Skip to main content
Glama

compare_coverage

Read-onlyIdempotent

Check whether two coverage percentages are comparable by reading the pins and conditions in both scan blocks, returning comparable, not_comparable, or unestablished.

Instructions

Say whether two scans produced numbers that can be compared at all.

Two coverage percentages can differ because the estate moved, because the instrument moved, or because the corpus was collected differently, and the percentages show none of the three. This reads the pins and conditions in both blocks and answers with one of three verdicts.

comparable means nothing that moves the number differs. not_comparable lists which conditions differ, each with what it means, so the reader knows whether to re-run, re-clone or ignore it. unestablished means the blocks do not carry enough to decide -- an unpinned corpus, or an emitter that names no commit -- which is a different situation from a known difference and has a different repair.

Use it before putting two coverage figures in one table. Do not use it to compare findings; it reads conditions, not results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
firstYesThe `coverage` block from one scan result.
secondYesThe `coverage` block from another scan result.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.7.0

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive, so safety is covered. Beyond that, the description discloses the full behavioral model: it reads pins and conditions rather than results, returns one of three named verdicts, explains what each verdict means, and flags that 'unestablished' implies a different repair than a known difference. That is genuine context not available from annotations or the schema.

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?

Front-loaded with the core purpose, then a rationale paragraph, then a verdict taxonomy, then a use/don't-use instruction. Every sentence earns its place, though the multi-line prose format is slightly longer than strictly necessary for a two-parameter tool.

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?

For a read-only comparison tool with an output schema, the description supplies everything an agent needs: purpose, preconditions (pinned corpus, named commit), the meaning of each verdict, and the decision each verdict implies. Nothing material is left ambiguous.

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 coverage is 100%, so the two parameters (`first`, `second`) are already documented as coverage blocks. The description reinforces that it reads 'the pins and conditions in both blocks' but adds no syntax or field-level detail beyond the schema. Baseline 3 is appropriate when the schema carries the parameter burden.

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 clear verb (compare/say whether) and resource (two scans' coverage blocks), and draws a hard boundary between comparing conditions vs comparing findings. The explicit distinction 'Do not use it to compare findings; it reads conditions, not results' separates it from any result-diffing sibling.

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 when-to-use ('before putting two coverage figures in one table') and an explicit when-not-to-use ('do not use it to compare findings'). It even decomposes the three verdicts into decision-relevant repair actions (re-run, re-clone, ignore), which is unusually actionable guidance.

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