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NikitaDatar

Vector Decisions MCP Server

by NikitaDatar

risk_assessment

Evaluate AI agent deployment risks by inputting operational, ethical, security, and compliance scores to get a comprehensive risk assessment.

Instructions

Comprehensive risk assessment for an AI agent deployment.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agentNameYesName of the AI agent
ethicalRiskYesEthical risk score (0-100)
securityRiskYesSecurity risk score (0-100)
complianceRiskYesCompliance risk score (0-100)
operationalRiskYesOperational risk score (0-100)
deploymentContextYesContext of deployment
Behavior2/5

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

With no annotations provided, the description bears the full responsibility for disclosing side effects and behavior. It only says 'comprehensive risk assessment' without indicating whether this is a read-only operation, whether it returns a score, stores data, or has any side effects. The agent cannot infer safety or operational impact from this minimal text.

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

Conciseness3/5

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

The description is a single sentence, which is concise, but it lacks structure and substance. It does not present key scoping or usage information up front. For a tool with six required parameters and no output schema, this is under-specified rather than appropriately compact.

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

Completeness2/5

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

Given the tool's complexity—six required parameters, no output schema, and no annotations—the description is incomplete. It fails to explain what the tool actually does with the inputs, what the result looks like, or how it relates to sibling tools like compliance_check or assure_action. The agent lacks essential context to call it correctly.

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?

Schema description coverage is 100%, with clear descriptions for all six parameters, including the 0-100 range for risk scores. The description itself adds no additional meaning or context about parameter usage, so this is the baseline score for high coverage. The agent can rely on the schema for parameter semantics.

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 it performs a comprehensive risk assessment for an AI agent deployment, which is a specific and informative purpose. However, it does not differentiate this tool from siblings like compliance_check or gatri_trust_score, which could overlap. The verb 'assessment' and resource 'AI agent deployment' are clear.

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 alternatives. There are no conditions, exclusions, or references to sibling tools. An agent would have to infer when 'comprehensive risk assessment' is needed without any hints about scenarios or prerequisites.

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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