Islam West Africa Collection (IWAC)

Co-occurrence matrix

get_cooccurrence

Read-onlyIdempotent

Count pairs of metadata values co-tagged on the same items. Matrix/network views show descriptive associations, not causal links. Filters apply before pair counting; top_n caps the vocabulary.

Input Schema

TableJSON Schema

Name	Required	Description
`exact`	No	AND values. Fields: subject/spatial/author/language/country/newspaper/topic_id/min_prob/scored_by; country_raw=whole cell; polarity:<model>.
`field`	No	subject (default) \| spatial \| author \| language
`top_n`	No	Values on each axis (default 15, max 30)
`subset`	No	articles (default) \| publications \| references
`country`	No	Exact country name (accents optional)
`date_to`	No
`keyword`	No
`subject`	No	Exact tag
`date_from`	No	Inclusive date bounds: YYYY[-MM[-DD]]
`newspaper`	No	Newspaper/periodical substring
`hijri_year`	No
`hijri_month`	No
`keyword_mode`	No
`keyword_aliases`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`note`	No
`view`	Yes
`field`	Yes
`matrix`	Yes
`subset`	Yes
`values`	Yes
`filters`	Yes
`top_pairs`	Yes
`total_matches`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed14 schema fields changedOct 6, 2026

changedInput schema / properties / country / description

Previous value: -"Exact country name: Benin | Burkina Faso | Côte d'Ivoire | Niger | Nigeria | Togo (accents optional)"New value: +"Exact country name (accents optional)"

changedInput schema / properties / date_from / description

Previous value: -"YYYY-MM-DD (or YYYY)"New value: +"Inclusive date bounds: YYYY[-MM[-DD]]"

removedInput schema / properties / date_to / description
```
Removed value: -"YYYY-MM-DD (or YYYY)"
```

addedInput schema / properties / exact

Added value: +{
+  "additionalProperties": {
+    "items": {
+      "type": "string"
+    },
+    "maxItems": 20,
+    "minItems": 1,
+    "type": "array"
+  },
+  "description": "AND values. Fields: subject/spatial/author/language/country/newspaper/topic_id/min_prob/scored_by; country_raw=whole cell; polarity:<model>.",
+  "propertyNames": {
+    "type": "string"
+  },
+  "type": "object"
+}

addedInput schema / properties / hijri_month
```
Added value: +{
+  "type": "string"
+}
```
addedInput schema / properties / hijri_year
```
Added value: +{
+  "type": "integer"
+}
```

removedInput schema / properties / keyword / description

Removed value: -"ONE French concept keyword; substring over the subset's text fields"

addedInput schema / properties / keyword / minLength
```
Added value: +1
```

addedInput schema / properties / keyword_aliases

Added value: +{
+  "items": {
+    "minLength": 1,
+    "type": "string"
+  },
+  "maxItems": 12,
+  "type": "array"
+}

addedInput schema / properties / keyword_mode

Added value: +{
+  "enum": [
+    "literal",
+    "all_terms"
+  ],
+  "type": "string"
+}

changedInput schema / properties / newspaper / description

Previous value: -"Newspaper (articles) or periodical/series title (publications)"New value: +"Newspaper/periodical substring"

changedInput schema / properties / subject / description

Previous value: -"Exact subject tag (pipe-aware)"New value: +"Exact tag"

removedInput schema / properties / top_n / maximum
```
Removed value: -9007199254740991
```
removedInput schema / properties / top_n / minimum
```
Removed value: --9007199254740991
```

Changed2 schema fields changedJul 29, 2026

changedInput schema / $schema

Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"

changedOutput schema / $schema

Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"

AddedJul 28, 2026

TDQS

A3.5/5.0

Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive behavior, and the description still adds real value: filters are applied before pair counting, top_n caps the vocabulary, and results are descriptive associations rather than causal links. It doesn't cover ordering of output rows or how ties are broken, but the operational semantics disclosed are substantive.

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 short sentences, each carrying distinct information (what is counted, how to interpret it, and filter/top_n semantics), with the core purpose front-loaded and no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 14 parameters, an enum, nested objects, and an output schema, the description is thin: it explains the computation and one parameter but leaves the filter vocabulary, subset scope, and keyword modes unaddressed. The output schema relieves it of explaining return values, but the input surface is under-described.

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 57% across 14 parameters, and the description only illuminates top_n ('caps the vocabulary') and the generic notion of filters. Undocumented parameters such as subset, keyword_mode, field, and the Hijri/date options receive no compensating explanation. The added meaning is real but narrow.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: 'Count pairs of metadata values co-tagged on the same items,' which clearly identifies a co-occurrence computation. It does not name or contrast with the nearest siblings (get_field_distribution, get_semantic_map, get_topic_distribution), so an agent must infer the boundary from the title alone.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The only guidance is interpretive ('Matrix/network views show descriptive associations, not causal links') and a filter-ordering note. There is no statement of when to choose this tool over get_field_distribution or get_semantic_map, nor any prerequisite or exclusion conditions.

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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