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SkillFlow MCP Server

SkillFlow MCP Server

by rafsilva85

SkillFlow MCP Server

npm version npm downloads MCP Registry License: MIT Available on SkillFlow

Connect AI coding agents to the SkillFlow AI skills marketplace. Live data. Zero config. One command.

A Model Context Protocol (MCP) server that connects AI coding agents to the SkillFlow marketplace — a curated AI skills marketplace with trust metrics and performance data.

Why SkillFlow?

AI agents are the new users. Instead of browsing a website, agents can now programmatically search, discover, and evaluate skills from SkillFlow using the MCP protocol.

"It's 2026. Build. For. Agents." — Andrej Karpathy

Key benefits:

  • Live API — real-time data from the SkillFlow backend, not hardcoded

  • Curated skills across 6 business categories with performance metrics

  • Trust metrics — success rates, run counts, ratings, and speed benchmarks

  • Zero config — no API keys, no environment variables, just npx

  • Works everywhere — Claude Desktop, Cursor, Windsurf, Copilot, Gemini CLI, and any MCP-compatible agent

Related MCP server: SkillHub MCP

Transports

This server supports two transports:

Transport

Use Case

Command

stdio

Local use with Claude Desktop, Cursor, etc.

npx skillflow-mcp-server

Streamable HTTP

Remote access via Smithery.ai, cloud agents

skillflow-mcp-http or npm start:http

Quick Start (stdio — Local)

One-liner (npx)

npx skillflow-mcp-server

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "skillflow": {
      "command": "npx",
      "args": ["-y", "skillflow-mcp-server"]
    }
  }
}

Cursor

Add to .cursor/mcp.json:

{
  "mcpServers": {
    "skillflow": {
      "command": "npx",
      "args": ["-y", "skillflow-mcp-server"]
    }
  }
}

Windsurf / VS Code / Other MCP Clients

{
  "mcpServers": {
    "skillflow": {
      "command": "npx",
      "args": ["-y", "skillflow-mcp-server"]
    }
  }
}

Global Installation

npm install -g skillflow-mcp-server
skillflow-mcp

Available Tools

Tool

Description

search_skills

Search for skills by keyword, category, or tag

get_skill_details

Get detailed info about a specific skill including pricing and performance

list_categories

List all skill categories

get_trending_skills

Get currently trending skills

get_platform_stats

Get overall platform statistics (skills, runs, creators, revenue)

Examples

Once connected, ask your AI agent:

  • "Search for skills that help with SEO content"

  • "What are the trending skills on SkillFlow?"

  • "Get details about the blog-seo-writer skill"

  • "List all skill categories"

  • "Show me the platform stats"

  • "Find skills for lead generation"

Quick Start (Streamable HTTP — Remote)

Self-hosted

npm install -g skillflow-mcp-server
PORT=3000 skillflow-mcp-http

Docker

docker build -t skillflow-mcp .
docker run -p 3000:3000 skillflow-mcp

Smithery.ai

Available at: smithery.ai/server/skillflow-mcp-server

HTTP API Endpoints

Method

Path

Description

POST

/mcp

Send JSON-RPC requests (initialize, tools/list, tools/call)

GET

/mcp

Open SSE stream for server notifications

DELETE

/mcp

Terminate a session

GET

/health

Health check (includes live skill count)

Architecture

┌─────────────┐     MCP Protocol     ┌──────────────────┐     tRPC API     ┌──────────────┐
│  AI Agent   │ ◄──────────────────► │ SkillFlow MCP    │ ◄──────────────► │  SkillFlow   │
│ (Claude,    │  stdio / HTTP+SSE    │ Server           │   (live data)    │  Backend     │
│  Cursor...) │                       └──────────────────┘                  └──────────────┘
└─────────────┘

The MCP server acts as a bridge between AI coding agents and the SkillFlow marketplace. It fetches real-time data from the SkillFlow backend API with 5-minute caching for optimal performance.

Listed On

Development

git clone https://github.com/rafsilva85/skillflow-mcp-server.git
cd skillflow-mcp-server
npm install
npm run build
npm start

Contributing

Contributions welcome! Please open an issue or submit a PR.

License

MIT — Rafael Silva


Built with the MCP SDK | Powered by SkillFlow

Available Tools

5 tools
get_publisher_infoB

Get information about a skill publisher including their published skills and verification status.

Inputs: publisher_id (required).

get_skill_detailsA

Get detailed information about a specific skill including description, install instructions, trust score, and compatible platforms.

Inputs: skill_id (required).

list_categoriesA

List all available skill categories on SkillFlow with descriptions and skill counts.

Inputs: no named parameters.

search_skillsA

Search for AI agent skills on SkillFlow marketplace by keyword, category, or tag. Returns matching skills with trust scores and install instructions.

Inputs: query (required), category, min_trust_score.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    A3.9/5.0

    Scored across 5 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose with no overlap: get_publisher_info focuses on publishers, get_skill_details on individual skills, get_trending_skills on trending lists, list_categories on categories, and search_skills on keyword-based discovery. The descriptions reinforce these boundaries, making misselection unlikely.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern using snake_case (e.g., get_publisher_info, search_skills). The verbs are descriptive and uniform across the set, with no deviations in style or convention.

    Tool Count5/5

    With 5 tools, this server is well-scoped for a marketplace discovery service. Each tool earns its place by covering distinct aspects of browsing and querying skills, publishers, and categories, without being overly sparse or bloated.

    Completeness4/5

    The tool set provides comprehensive read-only coverage for exploring a skill marketplace, including publishers, skills, categories, and search. A minor gap exists in the lack of tools for installing or managing skills, but agents can still perform discovery tasks effectively.

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