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Glama

Islam West Africa Collection (IWAC)

by io.github.fmadore

Filter articles by AI sentiment

search_by_sentiment
Read-onlyIdempotent

Filter articles by gpt-5-6-luna sentiment labels (accent/case-insensitive exact match). One model's reading, not a consensus — 4 other models scored the same articles and often disagree; get_sentiment_distribution with model:"all" shows by how much. subjectivity is much the weakest of the three scales, so treat a set selected on it as a lead to read rather than as a finding.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoDefault 20, max 100
offsetNo
countryNoExact country name: Benin | Burkina Faso | Côte d'Ivoire | Niger | Togo (accents optional)
subjectNo
disputedNopolarite | centralite | subjectivite — keep only articles the panel SPLIT on for that field (French field names, as stored). Selects contested readings, not a sentiment value.
polarityNoTrès positif | Positif | Neutre | Négatif | Très négatif | Non applicable
centralityNoTrès central | Central | Secondaire | Marginal | Non abordé
subjectivityNoTrès objectif | Plutôt objectif | Mixte | Plutôt subjectif | Très subjectif — least to most subjective. Unscored where the model answered Non abordé, so this filter also excludes those.

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare read-only/idempotent behavior. The description adds valuable behavioral context: the results come from exactly one model, other models often disagree, and the subjectivity scale is the weakest. The accent/case-insensitive exact-match behavior is also disclosed rather than left to inference.

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?

Three dense sentences with no wasted words. The first sentence states the core semantics, the second prevents a consensus misinterpretation and names the alternative, and the third gives a reliability caveat. Every clause earns its place.

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?

For an 8-parameter tool with no output schema, the description covers the main conceptual risks: single-model scope, consensus routing, and weak subjectivity. The remaining detail is largely in the input schema, but the undocumented `subject` parameter is still a small gap.

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 75%, so the schema already does most of the work. The description adds non-obvious meaning by stating that labels are accent/case-insensitive exact matches and that subjectivity results are lower-confidence. It does not fully compensate for the undocumented `subject` parameter, but that gap is minor because the description's caveats target the highest-risk interpretation.

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 states a specific verb and resource: 'Filter articles by gpt-5-6-luna sentiment labels', and it also defines the exact matching behavior (accent/case-insensitive exact match). It distinguishes itself from the aggregate sentiment sibling by clarifying that this is one model's reading, not a consensus.

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?

The description explicitly tells the agent when this tool is not enough: if consensus across models is needed, it points to get_sentiment_distribution with model:'all'. It also warns that subjectivity-filtered results should be treated as leads, not findings, giving concrete decision guidance.

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

A4/5.0
Disambiguation5/5

Each tool targets a distinct resource or analytical dimension: get_* tools are specific to item categories (article, audiovisual, document, image, publication, reference) or specific analyses (cooccurrence, field distribution, lexical metrics, semantic map, sentiment distribution, similar items, temporal distribution, topic distribution). Search tools are clearly separated by subset, with generic 'search' for cross-category discovery and search_* for filtered queries. The only potential overlap between 'fetch' and get_* is resolved by 'fetch' returning a standard format while get_* tools provide category-specific extra metadata.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern: fetch, get_*, list_*, search_*. The verbs are clear and the nouns precisely indicate the resource or action. There are no mixed conventions (e.g., no camelCase or inconsistent verb styles), making the naming predictable and easy to navigate.

Tool Count2/5

At 34 tools, the count is well above the 25+ threshold that the rubric flags as too many. While the server's comprehensive scope for a digital archive with multiple subsets and analytical features explains the high count, the sheer number may overwhelm agents and makes the toolset feel heavy rather than well-scoped. Some grouping or consolidation (e.g., merging distribution tools or providing a single fetch with optional detail levels) could reduce the load without losing functionality.

Completeness5/5

The tool surface provides complete coverage for the domain: every content type (articles, publications, references, documents, audiovisual, images, index) has both search and get/retrieve tools, plus listing tools for key vocabularies. Analytical tools for statistics, distributions, sentiment, topics, and similarity are fully realized. There are no obvious dead ends—users can discover, retrieve, and analyze all parts of the collection, and even gaps in the underlying data (e.g., limited OCR coverage) are explicitly surfaced via tools like get_collection_stats.

Resources