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Glama

get_index_stats

Read-onlyIdempotent

Aggregate local-cache metrics without network calls: all-time site, element, and technology counts, plus windowed palette, jury-score, and Motion DNA frequencies from recent cached details.

Instructions

Aggregate metrics over the LOCAL cache only (zero network). ALL-TIME: site count, element count and technology/tag frequency. WINDOWED by windowDays (default 90, 1-3650): cached details, palette colour frequency and jury score stats from at most the 500 most recently fetched detail rows, plus Motion DNA library frequency. Each metric carries coverage labelled 'locally cached subset'; windowDays/detailsCached/detailParseCap appear only on windowed metrics (sites/elements/technologies are explicitly all-time). top caps each frequency list (default 10, 1-50). Aggregates only - no site slugs or titles. Personal, non-commercial use.

Input Schema

NameRequiredDescriptionDefault
topNoMax rows per frequency list (technologies, colours, motion libs)
windowDaysNoWindow for detail-derived metrics only (details, colours, scores, motion DNA); sites/elements/technologies are all-time

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.13.0

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already establish read-only, idempotent, closed-world behaviour, and the description goes well beyond them: zero network, cache-only, a 500-row detail cap, coverage labels reading 'locally cached subset', and a note that only aggregates return (no slugs or titles). These are exactly the limitations an agent needs before trusting the numbers.

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 description is dense but front-loaded, leading with the scope and then the mode split. It is a long run-on in places, but nearly every clause carries operational meaning (cap, defaults, coverage labels) rather than filler.

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 no output schema, the description carries the burden of describing the return shape, and it does: it enumerates the metric families, says which carry coverage labels, notes which parameters are echoed, and warns there are no slugs or titles. An agent has enough to call it correctly and interpret the result.

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 100%, so the parameters are already documented, but the description adds real meaning: it states windowDays applies only to detail-derived metrics and that top caps each frequency list. That clarifies the interaction between the parameter and which returned metrics change, which the bare schema does not say.

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 a specific verb-plus-resource ('aggregate metrics over the LOCAL cache only') and then splits the tool's two modes, all-time and windowed, naming the exact metric families each returns. An agent can distinguish this from get_index_status (a status check) or search_sites without opening either 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?

It clearly frames when each mode applies: sites/elements/technologies are always all-time, while detail-derived metrics (colours, scores, motion DNA) are gated by windowDays. It stops short of explicitly naming an alternative sibling to use for non-aggregate needs, leaving that inference to the agent.

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