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Glama

Crossload

crossload_topic_stats

Returns counts only, never the items themselves: for the items use crossload_search. Given one topic name, it counts the whole topic server-side and reports how it is distributed over bible books and chapters, authors, series, years, licences, categories, media and duration, plus the topics that co-occur on the same items. Use it to answer 'which passages/authors/years does this topic span', to find related topics by evidence rather than by guess, and before writing anything that sums up a topic: counting by hand means paging through every hit. The topic name must match crossload_browse exactly; a guessed spelling is rejected and nothing is counted. 'withoutBibleRef' says how many items carry no passage at all, which is the caveat to every statement about the passages of a topic. Each distribution lists the most frequent entries only, and says so when it leaves something out.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYesTopic name, exactly as listed by crossload_browse, e.g. 'Vergebung'. Resolved server-side; a guessed spelling is rejected and nothing is counted.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicNo
statusYes
messageNo
resolvedNo
truncatedNo
candidatesNo
distributionsNo
policyVersionNo
totalContentsNo
countedContentsNo

Schema Changelog

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

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden, and it delivers: it reveals server-side counting (no client-side paging), exact-match requirement with rejection of guessed spellings, the 'withoutBibleRef' caveat, and that distributions are truncated to most-frequent entries and say when they omit data. No hidden surprise about what gets counted or omitted is left unstated.

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 front-loaded with the most important distinction ('counts only, never the items themselves') and uses compact sentences. It is a little dense and re-states the exact-match rule already in the schema, but every sentence contributes either behavior, usage guidance, or a caveat, so nothing feels wasted.

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?

Given that an output schema exists and there is only one parameter, the description is complete: it states what the tool returns, what it does not return, its caveats, its prerequisites, and when to choose it over a sibling. An agent has everything needed to invoke it correctly and interpret its results.

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%, and the schema already explains that 'topic' must be exactly as listed by crossload_browse with the rejection consequence. The description repeats this rather than adding new parameter-level meaning, so it stays at the baseline: useful reinforcement, but no substantial semantic addition beyond the schema.

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 explicitly states the tool 'returns counts only, never the items themselves' and then enumerates the exact distribution dimensions (bible books, authors, series, years, etc.). It also names the sibling tool crossload_search as the place to go for the actual items, making the tool's scope unmistakable.

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?

It gives concrete use cases: answering which passages/authors/years a topic spans, finding related topics by evidence, and summarizing a topic before writing. It also names the alternative (crossload_search for item-level results) and states a prerequisite: the topic name must match crossload_browse exactly.

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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TDQS

A4.6/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: browse resolves controlled vocabulary, search finds content items, read_content reads transcripts, lexicon looks up bible terms, and topic_stats aggregates counts. There is no real overlap or ambiguity between them.

Naming Consistency4/5

All tools share the crossload_ prefix and use snake_case, which makes the set feel consistent. The main deviation is that browse and search are bare verbs while read_content is verb_noun and lexicon/topic_stats are noun-style names, but the pattern is still easy to predict.

Tool Count5/5

Five tools is well-scoped for a retrieval-focused archive server. Every tool covers a distinct, necessary capability without redundancy or bloat.

Completeness4/5

The tool surface covers search, controlled-vocabulary lookup, transcript reading, term definition, and topic statistics, which is comprehensive for the domain. Minor gaps exist, such as no standalone metadata-by-uid retrieval and no way to locate a passage inside an item except by reading through it, but agents can work around these.

Resources