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ANIME INTELLIGENCE

by io.github.Mike05102

full_intelligence

Read-onlyIdempotent

Complete decision package for a Japanese anime collectible: exact identity, current market, rarity, rerelease status, authenticity risk, BUY-WAIT decision and best purchase route. Highest-value endpoint for autonomous agents that want one final purchase recommendation. Price: 0.05 USDC via x402. Use when the agent needs identity, market, rarity, authenticity, timing and purchase route together instead of calling individual endpoints.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoANIME INTELLIGENCE canonical product UUID.
langNoen
queryNoProduct name, JAN/EAN-13, model number or identifying description.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
intentNo
paymentNo
price_usdcNo
paid_x402_urlNo
expected_outputNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed10 schema fields changed
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / anyOf
      Added value: +[
+  {
+    "required": [
+      "query"
+    ]
+  },
+  {
+    "required": [
+      "id"
+    ]
+  }
+]
    • addedInput schema / properties / id
      Added value: +{
+  "description": "ANIME INTELLIGENCE canonical product UUID.",
+  "type": "string"
+}
    • addedInput schema / properties / lang / default
      Added value: +"en"
    • removedInput schema / properties / lang / description
      Removed value: -"Preferred response language such as en or ja."
    • addedInput schema / properties / lang / enum
      Added value: +[
+  "ja",
+  "en",
+  "zh",
+  "ko"
+]
    • changedInput schema / properties / query / description
      Previous value: -"Anime figure name, JAN code, model number, character, series, or manufacturer."New value: +"Product name, JAN/EAN-13, model number or identifying description."
    • addedInput schema / properties / query / examples
      Added value: +[
+  "Full intelligence for Nendoroid Hatsune Miku",
+  "Should I buy this exact figure and where?"
+]
    • removedInput schema / required
      Removed value: -[
-  "query"
-]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "expected_output": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "intent": {
+      "type": "string"
+    },
+    "paid_x402_url": {
+      "type": "string"
+    },
+    "payment": {
+      "type": "string"
+    },
+    "price_usdc": {
+      "type": "number"
+    }
+  },
+  "type": "object"
+}
  2. Changed10 schema fields changed
    • addedInput schema / additionalProperties
      Added value: +false
    • removedInput schema / anyOf
      Removed value: -[
-  {
-    "required": [
-      "query"
-    ]
-  },
-  {
-    "required": [
-      "id"
-    ]
-  }
-]
    • removedInput schema / properties / id
      Removed value: -{
-  "description": "ANIME INTELLIGENCE canonical product UUID.",
-  "type": "string"
-}
    • removedInput schema / properties / lang / default
      Removed value: -"en"
    • addedInput schema / properties / lang / description
      Added value: +"Preferred response language such as en or ja."
    • removedInput schema / properties / lang / enum
      Removed value: -[
-  "ja",
-  "en",
-  "zh",
-  "ko"
-]
    • changedInput schema / properties / query / description
      Previous value: -"Product name, JAN/EAN-13, model number or identifying description."New value: +"Anime figure name, JAN code, model number, character, series, or manufacturer."
    • removedInput schema / properties / query / examples
      Removed value: -[
-  "Full intelligence for Nendoroid Hatsune Miku",
-  "Should I buy this exact figure and where?"
-]
    • addedInput schema / required
      Added value: +[
+  "query"
+]
    • changedOutput schema / (root)
      Previous value: -{
-  "properties": {
-    "expected_output": {
-      "items": {
-        "type": "string"
-      },
-      "type": "array"
-    },
-    "intent": {
-      "type": "string"
-    },
-    "paid_x402_url": {
-      "type": "string"
-    },
-    "payment": {
-      "type": "string"
-    },
-    "price_usdc": {
-      "type": "number"
-    }
-  },
-  "type": "object"
-}New value: +null
  3. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds valuable operational context beyond annotations: it costs 0.05 USDC via x402, and it aggregates multiple decision dimensions into one recommendation. This transparency about cost and scope is especially important for an autonomous agent.

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 compact and front-loaded with the core value proposition, then adds cost and usage guidance. There is mild redundancy in listing the package components twice, but every sentence provides useful information. It is appropriately sized for a high-value aggregate endpoint.

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?

The output schema exists, so return value details are not required in the description. The description covers what the tool does, when to use it, what it costs, and how it relates to sibling endpoints. Nothing essential is missing for correct invocation and selection.

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?

The schema already describes id and query with useful examples, and lang has an enum. The description adds no parameter-level detail beyond the schema, but with 67% schema coverage, the schema carries most of the burden. A neutral score is appropriate because the description does not need to repeat schema content.

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 defines the tool as a complete decision package for a Japanese anime collectible, enumerating the exact components: identity, market, rarity, rerelease status, authenticity risk, BUY-WAIT decision, and purchase route. It differentiates itself from sibling tools by framing itself as the highest-value, aggregate endpoint. The purpose is unmistakable and specific.

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 explicitly states when to use the tool: when the agent needs identity, market, rarity, authenticity, timing, and purchase route together. It contrasts with calling individual endpoints, implying that single-dimension needs should go elsewhere, though it does not name specific sibling alternatives. This is clear enough for an agent to route correctly.

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

All ANIME INTELLIGENCE toolsInspect
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TDQS

A3.9/5.0
Disambiguation4/5

Most tools target a distinct decision or data need: identity, market price, rarity, authenticity, buy/wait decision, and best purchase route. The main ambiguities are search_anime_product vs identify_anime_product and anime_market vs best_place, but the descriptions largely clarify the intended use.

Naming Consistency3/5

The naming is readable but mixes conventions: four tools use an anime_ noun prefix, two use verb_anime_product, and two use standalone noun phrases like best_place and full_intelligence. The pattern is not predictable enough for a consistent verb_noun or prefix-based convention.

Tool Count5/5

Eight tools is a well-scoped count for an anime-collectible intelligence service. Each tool maps to a necessary step in the decision workflow, and full_intelligence is a justified aggregation endpoint rather than redundant bloat.

Completeness4/5

The workflow from search/identify through market, rarity, authenticity, buy/wait, and best place covers the core advisory journey well. Minor gaps like price history or an explicit watch-list mechanism are absent, but agents can work around them using market data and the watch recommendation.

Resources