Ground Hermes Agent with Live Search

The Problem

Hermes Agent's self-improving skills are powerful but grounded in the LLM's training data by default. When a skill creates a research report or comparison, it draws on potentially outdated information. A skill that worked well last month might produce stale recommendations today because competitor pricing changed, a tool released a new version, or a company was acquired.

The Scavio Solution

Connect Hermes Agent to Scavio's MCP server so that research skills can pull live search data before generating outputs. Configure a research profile that uses the MCP connection for web search, Reddit sentiment, and product data. When Hermes creates or improves a skill, the search grounding ensures its outputs reflect current information rather than training data snapshots. The MCP server exposes 11 tools covering Google, Reddit, YouTube, Amazon, and Walmart, so the agent can select the appropriate platform based on what it is researching.

Who It Is For

Developers running Hermes Agent for research, market intelligence, or content generation who need outputs grounded in current data rather than training snapshots.

Key Benefits

• Live web data grounds Hermes skill outputs in current facts
• MCP connection requires no custom code, just config
• 11 search tools auto-discovered by the agent
• Research skills self-improve with verified information
• Works with Hermes Agent's profile system for context separation

Python Example

# Hermes Agent MCP configuration (hermes.config.json)
# Add to your research profile's MCP servers:
#
# {
#   "profiles": {
#     "research": {
#       "mcp_servers": [
#         {
#           "name": "scavio-search",
#           "url": "https://mcp.scavio.dev/mcp",
#           "auth": { "type": "header", "key": "x-api-key", "value": "$SCAVIO_API_KEY" }
#         }
#       ]
#     }
#   }
# }

import requests, os
H = {'x-api-key': os.environ['SCAVIO_API_KEY']}

def ground_research(topic: str) -> str:
    resp = requests.post('https://api.scavio.dev/api/v1/search', headers=H,
        json={'platform': 'google', 'query': f'{topic} 2026'}, timeout=10)
    results = resp.json().get('organic', [])[:5]
    return '\n'.join(f"- {r['title']}: {r['snippet']}" for r in results)

JavaScript Example

// MCP config for Hermes Agent research profile
// Add to mcp_servers array:
// { name: 'scavio-search', url: 'https://mcp.scavio.dev/mcp',
//   auth: { type: 'header', key: 'x-api-key', value: process.env.SCAVIO_API_KEY } }

async function groundResearch(topic) {
  const resp = await fetch('https://api.scavio.dev/api/v1/search', {
    method: 'POST',
    headers: { 'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json' },
    body: JSON.stringify({ platform: 'google', query: `${topic} 2026` })
  });
  const data = await resp.json();
  return (data.organic || []).slice(0, 5).map(r => `- ${r.title}: ${r.snippet}`).join('\n');
}

Platforms Used