# MetaSearchMCP

> MetaSearchMCP — gefsikatsinelou-metasearchmcp. Use this tool when you need to integrate a scalable and robust metasearch capability into your LLM agent workflows, aggregating results from multiple search engines and providing structured JSON output. It solves problems of search result duplication, provider fallback, and data consistency, making it ideal for applications requiring comprehensive and reliable search functionality. With support for SerpBase, Serper, and SearXNG, MetaSearchMCP provides a flexible and open-source solution for building advanced search systems.

Canonical page: https://skillsregistry.net/skills/gefsikatsinelou-metasearchmcp  
JSON: https://api.skillsregistry.net/v1/skills/gefsikatsinelou-metasearchmcp

## Description

Open-source metasearch backend, MCP server, and AI search API for LLM agents. Python FastAPI search gateway with Google search via SerpBase and Serper, multi-engine search aggregation, structured JSON output, provider fallback, deduplication, and SearXNG alternative architecture for agent workflows.

## Trust

- **Trust score (0–1):** 0.36
- **Verification tier:** scanned
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** ai-ml
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/gefsikatsinelou/MetaSearchMCP)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "gefsikatsinelou-metasearchmcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/gefsikatsinelou-metasearchmcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/gefsikatsinelou-metasearchmcp/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt