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pre_action_check

Check whether a proposed actor action on a target is ready to proceed by reviewing claims, evidence, risk tier and approvals; get continue, request evidence, require approval or stop.

Instructions

Choose this for an unsigned readiness check of a specific actor, intended action and target. Pass action_request with claim evidence, risk tier, policy checks and any external approval reference. Returns continue, request_evidence, require_approval or stop, with gaps and reasons. Use agent_output_verification for an answer-only claim review. Resubmit the same run_id after adding evidence or approval. Does not authenticate, authorize, enforce, persist state, issue a signed receipt or perform the action.

Input Schema

NameRequiredDescriptionDefault
action_requestYesStructured pre-action check request. Call get_schema('pre_action_check_request') for the full nested contract.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.23.0
    • addedInput schema / properties / action_request / properties / topic
      Added value: +{
      +  "type": "string"
      +}
  2. Addedv1.5.0

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so well: it lists the four possible outcomes, mentions gaps and reasons, explains resubmission behavior, and clearly states the tool does not authenticate, authorize, enforce, persist, issue signed receipts, or perform the action.

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?

The description is front-loaded with the selection condition and input requirement, then returns, alternatives, resubmission, and limitations. Every sentence adds operational value and there is no filler.

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

Completeness5/5

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

Given the nested input object, lack of annotations, and no output schema, the description is complete enough for correct invocation: it covers purpose, composition of action_request, possible outcomes, alternative tool, retry behavior, and explicit non-capabilities.

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

Parameters4/5

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

Schema coverage is 100%, so the schema already defines the nested action_request contract. The description adds useful semantic framing by listing key pieces such as claim evidence, risk tier, policy checks, approval reference, and the run_id resubmission behavior, though it does not fully document the nested fields itself.

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

Purpose5/5

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

The description states a specific verb and resource: an unsigned readiness check for a specific actor, intended action, and target. It also distinguishes the tool from agent_output_verification, so an agent can route correctly without opening the schema.

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

Usage Guidelines5/5

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

It says to choose this tool for readiness checks, names agent_output_verification as the alternative for answer-only claim review, and explains that the same run_id should be resubmitted after adding evidence or approval. This gives explicit selection and follow-up guidance.

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