gns_get_compliance_reportReturns a full EU AI Act compliance report for a GNS agent, including trust score, chain verification, Merkle epoch proofs, delegation certificate, and regulatory status.
| Name | Required | Description | Default |
|---|---|---|---|
| agentHandle | No | GNS handle of the agent (e.g. energy@italy-geiant) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the report's contents (trust score, proofs, etc.) but does not mention behavioral aspects such as read-only nature, authorization requirements, potential errors (e.g., unknown agent), or whether the report is generated on-demand. The verb 'Returns' implies a read operation, but more explicit transparency would be helpful.
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?
The description is a single, well-structured sentence that front-loads the main action ('Returns a full EU AI Act compliance report') and then enumerates key contents. Every word contributes value, with no fluff or repetition.
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?
For a simple one-parameter getter with no output schema, the description is fairly complete: it states the purpose, lists report components, and is supported by complete schema parameter documentation. However, it could additionally mention the absence of real-time guarantees or clarify what 'regulatory status' entails, leaving a small gap.
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?
The input schema has one parameter 'agentHandle' with a full description ('GNS handle of the agent (e.g. energy@italy-geiant)'), so schema coverage is 100%. The tool description does not add parameter details, but the schema already provides sufficient meaning. Baseline of 3 is appropriate since no additional semantic value is needed.
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 uses the specific verb 'Returns' and identifies the resource as a 'full EU AI Act compliance report for a GNS agent', listing concrete components (trust score, chain verification, Merkle epoch proofs, delegation certificate, regulatory status). This clearly distinguishes it from siblings like gns_get_trust_score and gns_verify_chain, which focus on individual aspects.
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 this tool provides a comprehensive report, but it does not explicitly state when to choose it over narrower siblings. For example, it lacks guidance like 'use gns_get_trust_score for just the trust score'. The presence of siblings suggests alternatives, but the description offers no direct comparison or exclusions.
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 distinct purpose within its domain: gns_* tools handle compliance reporting, trust scoring, epoch rolling, and chain verification, while perception_* tools handle tile fetching, classification, embedding, and weather queries. Even similar tools like gns_get_compliance_report and gns_get_trust_score are clearly differentiated by scope (full report vs quick score).
The gns_* tools follow a consistent verb_noun pattern (get_compliance_report, get_trust_score, roll_epoch, verify_chain), but perception_* tools mix styles: perception_fetch_tile is verb_noun, perception_classify and perception_embed are just verbs, and perception_weather is a noun. The two prefixes (gns_ vs perception_) also introduce a split, though each group is internally readable.
Eight tools is well-scoped for a server that combines two related functions: AI-powered earth observation and cryptographic compliance auditing. Each tool serves a distinct role and there are no redundant utilities, making the count feel intentional and complete.
The core workflows are covered: fetch a tile, classify it, embed it (though not yet implemented), and weather queries for context, with a full compliance trail via breadcrumbs, epochs, and verification. The only notable gap is that perception_embed is explicitly marked as not implemented, leaving a placeholder in the tool surface, but the rest of the pipeline is functional.