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Suite Check

lens.suite_check
Read-only

Validate a test suite before approval: count by split, domain, tag, source, review status; find duplicates; detect unmasked PII types and locations; run AgenticLens checks. Nothing stored.

Instructions

Check a test suite before approval: counts by split, domain, tag, source and review status; exact, near and conflicting duplicates; unmasked PII (types and locations only); and the approval checks AgenticLens would run. Nothing is stored. Needs agenticlens>=0.8.0.

Input Schema

NameRequiredDescriptionDefault
suiteYes
thresholdNo
session_idNoSession to read/write shared state under. Defaults to 'default'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.3.14

TDQS

A4/5.0
Behavior4/5

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

Beyond readOnlyHint=true, the description discloses that nothing is stored, that a minimum dependency version (agenticlens>=0.8.0) is required, and that PII findings return only types and locations. These are meaningful operational facts an agent cannot get from annotations. It does not describe cost, latency, or how the approval checks are summarized.

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?

One dense front-loaded sentence carrying the full scope, followed by two short constraint sentences ('Nothing is stored', version requirement). No filler; every clause adds information.

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

Completeness3/5

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

With no output schema and minimal annotations, the description does well on behavior and output scope but leaves the required `suite` object's shape entirely unspecified, which is the biggest obstacle to actually invoking the tool. The picture is adequate but incomplete for a tool with a complex nested required parameter.

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

Parameters2/5

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

Schema coverage is only 33% and only session_id is documented. The required `suite` parameter is a nested object with no description at all, and `threshold` (a 0–1 exclusive similarity bound) is undocumented; the description's mention of 'near duplicates' only loosely gestures at what threshold controls. With low coverage the description should compensate and it does not.

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 ('Check a test suite') and then enumerates exactly what is checked: counts by split/domain/tag/source/review status, duplicate classes, unmasked PII, and approval checks. This is specific enough to separate it from siblings like lens.suite_from_traces and evals.validate_artifact without opening any schema.

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

Usage Guidelines4/5

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

'Check a test suite before approval' gives a clear temporal trigger and intent, and the mention of 'the approval checks AgenticLens would run' ties it to a known workflow. It stops short of naming alternatives or stating when NOT to use it (e.g. versus spec.validate_artifact), so it is clear context without exclusions.

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