Tool Action Allowlist Configuration Prompt Template

This prompt is for platform operators and security engineers who need to generate a structured, machine-readable allowlist that constrains which tools an agent can invoke, what operations are permitted, and what argument boundaries apply. Use it when you are deploying a tool-bearing agent into production and need an explicit allowlist declaration that can be enforced at the policy layer, fed into a pre-execution gate, or embedded into a system prompt. The primary job-to-be-done is converting a human-readable security policy into a configuration artifact that a deterministic enforcement layer can consume without ambiguity.

The ideal user has already completed a threat model for their agent's tool surface and knows which tools, operations, and argument ranges are necessary for the agent's task. You should have a list of available tools with their full operation signatures, a clear understanding of the data classification for each resource the tools touch, and a defined default-deny posture. This prompt works best when the tool ecosystem is relatively stable—frequent tool additions or schema changes will require regenerating and revalidating the allowlist, making it less suitable for highly dynamic, self-discovering tool registries.

This prompt is not a substitute for runtime enforcement code. It produces the configuration artifact that your enforcement layer consumes. Do not use this prompt when you need a dynamic runtime RBAC evaluator, a real-time permission negotiation protocol, or a system that adapts permissions based on user identity or session context. It is a configuration generator, not a runtime decision engine. For environments where tool access must change per-user or per-session, pair this allowlist with a session-scoped policy overlay rather than attempting to encode all permutations into a single static declaration.

Before using this prompt, ensure you have a clear inventory of every tool the agent could potentially discover or be instructed to call. An incomplete tool list will produce an allowlist with dangerous gaps. After generation, always validate the output against your denylist rules to detect conflicts—an operation that appears in both the allowlist and an implicit denylist indicates a policy specification error that must be resolved before deployment. The next step after generating this configuration is to feed it into your pre-execution gate and run the eval suite described in the full playbook to verify that blocked operations are indeed rejected and permitted operations proceed with the correct argument constraints.

This prompt template produces a structured allowlist declaration that defines exactly which tools an agent may invoke, what operations are permitted on each tool, and what argument and scope constraints apply. It is designed for platform operators and security engineers who need to convert a tool registry and policy document into a machine-readable allowlist that can be validated, versioned, and enforced at runtime. The prompt also includes built-in validation checks for allowlist completeness and conflict detection against any existing denylist rules.

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You are a tool access policy compiler. Your task is to produce a structured allowlist declaration from the provided tool registry, scope definitions, and policy constraints.

## INPUTS
- Tool Registry: [TOOL_REGISTRY]
- Scope Definitions: [SCOPE_DEFINITIONS]
- Policy Constraints: [POLICY_CONSTRAINTS]
- Existing Denylist (if any): [DENYLIST]
- Risk Level: [RISK_LEVEL]

## OUTPUT SCHEMA
Return a JSON object with the following structure:
{
  "allowlist_version": "string",
  "generated_at": "ISO8601 timestamp",
  "tools": [
    {
      "tool_name": "string",
      "permitted_operations": ["operation_name"],
      "argument_constraints": {
        "argument_name": {
          "type": "string",
          "allowed_values": ["value"],
          "pattern": "regex",
          "range": {"min": number, "max": number}
        }
      },
      "scope_limits": {
        "max_items": number,
        "rate_limit_per_minute": number,
        "resource_patterns": ["glob_pattern"]
      },
      "requires_approval": boolean,
      "approval_threshold": "string describing when human approval is required"
    }
  ],
  "validation_checks": {
    "completeness_errors": ["string"],
    "denylist_conflicts": ["string"],
    "missing_tools": ["string"]
  },
  "policy_violations": ["string describing any policy rules that could not be satisfied"]
}

## CONSTRAINTS
- Only include tools that appear in the Tool Registry.
- For each tool, only permit operations explicitly listed in Scope Definitions.
- Apply argument constraints as specified in Policy Constraints.
- If a tool or operation appears in the Denylist, flag it in denylist_conflicts and do not include it in the allowlist.
- For Risk Level "high", set requires_approval to true for any write, delete, or administrative operation.
- If a tool in the Tool Registry has no permitted operations after applying constraints, list it in missing_tools.
- Do not invent tools, operations, or constraints not present in the inputs.

## EXAMPLES
[EXAMPLES]

Generate the allowlist declaration now.

To adapt this template, replace each square-bracket placeholder with your actual data. [TOOL_REGISTRY] should contain the full inventory of available tools with their operation signatures. [SCOPE_DEFINITIONS] should specify which operations are in scope for the agent's assigned role. [POLICY_CONSTRAINTS] should include argument-level restrictions such as allowed enum values, regex patterns, and numeric ranges. If you maintain a separate denylist, populate [DENYLIST] with tool-operation pairs that are explicitly forbidden. Set [RISK_LEVEL] to "low", "medium", or "high" to control the approval threshold behavior. The [EXAMPLES] placeholder should contain one or two few-shot examples of correctly formatted allowlist entries to guide output consistency. After generation, always run the validation_checks output through an automated validator before loading the allowlist into your agent's enforcement layer. For high-risk deployments, require human review of the generated allowlist before it goes live.

The Tool Action Allowlist Configuration Prompt is designed to be called programmatically by a Tool Authorization Service that sits between the agent's planning layer and the tool execution layer. Rather than embedding the prompt directly in the agent's system instructions, deploy it as a dedicated pre-flight check: before any tool invocation, the service extracts the proposed tool name, operation, and arguments from the agent's tool call, packages them into the [TOOL_CALL_REQUEST] placeholder, and sends the prompt to a fast, low-cost model (such as GPT-4o-mini or Claude Haiku). The model returns a structured allowlist decision that your service enforces—blocking, allowing, or flagging the call for human review. This separation ensures that allowlist logic cannot be overridden by prompt injection into the agent's own context.

Validation and enforcement: Parse the model's JSON output and validate it against a strict schema before acting on it. Required fields include decision (enum: allow, deny, escalate), matched_allowlist_entry, violated_constraints (array of strings), and audit_reference. If the model returns deny, your service must block the tool call and return the violated_constraints to the agent as a structured error—never as raw model output that could be manipulated. For escalate decisions, queue the request for human approval with a timeout; if the timeout expires, default to deny. Retry logic: If the model returns malformed JSON or fails schema validation, retry once with the same prompt plus the validation error message appended. If the second attempt fails, log the failure and default to deny for safety. Caching: Cache allowlist decisions for identical (tool_name, operation, argument_signature) tuples with a short TTL (60-300 seconds) to reduce latency and cost during bursty agent activity. Invalidate the cache immediately when allowlist rules are updated.

Model choice and latency budget: Use a fast, instruction-following model for this prompt. The allowlist check is on the critical path of every tool call, so target sub-500ms response times. Avoid large models that add latency without meaningful accuracy gains for this structured classification task. Logging and audit trail: Log every decision—including the full prompt, model response, validation result, and enforcement action—to an append-only audit store. Include tool_call_id, session_id, user_id, and timestamp in every log entry. This audit trail is essential for security investigations, compliance reviews, and debugging false-positive denials. Integration pattern: Expose the authorization service as a gRPC or REST endpoint that your agent framework calls synchronously before every tool invocation. For agent frameworks that support middleware or interceptors (LangChain, CrewAI, custom loops), register the authorization check as a pre-tool hook to avoid scattered enforcement logic. What to avoid: Do not embed the allowlist prompt directly in the agent's system prompt—this creates a single point of injection vulnerability. Do not skip validation of the model's output; a hallucinated allow decision is a direct path to unauthorized tool execution. Do not use this prompt for runtime tool discovery or capability description; it assumes the allowlist rules are already defined and only needs to evaluate a specific invocation against them.