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Glama

Islam West Africa Collection (IWAC)

by io.github.fmadore

Find similar items

get_similar_items
Read-onlyIdempotent

The items nearest to a given one in meaning, by cosine similarity over the stored embeddings. Answers 'what else is like this' without a keyword — it finds pieces on the same event or theme that share no vocabulary. A neighbour above ~0.85 is usually the same story reprinted or lightly rewritten, which is how to spot syndication in this corpus; 0.6-0.8 is 'same subject, different piece'. Needs no API key: the item's own vector is a column, so nothing has to be embedded at request time. This is per-item, NOT the corpus-wide near-duplicate sweep — that is an all-pairs job and belongs offline.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesItem id — either a bare o:id ('3064') or the namespaced form search returns ('articles:3064')
limitNoNeighbours returned (default 12, max 50)
subsetNoarticles (default) | publications | references
min_scoreNoDrop neighbours below this cosine similarity (0-1)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteYes
viewYes
sourceYes
subsetYes
neighboursYes

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds valuable behavioral context: no API key needed, it's per-item (not corpus-wide), and explains what similarity scores mean. No contradiction with annotations.

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 two paragraphs with front-loaded main action. It is informative but slightly verbose; however, every sentence adds value. Could be more concise, but still good.

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 rich annotations, full schema coverage, and output schema, the description is complete. It covers usage context, parameter interpretation, and behavioral traits sufficiently for an agent to invoke correctly.

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?

Schema coverage is 100%, but the description adds meaning beyond schema: it interprets the min_score parameter with concrete ranges (0.85+ syndication, 0.6-0.8 same subject) and clarifies the subset parameter implicitly. This helps agents choose appropriate values.

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 clearly states the tool finds items nearest in meaning by cosine similarity over embeddings, answering 'what else is like this' without keywords. It distinguishes itself from sibling tools like search or get_article by specifying it uses embeddings for semantic similarity.

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 explains when to use the tool (find similar items without keywords, identify syndication or same subject) and explicitly contrasts with the corpus-wide near-duplicate sweep, which belongs offline. It also provides guidance on interpreting similarity thresholds (0.85+ for syndication, 0.6-0.8 for same subject).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Islam West Africa Collection (IWAC) toolsInspect
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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