Skip to main content
Glama
andronaft

health-os

approve_staged_source

DestructiveIdempotent

Approve a staged lab panel on explicit user instruction, moving it from pending to approved. Refuses numeric values with unconvertible units unless missing canonical values are allowed.

Instructions

Approve a staged panel (pending→approved). ONLY on an explicit instruction from the user in the current message — do not call right after stage_lab_panel. Refuses when numeric values have an unconvertible unit (they'd be invisible to trends); allow_missing_canonical=true only if the user accepts that.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
source_idYes
allow_missing_canonicalNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.5/5.0
Behavior4/5

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

Annotations cover the safety profile (destructive, idempotent, non-read-only), so the bar is lower, yet the description still adds real behavior: it will refuse panels with unconvertible units and hides them from trends. It does not spell out post-approval effects on downstream data, keeping it short of a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three dense sentences, front-loaded with the action and state change, then the critical gating rule, then the parameter caveat. No filler, though the parenthetical aside slightly interrupts flow.

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

Completeness4/5

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

Output schema exists so return values need no explanation, and the description covers the confirmation requirement, the refusal condition, and the one non-obvious parameter. Complete enough to call safely; only minor downstream-effect detail is absent.

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 0%, so the description carries the burden. It explains allow_missing_canonical's consequence ('only if the user accepts that' the values are invisible to trends), which is meaningful beyond the bare boolean. source_id is left implicit but is self-evident from the tool's subject.

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 plus the state transition (pending→approved), which an agent cannot get from the name alone. It also implicitly differentiates from the staging sibling by naming stage_lab_panel in the guidance.

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 an explicit precondition ('ONLY on an explicit instruction from the user in the current message') and an explicit exclusion ('do not call right after stage_lab_panel'). This is exactly the when/when-not guidance the dimension rewards.

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