Overview

The MCP (Model Context Protocol) server is a backend service that indexes and exposes metadata for 5,000+ AI agents and tools from the AI Agent Marketplace Index. It provides searchable access to agent metadata, categorical browsing, and traffic/usage metrics so developers and product teams can discover, monitor, and integrate third-party agents into applications and dashboards.

This server is useful when you need a single, queryable source of truth for agent capabilities, categories, and runtime traffic patterns. Instead of scraping multiple sources or maintaining your own registry, the MCP server centralizes indexing and offers an API surface for search, filtering, and monitoring, enabling faster prototyping and operational observability of large agent catalogs.

Features

Indexes 5,000+ AI agents and tools from the AI Agent Marketplace Index
Full-text search across agent names, descriptions, and tags
Browse and filter by categories, providers, and capabilities
Traffic and usage metrics for monitoring agent popularity and load
REST API for programmatic access and integrations
Lightweight configuration with environment variables and Docker support
Export and snapshot capabilities for offline analysis

Installation / Configuration

Clone the repository and run locally, or use Docker for containerized deployment.

Clone and run (Node/npm example)

git clone https://github.com/AI-Agent-Hub/ai-agent-marketplace-index-mcp.git
cd ai-agent-marketplace-index-mcp
npm install
npm run build
npm start

Environment variables (example .env)

PORT=4000
NODE_ENV=production
DATABASE_URL=postgres://user:pass@localhost:5432/mcp
MCP_INDEX_SOURCE=https://raw.githubusercontent.com/AI-Agent-Hub/ai-agent-marketplace-index/main/index.json
API_KEY=your_api_key_here

Docker (compose) example

version: "3.8"
services:
  mcp:
    image: aiagent/mcp:latest
    ports:
      - "4000:4000"
    environment:
      - PORT=4000
      - DATABASE_URL=postgres://user:pass@db:5432/mcp
      - MCP_INDEX_SOURCE=https://...
  db:
    image: postgres:15
    environment:
      - POSTGRES_USER=user
      - POSTGRES_PASSWORD=pass
      - POSTGRES_DB=mcp

Start with Docker Compose:

docker-compose up -d

Available Resources

The MCP server exposes a REST API that typically includes endpoints for search, agents, categories, and traffic metrics. Example endpoints (names and paths may vary by release):

Endpoint	Method	Description
/api/search?q=…	GET	Full-text search across agents
/api/agents	GET	List agents with pagination and filters
/api/agents/:id	GET	Agent detail (metadata, tags, provider)
/api/categories	GET	Available categories and counts
/api/traffic	GET	Aggregate traffic/usage metrics
/api/snapshot	POST	Trigger snapshot/export of index

Example curl requests

# Search agents
curl "http://localhost:4000/api/search?q=code+assistant&limit=10"

# Get categories
curl "http://localhost:4000/api/categories"

# Get agent details
curl "http://localhost:4000/api/agents/agent-1234"

If the server supports API keys, include an Authorization header:

curl -H "Authorization: Bearer YOUR_API_KEY" "http://localhost:4000/api/traffic?since=2026-01-01"

Use Cases

Build a discovery UI
- Create a searchable frontend that queries /api/search and renders agent cards with metadata and provider links. Filter by category and sort by traffic to highlight popular agents.
Monitor agent traffic and detect anomalies
- Poll /api/traffic or integrate with a metrics pipeline to detect sudden spikes in requests to an agent. Generate alerts or auto-scale downstream resources when popular agents exceed thresholds.
Aggregate and compare capabilities
- Pull metadata for agents in a category (e.g., “code assistants”) and generate comparison tables of supported languages, model backends, and pricing tiers for product research.
Offline analysis and reporting
- Use the snapshot/export endpoint to dump the current index to JSON or CSV for machine learning, analytics, or archival purposes.
Integrate into agent orchestration
- Use the agent metadata to select candidate agents dynamically in orchestration logic (e.g., route a user request to an agent that supports the required capability).

Getting Help and Contributing

Repository: https://github.com/AI-Agent-Hub/ai-agent-marketplace-index-mcp
Open issues and PRs on GitHub for bugs, feature requests, and contributions.
Check the README and example configs in the repo for up-to-date endpoint names and deployment tips.

This MCP server is intended to act as a reliable index and monitoring layer for large collections of AI agents, simplifying discovery, observability, and integration for developer teams.

MCP Server: Search & Monitor 5,000+ AI Agents