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Glama

Islam West Africa Collection (IWAC)

Search photographs

search_images
Read-onlyIdempotent

Search the IWAC photographs (30 items: mosques, radio stations, schools, signage and street scenes documented during fieldwork). Keyword matches title, creator, subject, place and the rare caption. Each result carries image_url (the full-resolution file), coordinates ('lat, lng' where known) and the canonical IWAC page. Call with no arguments to list all. Captions are almost never present, so prefer subject/place filters over keywords, or semantic_search_images when it is enabled.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoDefault 20, max 50
offsetNo
countryNoExact country name: Benin | Burkina Faso | Côte d'Ivoire | Niger | Nigeria | Togo (accents optional)
creatorNoPhotographer name (substring match)
date_toNoYYYY-MM-DD (or YYYY)
keywordNoFrench concept keyword; substring match on title, creator, subject, place and caption
spatialNoExact place name, e.g. Ouagadougou (pipe-aware)
subjectNoExact subject tag (pipe-aware)
date_fromNoYYYY-MM-DD (or YYYY)

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the read-only/idempotent annotations, the description reveals that captions are almost never present, that results include image_url, coordinates, and a canonical page, and that calling with no arguments lists all items. These are non-obvious behavioral traits that affect invocation and interpretation of results.

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 four sentences, tightly packed with useful information: collection size, matching behavior, result fields, alternative tools, and usage tips. No filler or repetition; the structure front-loads the core purpose before adding nuance.

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's enumeration of result fields (image_url, coordinates, page) is essential and provided. The 30-item scope, no-argument listing, and mention of semantic_search_images cover typical agent questions. The description is complete for a search tool of this complexity.

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 high (89%), so baseline is 3. The description adds value by explaining that keyword searches rarely match captions, that subject/place filters are preferable, and that these filters are 'pipe-aware' (though that detail is in the schema). It compensates for the few undocumented parameters by giving selection guidance rather than re-listing parameter syntax.

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 and resource: 'Search the IWAC photographs', and clearly defines the collection scope (30 items, content types). It distinguishes itself from siblings by focusing on photographs and even references semantic_search_images as an alternative, while the no-arguments listing behavior further clarifies its purpose.

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?

Explicit guidance is provided: captions are rare, so users should prefer subject/place filters over keywords, and semantic_search_images is named as an alternative. It also explains how to list all items, which covers the common 'no filter' use case. This directly helps an agent choose between this tool and siblings.

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.