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/liquidz00/jamfmcpWrote 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/liquidz00/jamfmcp/jamf-api-best-practices)<a href="https://agentmods.dev/rules/liquidz00/jamfmcp/jamf-api-best-practices"><img src="https://agentmods.dev/badge/rules/liquidz00/jamfmcp/jamf-api-best-practices/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/rules/liquidz00/jamfmcp/jamf-api-best-practices"><img src="https://agentmods.dev/badge/rules/liquidz00/jamfmcp/jamf-api-best-practices.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 | $0.00011 | $0.00692 |
| Opus 5.5 | $0.00004 | $0.00277 |
| Sonnet 5.5 | $0.00002 | $0.00138 |
| Haiku 4.5 | $0.00001 | $0.00069 |
Grade A, and why
jamf-api-best-practices 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Jamf API Integration and AI-Compatible Development
Jamf API Best Practices
- Reference the official Jamf Pro Developer documentation for all API usage:
- Include links to relevant Jamf docs in comments or commit messages when implementing or updating API calls
- Prefer the Pro API over Classic API when possible, unless a feature is only available in the Classic API
- Follow authentication, pagination, and rate-limiting guidance as described in Jamf documentation
OpenAPI Schema Usage
- Consult the official Jamf OpenAPI schema to understand expected request and response formats
- Parse or reference field types and structures from the schema to validate payloads
- Generate response models with Pydantic where possible to support explicit typing and schema validation
Error Handling
- Catch only specific, expected exceptions:
- requests.exceptions.HTTPError
- requests.exceptions.ConnectionError
- json.JSONDecodeError
- Custom exceptions like JamfApiError
- Avoid general
except Exceptionblocks unless absolutely necessary - If a general exception is used:
- Document the reasoning in a code comment
- Log the full exception trace and request context
- Never use bare
except:blocks - Use structured logging for error traceability and AI observability
AI Integration & Tooling Compatibility
- Write clear, composable functions with well-defined inputs and outputs
- Avoid hidden state and side effects where possible
- Use consistent naming conventions to help with code interpretation by LLMs and agents
- Annotate interfaces with type hints and docstrings to improve discoverability
- Design modules and utilities to be used independently or chained together as AI “playbooks”
- Document each function’s purpose, expected input shape, and output format for downstream use
Observability and Logging
- Use Python’s
loggingmodule with structured output (e.g., JSON or key-value logs) - Include contextual metadata in logs (e.g., API endpoint, serial number, device ID)
- Avoid printing sensitive information (e.g., tokens, user emails)
- Ensure logs can be parsed and analyzed by external tools or AI agents
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.
- 1mo ago First seen · 68 lines · 11 tokens per session scan A 6d9b2151957b
jamf-api-best-practices is a cursor rule published in the GitHub repository liquidz00/jamfmcp (8 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 11 tokens to every session and 692 once invoked, about $0.0000 per session on Opus 5.5. 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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