MCP Agent Marketplace: Agents Hiring Agents

MCP-based agent marketplaces are emerging where agents discover, evaluate, and hire other agents via standardized tool interfaces. Instead of a human browsing a marketplace UI, an orchestrator agent searches for specialized sub-agents, checks their capabilities via MCP tool listings, negotiates parameters, and delegates tasks -- all programmatically through the Model Context Protocol.

How agent-to-agent discovery works

An orchestrator agent needs a "find me a TikTok analytics agent" capability. It queries a registry (or a web search) for MCP servers that expose TikTok-related tools. Each MCP server advertises its tools with names, descriptions, and input schemas. The orchestrator evaluates which server fits the task, connects, and invokes the appropriate tool.

Discovery via search API

import os, requests, json

SCAVIO_KEY = os.environ["SCAVIO_API_KEY"]

def discover_agents(capability: str) -> list:
    """Search for MCP servers that provide a specific capability."""
    resp = requests.post(
        "https://api.scavio.dev/api/v1/search",
        headers={"x-api-key": SCAVIO_KEY},
        json={
            "query": f"MCP server {capability} github",
            "num_results": 10,
        },
    )
    results = resp.json().get("organic_results", [])
    candidates = []
    for r in results:
        if "github.com" in r.get("link", ""):
            candidates.append({
                "name": r["title"],
                "url": r["link"],
                "description": r["snippet"],
            })
    return candidates

# Orchestrator discovers TikTok analytics agents
agents = discover_agents("tiktok analytics")
print(json.dumps(agents[:3], indent=2))

The orchestrator pattern

class AgentOrchestrator:
    def __init__(self, search_key: str):
        self.search_key = search_key
        self.connected_agents = {}

    def find_agent(self, task_description: str) -> dict:
        """Find the best agent for a task via web search."""
        resp = requests.post(
            "https://api.scavio.dev/api/v1/search",
            headers={"x-api-key": self.search_key},
            json={"query": f"MCP server for {task_description}",
                  "num_results": 5},
        )
        candidates = resp.json().get("organic_results", [])
        # Score by relevance to task
        return candidates[0] if candidates else None

    def delegate_task(self, agent_id: str, task: dict) -> dict:
        """Send task to a connected agent via MCP tool call."""
        agent = self.connected_agents.get(agent_id)
        if not agent:
            return {"error": "Agent not connected"}
        # In practice: invoke agent's MCP tool with task params
        return {"status": "delegated", "agent": agent_id, "task": task}

orchestrator = AgentOrchestrator(os.environ["SCAVIO_API_KEY"])
agent = orchestrator.find_agent("scrape product reviews")
print(f"Found agent: {agent['title'] if agent else 'none'}")

Real marketplace examples in 2026

• Smithery.ai: registry of MCP servers with install commands
• MCP Hub: curated directory of production-ready MCP servers
• GitHub MCP topic: 2,000+ repositories tagged with MCP server implementations
• SwarmWage: on-chain agent marketplace where agents bid on tasks

What search enables in the marketplace

Without search, an orchestrator agent can only use pre-configured sub-agents. With search, it can discover new agents at runtime. A research task that needs TikTok data, Google Maps results, and Reddit sentiment can dynamically find and connect to three specialized MCP servers without any hardcoded integrations.

Security considerations

Agents hiring agents introduces supply chain risk. An orchestrator that connects to an unknown MCP server is trusting arbitrary code. Production implementations need: capability verification (does the agent actually do what it claims), sandboxed execution, budget limits per sub-agent, and audit logging of all cross-agent interactions.

Key takeaway

MCP standardizes how agents expose and consume capabilities. Web search makes agent discovery dynamic rather than static. The combination enables a world where agents compose themselves into task-specific teams at runtime -- but only if you solve the trust and verification problems first.