This is a library/framework for gating tool calls, not a traditional MCP server. Tool definitions are present but lack rigor. Naming is inconsistent (gateToolCall vs gateOpenAIToolCall vs createIntentGate, mixing verbs and domains). Descriptions are present but generic (e.g., 'Gate a generic tool call through the intent gate' repeats the tool name without explaining WHEN or WHY to use it). Input schemas exist but are sparse: many parameters lack type constraints, enums, or validation guidance. Output schemas are not documented. Error handling is minimal, no recovery guidance or categorization. The library is well-intentioned but does not meet production tool quality standards for LLM consumption.

• Rewrite tool descriptions to answer: WHAT does it do? WHEN should I call it? WHAT does it return? Example: 'Evaluate a tool call against configured policies to determine if it should be allowed, blocked, or require human approval. Use this to gate any tool invocation before execution. Returns a decision (allowed|blocked|requires_approval) and an optional executable command.'
• Add explicit output schema documentation to each gate* tool. Define the return object: { decision: { outcome: 'allowed'|'blocked'|'requires_approval', reason?: string }, intent: { id: string, type: string, ... }, command?: string }.
• Constrain the 'args' parameter. Instead of bare 'object', document expected structure or accept a JSON string with validation. Add 'requestedCapabilities' as an enum array with valid capability names.
• Document error cases and recovery paths. E.g., 'If no policy matches, the fallbackDecision is returned. If policies fail to evaluate, an error is raised with the policy name and reason, retry or escalate to human review.'
• Clarify the role of createIntentGate and createPolicy as configuration/setup functions, not runtime tools. Consider renaming or adding a prefix (e.g., 'setup_intent_gate', 'setup_policy') to signal they are initialization helpers, not direct agent actions.