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/agent-rate-limiter-mcp/cursorrulesgit clone --depth 1 https://github.com/CSOAI-ORG/agent-rate-limiter-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/rules/csoai-org/agent-rate-limiter-mcp/cursorrules)<a href="https://agentmods.dev/rules/csoai-org/agent-rate-limiter-mcp/cursorrules"><img src="https://agentmods.dev/badge/rules/csoai-org/agent-rate-limiter-mcp/cursorrules.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 | $0.00149 | $0.00149 |
| Opus 5 | $0.00075 | $0.00075 |
| Sonnet 5 | $0.00030 | $0.00030 |
| Haiku 4.5 | $0.00015 | $0.00015 |
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 3d 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
Agent Rate Limiter MCP - Auto-trigger Rules
When the user asks about fleet-wide rate limiting for agents, shared quota across MCPs, abuse prevention, or tier-aware throttling (free/pro/enterprise), use agent-rate-limiter-mcp tools:
- check_rate_limit: Verify if api_key + scope is within quota
- record_call: Decrement the quota counter
- get_remaining_quota: Show remaining calls for current period
- reset_quota: Admin-only reset (Enterprise)
Install: pip install agent-rate-limiter-mcp
Solves a real bug: hostile user hitting 10 MCPs separately gets 10x quota. This MCP gives shared fleet-wide counters.
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.
- 3d ago First seen · 13 lines · 149 tokens per session scan A cf1bbe193c25
cursorrules is a cursor rule published in the GitHub repository CSOAI-ORG/agent-rate-limiter-mcp (1 stars, last pushed 2mo ago), licensed MIT. It adds 149 tokens to every session, about $0.0007 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 contract review, use contract-review-ai-mcp tools: analyzecontract, extractclauses, identifyrisks, comparecontracts, summarizecontract.
cursorrules
When the user asks about COBOL, legacy modernization, mainframe migration, banking IT, COBOL parser, use cobol-bridge-mcp tools.
cursorrules
When the user asks about churn predictor, use churn-predictor-ai-mcp tools: predictchurn, batchpredictchurn, getcustomerrisk, trackcustomer, updatecustomersignals.
cursorrules
When the user asks about ad copy, use ad-copy-ai-mcp tools: generateadcopy, generatevariants, createcampaign, getcampaign, addcreative.
cursorrules
When the user asks about accessibility, use accessibility-ai-mcp tools: checkcolorcontrast, suggestalttext, checkheadinghierarchy, ariavalidator.
cursorrules
When the user asks about api docs generator, use api-docs-generator-ai-mcp tools: generateendpoint, generateschema, generatefullspec, addauthtospec, validatespec.