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NikitaDatar

Vector Decisions MCP Server

by NikitaDatar

gatri_trust_score

Evaluate an AI agent's trustworthiness by computing a GATRI trust score based on governance, accountability, transparency, robustness, and inclusivity metrics.

Instructions

Compute GATRI trust score for an AI agent. Evaluates Governance, Accountability, Transparency, Robustness, and Inclusivity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agentNameYesName of the AI agent to evaluate
governanceYesGovernance score (0-100)
robustnessYesRobustness score (0-100)
inclusivityYesInclusivity score (0-100)
transparencyYesTransparency score (0-100)
accountabilityYesAccountability score (0-100)
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only says 'Compute' (implying a read-only calculation) but does not mention the return format, potential side effects, or any authentication/permission requirements. This is a minimal disclosure for a tool that likely just returns a score.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences deliver the core action and the evaluated dimensions. There is zero fluff, and the purpose is front-loaded. All information is essential.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple compute tool, the description is adequate but not complete. It lacks an explicit statement of the return value (e.g., a numeric score) and does not mention any edge cases or error behavior. Given the 6 required parameters are fully documented in the schema, the main gap is the absence of output description, but this is a minor omission for such a straightforward calculation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers 100% of the parameters with clear descriptions and ranges, so the baseline is 3. The description only lists the five dimension names without adding any additional meaning, such as weighting, aggregation logic, or expected values beyond the 0-100 ranges already in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific verb ('Compute') and a specific resource ('GATRI trust score for an AI agent'), and names the five dimensions evaluated. It is not a tautology and is easy to distinguish from siblings like kill_switch or decide_action, even though it doesn't explicitly name them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus the sibling tools (compliance_check, risk_assessment, etc.). It does not state any prerequisites, exclusions, or typical call conditions, leaving the agent to infer when a GATRI trust score is appropriate.

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

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