Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add damionrashford/RivalSearchMCP --skill rival-search-mcpgit clone --depth 1 https://github.com/damionrashford/RivalSearchMCPWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/damionrashford/rivalsearchmcp/rival-search-mcp)<a href="https://agentmods.dev/skills/damionrashford/rivalsearchmcp/rival-search-mcp"><img src="https://agentmods.dev/badge/skills/damionrashford/rivalsearchmcp/rival-search-mcp/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/damionrashford/rivalsearchmcp/rival-search-mcp"><img src="https://agentmods.dev/badge/skills/damionrashford/rivalsearchmcp/rival-search-mcp.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00151 | $0.00798 |
| Opus 5.5 | $0.00060 | $0.00319 |
| Sonnet 5.5 | $0.00030 | $0.00160 |
| Haiku 4.5 | $0.00015 | $0.00080 |
Grade A, and why
rival-search-mcp scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RivalSearchMCP
You have access to 9 research tools via the CLI at scripts/cli.py. Run all commands with uv run scripts/cli.py.
Every tool returns deterministic, auditable output. There is no in-server LLM — you're the one doing the synthesis.
How to invoke tools
uv run scripts/cli.py call-tool <tool_name> --flag value
Available tools
web_search— concurrent search across DuckDuckGo, Bing, Yahoo, Mojeek, Wikipedia. Use for general web queries.social_search— Reddit, Hacker News, Stack Overflow, Dev.to, Medium, Product Hunt, Bluesky, Lobste.rs, Lemmy. Use for community discussions.news_aggregation— Google News, Bing News, The Guardian, GDELT, DuckDuckGo News. Use for current events. Accepts--time-range day|week|month|anytime.github_search— search public GitHub repos. Use for code, libraries, projects.map_website— crawl a site inresearch/docs/mapmode. Use to explore site structure or documentation.content_operations— one tool, six ops (retrieve,stream,analyze,extract,score,find_conflicts). Use to get full page content, rate source quality, or surface disagreements between sources.document_analysis— extract text from PDFs, Word docs, images (image OCR via EasyOCR). Use for document processing.research_topic— end-to-end research workflow for a topic, combining search, content retrieval, and analysis.scientific_research— OpenAlex, CrossRef, arXiv, PubMed, Europe PMC (papers) + Kaggle, HuggingFace, Dataverse, Zenodo (datasets).
When to chain tools
- Found a URL from search? →
content_operations --operation retrieve --url <url> - Want to assess source trust before using results? →
content_operations --operation score --urls '[…]' - Two sources seem to disagree? →
content_operations --operation find_conflicts --urls '[…]' - Found a PDF link? →
document_analysis --url <url> - Need to explore a website? →
map_website --url <url> --mode docs - Need a unified entity profile in one shot? →
research_topic --mode entity --topic "OpenAI"
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 50 lines · 151 tokens per session scan A 62f88d5bfd4d
rival-search-mcp is a skill published in the GitHub repository damionrashford/RivalSearchMCP (134 stars, last pushed today), licensed MIT. It adds 151 tokens to every session and 798 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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