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Islam West Africa Collection (IWAC)

by io.github.fmadore

Search IWAC

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Read-onlyIdempotent

Search the Islam West Africa Collection across newspaper articles, Islamic publications, archival documents, academic references, audiovisual recordings, photographs, and the authority index (persons/places/organisations/events/subjects). Pass ONE concept or name — e.g. 'Tijaniyya', 'laïcité', 'Sheikh Gumi', 'pèlerinage'. Matching is accent- and case-insensitive; a multi-word query requires every word to appear somewhere in the item, so prefer a single concept per call. Write query strings and concept keywords in French for press/publication/document/index discovery even when the user's report language is not French. Academic references are multilingual, so try French and English title/abstract terms when relevant; metadata/filter labels remain French. Use the French transliteration of Islamic terms (Tabaski not 'Eid al-Adha', charia not 'sharia', Maouloud not 'Mawlid'). Returns {results:[{id,title,url,category}], ranking}; each result's category names its subset and the ranking field documents the ordering. Pass an id to fetch to read the full text. For filtered queries (by country, date, or newspaper) use the search_* tools instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results across all categories. Default 20, max 50.
queryYesOne concept, name, or short phrase; use French concept terms for primary sources, and French/English terms for references

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYes
limitYes
rankingYes
resultsYes
deep_scanYes
limit_warningNo
requested_limitNo
coverage_warningNo
unavailable_categoriesNo

TDQS

A5/5.0
Behavior5/5

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

Even with annotations declaring readOnly and idempotent, the description adds substantial behavioral context: accent- and case-insensitivity, the multi-word 'every word must appear' rule, the preference for a single concept per call, and the shape of the response including `ranking` semantics. It also links to `fetch` for full-text retrieval, which goes beyond the structured annotations.

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 longer than average but every sentence delivers actionable information: scope, query constraints, language rules, response format, and alternative tools. It is front-loaded with the core purpose in the first sentence and maintains clear logical flow without redundancy.

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 the tool's complexity (multi-domain search, multilingual requirements, custom ranking, and connection to `fetch`), the description covers all essential aspects: collection scope, query formulation rules, language handling, output structure, and disambiguation from filtered search tools. The presence of an output schema further reduces the need to document return values in prose.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema coverage is 100%, the description dramatically enriches the `query` parameter semantics by defining what constitutes a query (single concept/name), providing examples, explaining matching behavior, and prescribing language/transliteration conventions. This is far beyond the schema's 'One concept, name, or short phrase' description.

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 begins with a specific verb and resource: 'Search the Islam West Africa Collection', and enumerates the covered subsets (newspaper articles, Islamic publications, archival documents, etc.). It distinguishes itself from sibling search_* tools by explicitly stating 'For filtered queries (by country, date, or newspaper) use the search_* tools instead.'

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 provides explicit guidance on when to use this tool: for cross-collection searches with a single concept, and specifically directs filtered queries to sibling search_* tools as an alternative. It also gives actionable language instructions (French for press/publication/document/index, French/English for academic references) and transliteration rules (Tabaski, charia, Maouloud).

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.

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