Inter-Agent Policy Evaluator
inter_agent_policy_evaluatorRequire policy identity, subject, action, lifetime and valid decisions; compute the restrictive cap and shared actions; 0.003 USDC.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| policies | Yes |
inter_agent_policy_evaluatorRequire policy identity, subject, action, lifetime and valid decisions; compute the restrictive cap and shared actions; 0.003 USDC.
| Name | Required | Description | Default |
|---|---|---|---|
| policies | Yes |
Changes observed during successful MCP inspections.
Input schema / examplesAdded value: +[
+ {
+ "policies": [
+ {
+ "action": "read",
+ "allowed_actions": [
+ "read"
+ ],
+ "decision": "allow",
+ "expires_at": 4102444800,
+ "policy_id": "p1",
+ "subject": "agent-a"
+ },
+ {
+ "action": "read",
+ "allowed_actions": [
+ "read"
+ ],
+ "decision": "allow",
+ "expires_at": 4102444800,
+ "policy_id": "p2",
+ "subject": "agent-a"
+ }
+ ]
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and a closed world, so the safety profile is covered. The description adds genuinely new context by disclosing a cost (0.003 USDC) and by implying input validation requirements, but it says nothing about what the evaluation returns or how the 'restrictive cap' is derived.
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?
A single dense sentence with no filler; the computational purpose is front-loaded after the requirement clause. The trailing price clause is useful and compact, though the chained clauses make it slightly run-on.
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?
There is no output schema, yet the description never explains the return value of an evaluator whose entire value is its computed result (the restrictive cap and shared actions). For a tool taking a nested array of policy objects with hard min/max constraints, this leaves an agent without enough information to interpret results or anticipate failure modes.
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?
Schema description coverage is 0%, so the description must carry parameter meaning, and it does partially: 'policy identity, subject, action, lifetime and valid decisions' maps onto policy_id, subject, action, expires_at and decision. It leaves allowed_actions, the decision value set, and the minItems=2/maxItems=64 constraints unexplained, so compensation is incomplete.
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 verb 'compute' and the object 'restrictive cap and shared actions' give a partial picture of what the tool actually does (evaluate multiple agent policies together), but the leading clause 'Require policy identity, subject, action, lifetime and valid decisions' describes input validation rather than purpose. It never clearly states it is an inter-agent policy intersection/evaluation tool or how it differs from siblings like tool_call_policy_guard or delegation_scope_guard.
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?
No when-to-use guidance, no stated alternatives, and no condition that selects this tool over the many policy-related siblings. The closest thing to guidance is the requirement list, which is an input constraint, not a usage rule.
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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