get_compliance_rulesFetch LaunchTrust's sourced, founder-reviewed compliance rule snapshots, optionally filtered to one jurisdiction code (e.g. eu-ai-act-50, gdpr, ca-sb243). Compliance aid, not legal advice.
| Name | Required | Description | Default |
|---|---|---|---|
| jurisdiction | No | Optional jurisdiction code to filter by, e.g. gdpr or eu-ai-act-50. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety and idempotency. The description adds context ('sourced, founder-reviewed' and 'Compliance aid, not legal advice'), providing mild behavioral insight without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with front-loaded action and result. Every word serves a purpose; no fluff. Highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (one optional parameter, no output schema). The description covers the source and nature of the data, plus a disclaimer. It is adequate for an agent to understand the tool's role and safe use. However, it could mention that the output is a list of snapshots (implied but not explicit).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the parameter is fully documented. The description adds examples ('eu-ai-act-50, gdpr') echoing the schema's examples, providing marginal additional context. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action ('Fetch') and resource ('compliance rule snapshots'), with optional jurisdiction filtering. It distinguishes from siblings like 'list_jurisdictions' (listing codes) and 'get_market_report' (market data), establishing a unique purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool (to get compliance rules) and the filter option, but does not explicitly state when not to use it or point to alternatives among siblings. For example, it doesn't mention that 'list_jurisdictions' is for getting available codes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: retrieving rules vs jurisdictions vs scan results vs triggering scans vs verification. No overlap in functionality.
All tool names follow a consistent verb_noun (snake_case) pattern: get_, list_, register_, scan_, verify_. Conventions are uniform throughout.
9 tools cover the essential operations for compliance scanning: registration, scanning (full and quick), results retrieval, history, and verification. The scope feels well-calibrated.
Core CRUD-like coverage is present: register, scan, read results, verify. Missing update/delete for apps or scans is a minor gap but not critical for the domain's primary workflow.