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Check the premises an action relies on

check_premises

Stage 1 of the pre-action guard. Given the action you are about to take and the PINNED subject ids it relies on, returns allow/hold/deny per premise plus a short-lived lease for each cleared one. Takes subject ids, never a natural-language question — retrieval wording cannot change the verdict. Do the work, then call verify_premises with these leases IMMEDIATELY before acting. Fails closed: an unknown pin, an unreadable record or a premise that cannot be leased all return hold, never allow.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
surfaceNoOptional: the external system the action concerns, forwarded to the guard for observation scoping.
premisesYesThe configured premises the intended action relies on. Pinned ids only; this tool never searches.
intended_actionYesWhat the caller is about to do, as a short stable string (e.g. "reply to customer support email"). Hashed into the lease, so a lease acquired for one action cannot authorize a different one.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

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: it discloses the fail-closed policy ('an unknown pin, an unreadable record or a premise that cannot be leased all return hold, never allow'), the short-lived nature of leases, and that leases are hash-bound to the intended_action so one cannot authorize a different action. That is substantive behavioral context beyond any structured field.

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?

Four tight sentences, front-loaded with the tool's role, then the contract, then the follow-up call, then the failure mode. Every sentence carries information an agent needs.

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?

No output schema exists, yet the description states the return shape (allow/hold/deny per premise plus a lease per cleared premise), the failure semantics, and the downstream step. Nothing needed to invoke or chain this tool correctly is missing.

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 baseline is 3, but the description adds real semantic value: subject_id must be a pinned SoR id and never a natural-language query, and intended_action is hashed into the lease so it must be a stable string. Those constraints shape how an agent should populate the fields.

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?

States a specific verb and resource ('returns allow/hold/deny per premise plus a short-lived lease') and explicitly frames itself as 'Stage 1 of the pre-action guard', which cleanly separates it from the sibling verify_premises (stage 2). An agent can distinguish it from every sibling without opening a 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?

Gives explicit sequencing: do this work, then 'call verify_premises with these leases IMMEDIATELY before acting'. It also names the condition that selects the input shape ('Takes subject ids, never a natural-language question'), leaving nothing to inference.

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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