflows:

  • compress — one front door that auto-detects JSON, CLI output, or plain text.
  • fetch_optimized_data — fetches a public URL and applies URL-specific pruning rules when available.
  • reduce — compacts terminal output using Tokenjuice's rule-driven reducers.

Five advanced tools remain for compatibility and diagnostics: apply_json_semantic, classify_url, strip_ansi, estimate_tokens, and skin_reasoning.

Quick start

npx -y agentskin@latest

MCP config:

{
  "mcpServers": {
    "agentskin": {
      "command": "npx",
      "args": ["-y", "agentskin@latest"]
    }
  }
}

Design rule

Fidelity first, compression second. Explicit signals and URL rules are authoritative. Generic keys such as id, name, and url are only fallback signals when no explicit rule exists.

A reduction is useful only when the information required by the task survives it.

Current verification

The release gate runs the AgentSkin-owned test suite, including MCP startup/security and live HTTP integration tests. Run it with:

cd agentskin
npm ci
npm test

Benchmarks are workload-specific. The test suite prints measured reductions for its fixtures instead of claiming one universal savings percentage.

Runtime

  • Node.js MCP server over stdio
  • Tokenjuice is a normal npm dependency for CLI reduction
  • URL-specific semantic pruning rules for structured APIs
  • SSRF checks for fetches, response-size limits, rate limiting, Zod validation, and processing timeouts

Development

The npm package lives in agentskin/ inside this repository.

cd agentskin
npm ci
npm test
npm run lint
npm pack --dry-run

Credits

AgentSkin semantic pruning and MCP integration: Shawn Nichols Sr. / Nichols Transco LLC.

CLI reduction uses Tokenjuice by Vincent Koc under its MIT license.

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