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 rules/csoai-org/explainability-report-mcp/cursorrulesgit clone --depth 1 https://github.com/CSOAI-ORG/explainability-report-mcpWhat 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 | $0.00081 | $0.00081 |
| Opus 5 | $0.00041 | $0.00041 |
| Sonnet 5 | $0.00016 | $0.00016 |
| Haiku 4.5 | $0.00008 | $0.00008 |
Grade A, and why
cursorrules 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 yesterday.
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
explainability-report-mcp - Auto-trigger Rules
When the user asks about explainability report, use explainability-report-mcp tools: quick_scan, generate_model_card, explain_decision, transparency_audit, create_impact_assessment
MCP server for explainability report mcp operations
Install: pip install explainability-report-mcp
By MEOK AI Labs — MIT licensed.
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.
- yesterday First seen · 10 lines · 81 tokens per session scan A fc4ad7721fc9
cursorrules is a cursor rule published in the GitHub repository CSOAI-ORG/explainability-report-mcp (1 stars, last pushed 2mo ago), licensed MIT. It adds 81 tokens to every session, about $0.0004 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.
Other cursor rules, from other repositories
cursorrules
When the user asks about iso 27001, use iso-27001-ai-mcp tools: auditisms, riskassessment, gapanalysis, crosswalktoai, generatesoa.
cursorrules
When the user asks about healthcare ai governance, use healthcare-ai-governance-mcp tools: classifysamd, checkcdsexemption, hipaaaicheck, whohealthaiethics, dualcompliancecheck.
cursorrules
When the user asks about patient safety, use patient-safety-ai-mcp tools: checkdruginteractions, assesspatientrisk, validatedosage, generatesafetyalert, checkallergyconflicts.
cursorrules
When the user asks about database universal, use database-universal-mcp tools: querysql, listtables, describetable, insertrow, exporttocsv.
cursorrules
When the user asks about dependency updater, use dependency-updater-ai-mcp tools: checkoutdated, suggestupdates, checkvulnerabilities, generatelockfile.
cursorrules
When the user asks about habit tracker, use habit-tracker-ai-mcp tools: createhabit, logcompletion, gethabitstreak, gethabithistory, getallhabits.