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 agentmods add commands/lucas-servi/kegg-mcp-server-python/kegggit clone --depth 1 https://github.com/Lucas-Servi/kegg-mcp-server-pythonWrote 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/commands/lucas-servi/kegg-mcp-server-python/kegg)<a href="https://agentmods.dev/commands/lucas-servi/kegg-mcp-server-python/kegg"><img src="https://agentmods.dev/badge/commands/lucas-servi/kegg-mcp-server-python/kegg.svg" alt="Measured on agentmods" 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 | $0.00020 | $0.00247 |
| Opus 5 | $0.00010 | $0.00123 |
| Sonnet 5 | $0.00004 | $0.00049 |
| Haiku 4.5 | $0.00002 | $0.00025 |
Grade A, and why
kegg 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 4d 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
KEGG Search
Search the KEGG database for the user's query.
- Determine the most relevant KEGG category from the query:
- Biological processes/signaling → search_pathways
- Gene names/symbols/organisms → search_genes
- Chemical names/formulas → search_compounds
- Enzyme names/EC numbers → search_enzymes
- Reaction descriptions → search_reactions
- Disease names/symptoms → search_diseases
- Drug names/trade names → search_drugs
- Functional modules → search_modules
- Ortholog groups → search_ko_entries
- Sugar structures → search_glycans
- Call the appropriate search tool with:
$ARGUMENTS - Display results as a compact numbered list with IDs and descriptions
- End with: "Say a number or ID to get full details, or refine your search."
If the user picks a number, call the corresponding get_*_info tool for that entry.
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.
- 4d ago First seen · 27 lines · 20 tokens per session scan A 28c36aeebc6f
kegg is a command published in the GitHub repository Lucas-Servi/kegg-mcp-server-python (3 stars, last pushed 21d ago), licensed MIT. It adds 20 tokens to every session and 247 once invoked, about $0.0001 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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