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Glama

Islam West Africa Collection (IWAC)

Search newspaper articles

search_articles
Read-onlyIdempotent

Search IWAC newspaper articles by keyword (title + OCR + AI abstracts, French and English), country, newspaper, subject, and date range. Use French concept keywords regardless of the user's report language. Matching is accent- and case-insensitive.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoDefault 20, max 100 (10 and 25 with with_description)
offsetNo
countryNoExact country name: Benin | Burkina Faso | Côte d'Ivoire | Niger | Togo (accents optional)
date_toNoYYYY-MM-DD (or YYYY)
keywordNoConcept keyword; substring match on title, OCR text, and the French and English AI abstracts. Prefer French for the OCR; an English term still matches via the English abstract
subjectNo
date_fromNoYYYY-MM-DD (or YYYY)
newspaperNo
hijri_yearNoIslamic (Umm al-Qura) year, e.g. 1445
hijri_monthNoIslamic lunar month: 1-12, or a name (Ramadan, Chaabane, Chawwal, Dhu al-Hijja). Pulls the articles behind an observance peak — matches only items with a full YYYY-MM-DD date.
with_descriptionNoInclude each article's ~500-char AI abstract (description_ai) for triage without get_article. Adds ~125 tokens/row, so `limit` defaults to 10 and caps at 25 while this is on.

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds meaningful behavioral context: matching is accent- and case-insensitive, keyword searches across title, OCR, and AI abstracts in French and English, and the French-keyword preference. This goes beyond what annotations provide.

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?

The description is two sentences, front-loaded with the core purpose, and the second sentence delivers essential keyword usage guidance. No redundant or wasted wording.

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 a search tool with 11 parameters and no output schema, the description covers the search scope, language handling, and matching behavior. It omits details like sorting or pagination, but the schema covers limit and with_description, so the overall context is reasonably complete.

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 coverage is 73%, not high enough to fully rely on schema, but the description adds useful meaning to the keyword parameter (search scope, language) and lists searchable dimensions. It does not, however, explain the undocumented parameters like offset, subject, or newspaper beyond naming them, leaving some gaps.

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 action ('Search') and resource ('IWAC newspaper articles'), and lists concrete search dimensions (keyword, country, newspaper, subject, date range). This clearly distinguishes it from sibling tools like search_audiovisual or search_documents.

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 implies use for newspaper article search and gives a context-specific guideline: 'Use French concept keywords regardless of the user's report language.' However, it does not explicitly contrast with alternative search tools or state when not to use it, so it lacks explicit 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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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.