Get the current TierGate trust tier and score for an agent. Tiers: provisioned (0%) → observed (25%) → trusted (60%) → certified (85%) → sovereign (99%). Omit agent_pk to query the server's own agent.
| Name | Required | Description | Default |
|---|---|---|---|
| agent_pk | No | Ed25519 public key (64 hex chars). Omit for own agent. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of explaining behavior. It discloses the possible output tiers and percentages, and clarifies the meaning of omitting agent_pk. This adds valuable context about what the tool returns and how input affects behavior, though it doesn't explicitly state read-only status or error handling.
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 two sentences: the first is a clear purpose statement, the second adds the tier scale and a usage tip. It is front-loaded, concise, and every sentence contributes meaningful information without redundancy.
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 tool with one optional parameter and no output schema, the description covers the core purpose, the range of possible return values (tiers and percentages), and the key input behavior. It could specify the exact return format or error conditions, but for this simplicity it is largely complete.
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 100% description coverage for agent_pk, including its format and omission behavior. The tool description repeats the omission instruction but adds no new parameter-level meaning beyond what the schema already provides, so the baseline of 3 applies.
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 'Get the current TierGate trust tier and score for an agent' with a specific verb and resource. It uniquely identifies the tool among siblings like 'gns_get_compliance_report' by focusing on trust tier/score, and even lists the tier progression to disambiguate further.
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 provides a clear usage instruction: 'Omit agent_pk to query the server's own agent.' This gives parameter-specific guidance and implies when the tool is appropriate (querying trust scores). However, it does not explicitly mention alternatives or when not to use it, so it falls short of a 5.
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.