Evidenceline

Compare both rules for one round

compare_rules

Read-onlyIdempotent

Screen a monitoring round under two guideline rules side by side, displaying arithmetic, limits, and exceedance status to support scientist decisions.

Instructions

Screen one monitoring round under BOTH guideline rules side by side, with the arithmetic shown.

For each rule, every limit is listed with the value compared (for a sum, for example '0.038 + 0.019 = 0.057'), the limit, and whether the result is above or not above it. A result equal to the limit is not above it. The tool never picks a rule: choosing one is the scientist's call. When the two rules disagree, the notes say so; the lab values are identical in both columns.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`date`	Yes	Sample date of the round: '2025-09-16', '16 September 2025' or 'Sep 2025'.
`well`	Yes	Monitoring well id, for example 'MB2'.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`date`	Yes
`file`	Yes
`well`	Yes
`notes`	Yes
`rules`	Yes
`lab_report`	Yes
`rules_agree`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 24, 2026

TDQS

A4.3/5.0

Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses key behavior: a result equal to the limit is 'not above', the tool never chooses a rule, disagreements are flagged in the notes, and lab values appear identically in both columns. This is substantive, decision-relevant behavioral disclosure.

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 core purpose and each subsequent sentence covers a necessary caveat: arithmetic display, equality handling, rule selection, and disagreement behavior. It is detailed without being bloated.

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?

With only two well-documented parameters, an output schema, and annotations covering safety, the description needs only to supply the edge-case behavior and decision boundary. It does so thoroughly, making the tool actionable for an agent.

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

Parameters3/5

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

Schema description coverage is 100%: date and well each have their own format descriptions and examples. The prose does not add parameter-level semantics beyond the schema, so the baseline of 3 applies.

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 opens with 'Screen one monitoring round under BOTH guideline rules side by side,' clearly naming the resource (a monitoring round) and the operation (comparison under both rules). The detail about per-rule limits and arithmetic separates it from sibling tools like lookup_limit or get_results.

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

Usage Guidelines3/5

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

The 'BOTH rules' phrasing implies this tool is for side-by-side comparison, but the description never explicitly says when to use it instead of alternatives such as get_results or lookup_limit. The note that the tool never picks a rule is useful context but is not a usage guideline.

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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