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 helena-bioinformatics/noodle-mcp --skill noodle-biomedical-literature-discovery-mcpgit clone --depth 1 https://github.com/helena-bioinformatics/noodle-mcpWrote 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/helena-bioinformatics/noodle-mcp/noodle-biomedical-literature-discovery-mcp)<a href="https://agentmods.dev/skills/helena-bioinformatics/noodle-mcp/noodle-biomedical-literature-discovery-mcp"><img src="https://agentmods.dev/badge/skills/helena-bioinformatics/noodle-mcp/noodle-biomedical-literature-discovery-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/helena-bioinformatics/noodle-mcp/noodle-biomedical-literature-discovery-mcp"><img src="https://agentmods.dev/badge/skills/helena-bioinformatics/noodle-mcp/noodle-biomedical-literature-discovery-mcp.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00062 | $0.00236 |
| Opus 5 | $0.00031 | $0.00118 |
| Sonnet 5 | $0.00012 | $0.00047 |
| Haiku 4.5 | $0.00006 | $0.00024 |
Grade A, and why
noodle-biomedical-literature-discovery-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 11d 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.
What it actually says
Noodle Biomedical Literature Discovery MCP
Use the hosted read-only tools for source-linked biomedical literature claims.
Route natural-language or exact-identifier search to
search_biomedical_literature; PMID and work records to their matching detail
tools; graph exploration to the matching neighborhood tool; and coverage or
freshness questions to get_corpus_summary.
Start graph traversal from a resolved PMID or work ID. Preserve returned edge types and graph provenance, keep a visited set, and stop at a missing neighborhood. Never infer causality from citation or semantic proximity.
Send only public non-sensitive questions and identifiers. Never send patient, private case, clinical-record, credential, or private uploaded content. Verify important scientific conclusions in the linked primary publications.
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.
- 11d ago First seen · 23 lines · 62 tokens per session scan A 5c2e7e6ff4a6
noodle-biomedical-literature-discovery-mcp is a skill published in the GitHub repository helena-bioinformatics/noodle-mcp (0 stars, last pushed 11d ago), licensed Apache-2.0. It adds 62 tokens to every session and 236 once invoked, about $0.0003 per session on Opus 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 2026-08-31.
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Scope what an autonomous research run may claim and what it must never claim, carry limitations verbatim through every summary layer, and treat negative findings as first-class results. Use when writing up results from an autonomous or agent-driven measurement run, when drafting a research summary, README section, or…