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Deterministic research MCP server on FastMCP 3 — 5-engine web search, 9-platform social search, 6 academic DBs, news aggregation, entity profiles, conflict detection, document analysis. No API keys. No in-server LLM. Structured outputs for agent chaining.
RivalSearchMCP is an MCP server entry in ChatHome Skills, sourced from GitHub MCP Search and scored 38/100. Deterministic research MCP server on FastMCP 3 — 5-engine web search, 9-platform social search, 6 academic DBs, news aggregation, entity profiles, conflict dete. As a Model Context Protocol server, it exposes tools that MCP-compatible LLM clients such as Claude, Cursor, and Copilot can call directly, extending an agent's reach beyond its built-in capabilities. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding RivalSearchMCP to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
git clone https://github.com/damionrashford/RivalSearchMCP.gitReview the upstream source before running this command.
Downloads a script and pipes it straight into a shell, executing remote code with no review.
curl -LsSf https://astral.sh/uv/install.sh | shDispute this flag?Scan coverage: Install metadata and repository README.
Automated signals from install metadata and a bounded skill body — not a full code security audit and not a recursive source or dependency review.
{
"fullName": "damionrashford/RivalSearchMCP"
}Deterministic research MCP server on FastMCP 3 — 5-engine web search, 9-platform social search, 6 academic DBs, news aggregation, entity profiles, conflict detection, document analysis. No API keys. No in-server LLM. Structured outputs for agent chaining.