VineVerse — Bible Knowledge Graph

Relation vocabulary and canon table

get_vocabulary

Read-onlyIdempotent

The closed set of predicates every typed edge in the graph is drawn from — what develops, narrated-in, involves, stated-in and named-by mean, what their inverses are, which types they connect, and how many edges use each. Also returns the 66-book canon table with OSIS ids, slugs and chapter counts, which is how you find out that Song of Songs is "Song" before building a reference.

Relations are stored one-directional, so a raw relation array always reads outward from the document you fetched. Read this before interpreting a predicate.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Output Schema

TableJSON Schema

Name	Required	Description	Default
`note`	No
`books`	Yes
`licence`	No
`predicates`	Yes
`undeclared`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedAug 23, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "https://json-schema.org/draft/2020-12/schema",
+  "additionalProperties": {},
+  "properties": {
+    "books": {
+      "items": {
+        "additionalProperties": {},
+        "properties": {
+          "chapters": {
+            "type": "number"
+          },
+          "name": {
+            "type": "string"
+          },
+          "order": {
+            "type": "number"
+          },
+          "osisId": {
+            "type": "string"
+          },
+          "path": {
+            "type": "string"
+          },
+          "slug": {
+            "type": "string"
+          },
+          "testament": {
+            "type": "string"
+          }
+        },
+        "required": [
+          "osisId",
+          "name",
+          "slug",
+          "testament",
+          "order",
+          "chapters",
+          "path"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "licence": {
+      "additionalProperties": {},
+      "properties": {
+        "attribution": {
+          "type": "string"
+        },
+        "data": {
+          "type": "string"
+        },
+        "shareAlike": {
+          "type": "string"
+        },
+        "text": {
+          "type": "string"
+        }
+      },
+      "type": "object"
+    },
+    "note": {
+      "type": "string"
+    },
+    "predicates": {
+      "items": {
+        "additionalProperties": {},
+        "properties": {
+          "domain": {
+            "type": "string"
+          },
+          "edges": {
+            "type": "number"
+          },
+          "heading": {
+            "type": "string"
+          },
+          "id": {
+            "type": "string"
+          },
+          "inverse": {
+            "type": "string"
+          },
+          "range": {
+            "type": "string"
+          },
+          "symmetric": {
+            "type": "boolean"
+          }
+        },
+        "required": [
+          "id",
+          "edges"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "undeclared": {
+      "items": {
+        "additionalProperties": {},
+        "properties": {
+          "edges": {
+            "type": "number"
+          },
+          "id": {
+            "type": "string"
+          }
+        },
+        "required": [
+          "id",
+          "edges"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "required": [
+    "predicates",
+    "undeclared",
+    "books"
+  ],
+  "type": "object"
+}

First observedAug 23, 2026

TDQS

A4.7/5.0

Behavior5/5

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

The annotations mark the tool as read-only and idempotent, and the description goes further by revealing a non-obvious behavioral trait: relations are stored one-directionally, so raw relation arrays always read outward from the fetched document. It also emphasizes the vocabulary is a closed set, which is contextual data behavior beyond the structured fields.

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?

Every sentence in the description provides necessary information: the predicate vocabulary, the canon table, the directionality caveat, and the usage pointer. It is front-loaded with the most critical content and contains no repetition or filler.

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 that an output schema exists and annotations cover the read-only safety profile, the description supplies all needed behavioral context: what the returned data contains, how predicates are organized, a concrete example (Song), and a crucial directionality warning. An agent can decide when and how to invoke it correctly.

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?

The input schema has zero parameters, so the baseline of 4 applies. The description correctly adds no unnecessary parameter details because there are none to clarify.

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 uses a specific verb ('returns') and names a concrete resource: the closed set of graph predicates and the canon table, including their meanings, inverses, and usage counts. This clearly differentiates it from all sibling tools, which focus on references, entities, connections, passages, or searches.

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?

It communicates when to use the tool: 'Read this before interpreting a predicate' and before building a reference against the canon table (e.g., finding the OSIS slug 'Song'). It does not explicitly name a sibling alternative, but given that no sibling covers vocabulary/canon data, this is a clear enough use case.

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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