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.
git clone --depth 1 https://github.com/gondola-ai/gondola-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/commands/gondola-ai/gondola-mcp/hotel-points)<a href="https://agentmods.dev/commands/gondola-ai/gondola-mcp/hotel-points"><img src="https://agentmods.dev/badge/commands/gondola-ai/gondola-mcp/hotel-points/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/commands/gondola-ai/gondola-mcp/hotel-points"><img src="https://agentmods.dev/badge/commands/gondola-ai/gondola-mcp/hotel-points.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 | $0.00015 | $0.00171 |
| Opus 5 | $0.00008 | $0.00086 |
| Sonnet 5 | $0.00003 | $0.00034 |
| Haiku 4.5 | $0.00002 | $0.00017 |
Grade A, and why
hotel-points 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 12d 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
Hotel points search
Use the Gondola MCP search_hotels tool to find hotels for: $ARGUMENTS
Then for the strongest options, call compare_rates to show the cash price and the award
(points) price side by side, with the cents-per-point value for each redemption. Rank by
whichever delivers the best value, and call out any night where points clearly beat cash (or vice
versa). If the user is signed in, factor in their loyalty balances (get_loyalty_accounts) and note
which stays they can actually afford on points today.
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.
- 12d ago First seen · 16 lines · 15 tokens per session scan A 64e9ddff214f
hotel-points is a command published in the GitHub repository gondola-ai/gondola-mcp (8 stars, last pushed 17d ago), licensed MIT. It adds 15 tokens to every session and 171 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.
Other commands, from other repositories
search-flights
Search award flight availability across airlines.
compare-programs
Compare loyalty program award charts and rates.
analyze-bond-rv
Analyze a bond's relative value vs yield curves and credit spreads with scenario stress testing.
valuation-methods
Valuation methods analysis — multiples, DCF inputs, PEG integration, valuation assumption extraction.
merit-reconcile
Preview or check the status of Stripe → Merit payout reconciliation (read-only).
audit-checklist
Perform an internal audit, review controls, or prepare for an external financial audit.