ai_trust_score

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the burden of behavioral disclosure. It adds context about the score range (0-100) and the components involved, but does not explicitly state whether the tool is read-only, how it retrieves ecosystem signals, or any potential side effects or prerequisites. The lack of safety semantics is a notable gap.

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 highly concise, consisting of two sentences that front-load the core purpose and then provide complementary detail about the score range and inputs. Every sentence earns its place with no redundant or filler content.

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 description gives the score range and factor list, but does not fully explain the input parameters' role or the output structure. Since there is no output schema, the return value is only vaguely implied as a 0-100 score. It is adequate for a simple scoring tool but leaves ambiguity about how parameters are used and what exactly the tool returns as a response.

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 covers all three parameters with basic descriptions, satisfying 100% coverage. However, the tool description adds no additional meaning to the parameters—it does not explain how ai_system_name, organization, or deployment_context influence the trust score. Per rubric, the baseline is 3 when schema coverage is high, and the description does not exceed that.

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 a specific action ('Unified AI trust scoring') and resource ('combining all ecosystem signals'), and explicitly distinguishes itself from sibling tools that focus on individual aspects like governance or compliance. The composite score range and component list further clarify its comprehensive 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 use for obtaining an overall trust score but provides no explicit guidance on when to choose this over specialized sibling tools (e.g., ai_governance_assess). No alternatives, exclusions, or context-specific recommendations are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.