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Islam West Africa Collection (IWAC)

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Read-onlyIdempotent

Nearest stored embeddings by cosine similarity. Articles and documents lead with the dataset's published top 10. Scores run high (unrelated ~0.7) and rank reading candidates; >= 0.97 suggests a near-duplicate, but no threshold proves a reprint. Returns source IDs and dates. Corpus-wide candidate generation belongs offline.

Input 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) | documents | publications | references
min_scoreNoMinimum cosine similarity (-1 to 1)

Output Schema

NameRequiredDescriptionDefault
noteYes
viewYes
sourceYes
subsetYes
neighboursYes
stored_countYes
neighbours_sourceYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / additionalProperties
      Added value: +false
  2. Changed3 schema fields changed
    • addedOutput schema / properties / neighbours_source
      Added value: +{
      +  "type": "string"
      +}
    • addedOutput schema / properties / stored_count
      Added value: +{
      +  "type": "number"
      +}
    • changedOutput schema / required
      Previous value: -[
      -  "view",
      -  "subset",
      -  "source",
      -  "neighbours",
      -  "note"
      -]New value: +[
      +  "view",
      +  "subset",
      +  "source",
      +  "neighbours",
      +  "neighbours_source",
      +  "stored_count",
      +  "note"
      +]
  3. Changed1 schema field changed
    • changedInput schema / properties / subset / description
      Previous value: -"articles (default) | publications | references"New value: +"articles (default) | documents | publications | references"
  4. Changed5 schema fields changed
    • removedInput schema / properties / limit / maximum
      Removed value: -9007199254740991
    • removedInput schema / properties / limit / minimum
      Removed value: --9007199254740991
    • changedInput schema / properties / min_score / description
      Previous value: -"Drop neighbours below this cosine similarity (0-1)"New value: +"Minimum cosine similarity (-1 to 1)"
    • addedInput schema / properties / min_score / maximum
      Added value: +1
    • addedInput schema / properties / min_score / minimum
      Added value: +-1
  5. Changed2 schema fields changed
    • 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"
  6. Changed1 schema field changed
    • changedInput schema / properties / id / description
      Previous value: -"The o:id of the item to find neighbours for"New value: +"Item id — either a bare o:id ('3064') or the namespaced form search returns ('articles:3064')"
  7. Added

TDQS

A4.4/5.0
Behavior5/5

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

Beyond the read-only and idempotent annotations, the description explains score behavior in detail: unrelated items score around 0.7, scores of at least 0.97 suggest near-duplicates, and no threshold proves a reprint. It also notes that articles and documents lead with a published top 10 and that results include source IDs and dates.

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 compact and front-loaded: it leads with the core operation, then gives score semantics and scope limits. Every sentence adds useful information without repetition.

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 100% schema coverage, read-only annotations, and an output schema, the remaining description covers the important behavioral nuances: score interpretation, near-duplicate caveat, default published top 10 context for some subsets, and the offline boundary for corpus-wide work.

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 100%, so the schema already documents id, limit, subset, and min_score. The description adds meaningful interpretation for min_score by explaining that high cosine similarities are common and that 0.97 suggests near-duplication, which is more than the bare schema range.

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?

The first sentence states a specific retrieval operation: nearest stored embeddings by cosine similarity. It implies item-based similarity rather than query-based search, but it does not explicitly name a sibling alternative or state that it operates from a given item id in the description itself.

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?

The description gives a clear exclusion: 'Corpus-wide candidate generation belongs offline.' That helps an agent avoid using it for bulk candidate generation, but it does not explicitly name which sibling tool to use for query-based or corpus-wide search.

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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