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Prior Catches (institutional defect memory)

lens_catches_relevant

Reads defect bank for prior named defects matching artifact type, most-recurrent first. Flags recurring traps as [PROMOTE] so deterministic checks target known failure patterns before validating.

Instructions

Read the defect bank BEFORE validating: prior named defects for an artifact type, most-recurrent patterns first. This is the institutional memory that makes the gate improve over time — the recurring traps tell you where this class of artifact has failed before. Any pattern at threshold is marked [PROMOTE], meaning it recurs often enough to deserve a deterministic check. Runs the local lens-kit CLI, no LLM call. Omit artifact_type and pass all=true when the type has no history yet.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
allNoSurface every catch regardless of artifact type
cwdNoWorking directory to run in. catches.jsonl is read from and written to here. Defaults to the server's cwd.
domainNoAlso filter by domain, e.g. marketing, finance, agency
formatNoblock = paste-ready surface (default); json = raw records
thresholdNoRecurrence threshold for [PROMOTE] lines (default 3)
artifact_typeNoArtifact type to filter by, e.g. landing-copy, research-brief, content-pack

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.1.1

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden and discloses meaningful traits: it 'Runs the local lens-kit CLI, no LLM call' (execution mechanism and cost), returns 'most-recurrent patterns first' (ordering behavior), and explains the [PROMOTE] marker semantics. Minor gap: the schema's cwd description says catches.jsonl is both read from and written to, while the description frames this as purely a read, leaving the write side-effect ambiguous.

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?

The purpose is front-loaded in the first sentence, and the remaining sentences each add distinct information: history rationale, PROMOTE semantics, execution mechanism, and the parameter workaround for the no-history case. The institutional-memory sentence is slightly rhetorical but reinforces why recurrence ordering matters, so no sentence is wasted.

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?

With no output schema and no annotations, the description must carry the load, and it covers the core what (named defects), ordering, promotion marking, execution mode, and the special all=true case. The format parameter's schema descriptions ('block = paste-ready surface; json = raw records') partially compensate for the missing output schema, though the exact record shape is left unspecified.

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 description coverage is 100%, so the baseline is 3, and the description adds value on top: it explains the artifact_type/all interaction ('Omit artifact_type and pass all=true when the type has no history yet') and gives threshold meaning beyond the schema's terse wording by tying recurrence to deterministic checks. This pushes it clearly above baseline.

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 opening sentence names a specific verb and resource — 'Read the defect bank' — plus the scope ('prior named defects for an artifact type') and ordering ('most-recurrent patterns first'). This clearly differentiates it from siblings like lens_catches_add (write), lens_catches_stats (aggregate), and validate_content (validate), and the workflow position 'BEFORE validating' pins down its role.

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?

The description gives clear context for when to use the tool — 'Read the defect bank BEFORE validating' — and an explicit scenario: 'Omit artifact_type and pass all=true when the type has no history yet.' It stops short of naming sibling alternatives or stating when not to use it, so it has clear context without formal exclusions.

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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