by gefsikatsinelou · MCP Server · ★ 50
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
MetaSearchMCP Open-source metasearch backend for MCP, AI agents, and LLM workflows. MetaSearchMCP aggregates results from multiple search providers, normalizes them into a stable JSON schema, and exposes both an HTTP API and an MCP server for agent tooling. Positioning MCP-first metasearch backend Structured search API for AI pipelines Multi-provider search orchestration with deduplication and fallback Python FastAPI alternative to browser-first metasearch projects Why It Exists Most search aggregators are designed around browser UX: HTML pages, pagination, and interactive result cards. Agents and LLM workflows need a different contract: predictable JSON, stable field names, partial-failure tolerance, and provider-level execution metadata. MetaSearchMCP is built for that machine-consumable workflow. The design is centered on search orchestration, normalized contracts, and MCP integration.
| Stars | 50 |
| Forks | 2 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 71.5018219839752/100 |
| Open Issues | 2 |
| Last Updated | 2026-07-02 |
| Created | 2026-04-14 |
| Platforms | browser, mcp, python |
| Est. Tokens | ~16k |
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MetaSearchMCP is 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. It is categorized as a MCP Server with 50 GitHub stars.
MetaSearchMCP is primarily written in Python. It covers topics such as agent-tools, ai-agents, google-search.
You can find installation instructions and usage details in the MetaSearchMCP GitHub repository at github.com/gefsikatsinelou/MetaSearchMCP. The project has 50 stars and 2 forks, indicating an active community.
MetaSearchMCP is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to MetaSearchMCP on Agent Skills Hub include gopher-mcp, oxylabs-mcp, cortex-scout. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.