ai-furniture-hub

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v6.1.0
Disambiguation3/5
The tools have distinct primary purposes, but there is notable overlap in search and recommendation functions. For example, search_products, search_rakuten_products, and search_amazon_products all handle product searches across different platforms, while get_popular_products and get_curated_sets both provide recommendations, which could cause confusion in tool selection. Descriptions help clarify, but the boundaries are not always sharp.
Naming Consistency4/5
Most tools follow a consistent verb_noun naming pattern (e.g., calc_room_layout, compare_products, get_product_detail), which aids predictability. However, there are minor deviations like diagnose_ai_visibility and identify_product, which use different verb styles or compound terms, slightly breaking the pattern but remaining readable overall.
Tool Count5/5
With 15 tools, the server is well-scoped for its furniture and home goods domain, covering aspects like layout calculation, product search, comparison, recommendations, and diagnostics. Each tool appears to serve a specific function without redundancy, making the count appropriate for the intended purpose.
Completeness4/5
The toolset provides comprehensive coverage for furniture and home goods, including search, comparison, recommendations, layout planning, and product identification. Minor gaps exist, such as no explicit tools for user account management or order tracking, but these are not core to the domain, and agents can work around them with the available tools.
Average 4.2/5 across 15 of 15 tools scored. Lowest: 3.6/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses key behaviors: real-time API search, returns price/reviews/images, and requires presenting affiliate URLs to users. However, it doesn't mention rate limits, authentication needs, error handling, pagination, or what happens when no results are found. It adds some behavioral context but leaves significant gaps.
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 appropriately concise with two sentences that each serve distinct purposes: first establishes when to use the tool, second describes what it does and a key behavioral requirement. No wasted words, though it could be slightly more structured with clearer separation of functional vs. behavioral aspects.
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 search tool with 6 parameters, no annotations, and no output schema, the description provides adequate functional context but lacks important behavioral details. It covers the basic purpose and usage context but doesn't describe return format, error conditions, or performance characteristics that would help an agent use it effectively.
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 description coverage is 100%, so the schema already documents all 6 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. The baseline of 3 is appropriate when the schema does the heavy lifting for parameter documentation.
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 description clearly states the tool searches Rakuten Ichiba for furniture/storage products using real-time API data, returning price, reviews, and images. It specifies the resource (Rakuten Ichiba) and action (search), but doesn't explicitly differentiate from sibling tools like 'search_amazon_products' or 'search_products' beyond mentioning Rakuten specifically.
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 provides clear context for when to use this tool: 'when products aren't in the catalog or when latest price/stock info is needed.' It doesn't explicitly state when NOT to use it or name specific alternatives, but the context implies this is for real-time Rakuten searches rather than cached or curated data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it performs checks on multiple technical aspects (llms.txt, robots.txt, structured data, etc.), returns a 0-100 score and A-F grade, and evaluates cross-border readiness. However, it does not mention potential side effects, rate limits, authentication needs, or error handling, leaving some behavioral gaps.
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 appropriately sized and front-loaded, starting with the core functionality and then detailing the checks and outputs. However, the last sentence about the demo use case, while relevant, could be considered slightly extraneous, preventing a perfect score for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (diagnosing AI visibility with multiple checks) and the absence of annotations and output schema, the description does a good job of explaining what the tool does, what it checks, and what it returns (score and grade). It could be more complete by detailing the output structure or error cases, but it provides sufficient context for an agent to understand the tool's purpose and use.
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 description coverage is 100%, so the schema already documents both parameters ('intent' and 'url'). The description adds context by explaining that the URL is for the site to diagnose, but it does not provide additional meaning beyond what the schema states (e.g., examples of valid intents or URL formats). Baseline 3 is appropriate when the schema handles parameter documentation adequately.
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's purpose: 'diagnose how well a site is visible to AI agents' by checking specific technical aspects (llms.txt, robots.txt, structured data, etc.) and returning a score/grade. It specifies the verb ('diagnose'), resource ('site'), and scope ('AI agents like GPT/Claude/Gemini'), distinguishing it from sibling tools that focus on product search, comparison, or layout calculation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context ('URLを指定すると' - when you specify a URL) and mentions a demo use case ('AIOエージェンシーのデモとして'), but it does not explicitly state when to use this tool versus alternatives or provide exclusions. No sibling tools appear to offer similar functionality, so the lack of explicit alternatives is understandable, but guidance remains implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It describes what the tool returns (curated set information with product IDs) and suggests follow-up actions, but lacks details on behavioral traits like rate limits, authentication needs, error handling, or response format. It doesn't contradict annotations, but could provide more operational context.
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 efficiently structured in two sentences: the first explains the purpose with concrete examples and types, the second provides actionable follow-up guidance. Every sentence adds value with zero wasted words, making it easy to parse and understand quickly.
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?
Given no annotations and no output schema, the description adequately covers the tool's purpose and basic usage but lacks details on behavioral traits, response structure, or error handling. For a tool with 6 parameters and no structured output documentation, it provides a functional overview but leaves gaps in operational context that could hinder effective use by an AI agent.
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 description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by mentioning examples like 'new life essentials' (relating to intent/occasion) and listing the four types (matching the enum), but doesn't provide additional syntax, format details, or usage examples for parameters beyond what's in the schema.
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 returns curated set proposals for specific scenarios like 'new life essentials' or 'YouTuber desk tour products', explicitly listing the four types (bundle, room preset, influencer pick, hack set). It distinguishes from siblings like get_product_detail or search_rakuten_products by focusing on curated sets rather than individual product details or general 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?
The description provides clear context for when to use this tool (to get curated set proposals) and mentions calling get_product_detail or search_rakuten_products for detailed product information and purchase links, implying alternatives for different needs. However, it doesn't explicitly state when not to use it or compare directly with all sibling tools like suggest_by_space or get_popular_products.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes key behaviors: returns a comparison table, includes additional catalog-matched data (internal dimensions, compatible storage, buy_guide), incorporates decision_hint into recommendations, and requires affiliate_url presentation. However, it doesn't mention error handling, rate limits, authentication needs, or what happens when products can't be compared.
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 appropriately sized and front-loaded, starting with the primary use case and core functionality. All sentences contribute value, though the final sentence about affiliate_urls could be integrated more smoothly. There's no redundant information or unnecessary elaboration.
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 comparison tool with 3 parameters and no output schema, the description adequately covers the core functionality but lacks details about the comparison table structure, error cases, or how catalog matching works. With no annotations and no output schema, the description should ideally provide more behavioral context about what the tool returns and its limitations.
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 description coverage is 100%, so the schema already documents all three parameters. The description doesn't add significant semantic information beyond what's in the schema descriptions, though it implies the keywords parameter should contain product names or identifiers rather than generic search terms. The baseline of 3 is appropriate when the schema does the heavy lifting.
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's purpose: compare 2-5 products across price, size, reviews, and load capacity, returning a parallel comparison table. It specifies the exact resources (products) and verb (compare), distinguishing it from sibling tools like get_product_detail or search_products by focusing on multi-product comparison rather than single-product lookup or general search.
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 explicitly states when to use this tool: 'when comparing 2-5 products' and provides a concrete example ('NクリックとKALLAXどっちがいい？'). It also implicitly distinguishes from alternatives by specifying the comparison table output format, unlike sibling tools that return single products or search results.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses key behavioral traits: it returns both catalog successors and Rakuten search results, and requires presenting affiliate URLs to users. However, it doesn't mention rate limits, authentication needs, error handling, or response format details, leaving gaps for a tool with no annotation coverage.
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?
Front-loaded with the primary use case, followed by details on what it returns and user instructions. All sentences are necessary, though it could be slightly more structured (e.g., separating behavioral details from usage guidelines).
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 no annotations and no output schema, the description covers purpose and usage well but lacks details on return format, pagination, error cases, or authentication. It's adequate for basic understanding but incomplete for reliable agent invocation without additional context.
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 description coverage is 100%, providing good documentation for both parameters. The description adds minimal value beyond the schema by implying the 'query' parameter is used for model numbers or product names, but doesn't elaborate on format or constraints. Baseline 3 is appropriate when schema does the heavy lifting.
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's purpose with specific verbs ('探す', '返す') and resources ('カタログの後継候補', '楽天の検索結果'), distinguishing it from siblings like 'search_products' or 'search_rakuten_products' by focusing on finding replacements for discontinued/obsolete products rather than general product searches.
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?
Explicitly states when to use this tool ('「この型番が売ってない」「生産終了した棚の代わり」のときに呼ぶ') and provides clear alternatives for final verification ('最終確認はメーカー公式で'), giving specific guidance on context and limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the tool returns products with compatibility/storage information and Rakuten review trends, and that affiliate URLs should be presented to users. However, it doesn't disclose important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, error conditions, or response format details. The description adds some context but leaves significant gaps.
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 appropriately concise with three sentences that each serve a purpose: when to use the tool, what it does, and what to do with the results. It's front-loaded with usage context. While efficient, it could be slightly more structured by separating behavioral details from usage guidelines.
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?
Given the tool's moderate complexity (5 parameters, no output schema, no annotations), the description provides good usage context but lacks important behavioral details. It explains when to use the tool and what it returns, but doesn't cover response format, error handling, or operational constraints. For a tool with no annotations or output schema, more completeness would be expected.
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 description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description mentions filtering by category/brand and including Rakuten trends, which aligns with the schema but doesn't add meaningful semantic information beyond what's already in the parameter descriptions. The baseline of 3 is appropriate when the schema does the heavy lifting.
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's purpose: to return recommended products filtered by category/brand with compatibility/storage information and Rakuten review trends. It specifies the verb ('返す' - return) and resource ('おすすめ製品' - recommended products), and distinguishes from siblings like search_products or get_curated_sets by focusing on popularity/recommendations rather than general search or curated sets.
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 explicitly states when to use this tool: in response to questions like 'おすすめの棚は？' (What shelves do you recommend?) or '人気のキッチン家電は？' (What are popular kitchen appliances?). It also implies when not to use it by specifying its focus on popular/recommended products with compatibility information, suggesting alternatives like search_products for general searches or get_product_detail for individual product details.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: it uses Vision AI to extract features, queries catalog+Rakuten, and returns detailed info (model numbers, internal dimensions, consumables, compatible boxes). However, it lacks details on permissions, rate limits, error handling, or response format. The description adds useful context but doesn't fully compensate for the absence of 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 appropriately sized and front-loaded: it starts with usage scenarios, explains the process (Vision AI → features → catalog+Rakuten query), and ends with output details. Every sentence adds value, though it could be slightly more streamlined (e.g., by merging some clauses). No wasted words, but minor redundancy exists.
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?
Given the tool's complexity (5 parameters, no annotations, no output schema), the description is moderately complete. It covers purpose, usage, and high-level behavior but lacks details on output structure, error cases, or advanced usage. For an identification tool with rich parameters and no output schema, it should ideally explain return values or limitations more explicitly.
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 description coverage is 100%, so the schema already documents all parameters thoroughly. The description mentions 'features' and implies 'intent' through usage examples but doesn't add significant semantic value beyond what the schema provides. It doesn't explain parameter interactions or provide additional syntax/format details. Baseline 3 is appropriate when the schema does the heavy lifting.
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's purpose: identifying products from photo/feature text using Vision AI, returning candidates from catalog+Rakuten with model numbers, internal dimensions, consumables, and compatible box information. It specifies the verb ('identify'), resource ('products'), and scope (photo/feature text → product identification with detailed info), distinguishing it from siblings like search_products or get_product_detail which lack this specific identification functionality.
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 explicitly states when to use this tool: for queries like 'What is this shelf in the photo?' or 'I want to know boxes that fit my shelf.' It provides clear context (photo/feature text scenarios) and implicitly distinguishes from siblings by focusing on identification from visual/textual features rather than general searching, comparison, or detail retrieval.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses key behaviors: returns affiliate URLs (not product data), automatically selects SearchIndex from category, and expects URL presentation to user. However, it doesn't mention rate limits, authentication needs, or what happens with invalid parameters. It provides useful context but lacks comprehensive behavioral disclosure.
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?
Two concise sentences with zero waste. First sentence establishes when to use, second explains what it does and key behavioral details. Every word earns its place with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search/URL generation tool with no annotations and no output schema, the description provides good context about when to use, what it returns, and how to handle the output. It could be more complete by mentioning error cases or response format, but covers the essential usage scenario adequately given the tool's complexity.
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 description coverage is 100%, so parameters are well-documented in the schema. The description adds minimal parameter semantics beyond the schema - it mentions SearchIndex is automatically selected from category (not a parameter) and implies intent should reflect Amazon purchasing context. Baseline 3 is appropriate since the schema does the heavy lifting.
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's purpose: generating affiliate URLs for Amazon search results pages specifically for furniture/storage products. It distinguishes from siblings by mentioning 'Amazon' (vs Rakuten in search_rakuten_products) and specifying it returns URLs rather than product data (vs search_products which presumably returns data).
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?
Explicitly states when to use ('when user wants to buy on Amazon or can't find on Rakuten') and provides clear alternative context. Also specifies the expected output action ('present affiliate_url to user'), giving concrete usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses key behaviors: calculates box quantities and total costs, provides coordination tips based on location, includes persona-based advice with budget/brand recommendations, and requires affiliate URL presentation. However, it doesn't mention rate limits, error handling, or data sources.
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 appropriately sized and front-loaded with the primary use case. Every sentence adds value: first establishes when to use, second explains core calculations, third adds coordination advice, fourth details persona hints, fifth specifies affiliate requirement. Minor redundancy exists in mentioning persona_hints twice.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex tool with 6 parameters, no annotations, and no output schema, the description provides good coverage of what the tool does, when to use it, and key behavioral aspects. It explains the calculation logic, recommendation approach, and output requirements (affiliate URLs). The main gap is lack of information about return format or error cases.
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 description coverage is 100%, so the baseline is 3. The description adds meaningful context beyond the schema by explaining how parameters relate to the tool's functionality: '設置場所(押入れ/洗面所/キッチン等)に応じた' clarifies the 'scene' parameter's purpose, and 'persona_hints' connects to the broader recommendation system. However, it doesn't provide additional syntax or format details for individual parameters.
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's purpose with specific verbs ('計算し' for calculation, '算出' for computation, '提供' for provision) and resources (shelves, storage boxes). It distinguishes from siblings by focusing on coordination advice with quantity calculations, unlike general search or comparison tools in the sibling list.
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 explicitly states when to use this tool with example user queries ('「この棚に合うボックスは？」「カラーボックスの整理方法」のときに呼ぶ'). It also distinguishes usage context by mentioning specific scenarios like storage locations (押入れ/洗面所/キッチン) and persona-based recommendations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by explaining color alias handling, result structure hints (related_items_hint, buy_guide, seasonal_hints), and the requirement to present affiliate_url. It doesn't mention rate limits, authentication needs, or pagination behavior, but covers most operational aspects.
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?
Front-loaded with usage examples, then explains search scope and filters, then result handling instructions. Every sentence adds value, though it's slightly dense. Could be more structured with bullet points but remains efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex search tool with 14 parameters and no output schema, the description does well by explaining result structure and next-step actions. It covers the main behavioral aspects but doesn't mention error handling, result limits, or sorting options. Given the rich parameter schema and sibling context, it's mostly complete.
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 description coverage is 100%, so the baseline is 3. The description adds some context about color aliases and keyword search behavior (partial match, space-separated AND), but doesn't significantly enhance parameter understanding beyond what the schema already provides.
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 searches across 31 categories and 80+ brands using keywords, dimensions, price, color, and brand. It distinguishes from siblings like search_amazon_products or search_rakuten_products by specifying it searches a specific catalog, and from get_product_detail by being a search rather than detail retrieval tool.
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?
Explicitly provides usage examples ('when user says...'), mentions when to use sibling tools (get_related_items for accessories), and gives guidance on how to handle specific result fields (buy_guide, seasonal_hints, affiliate_url). It clearly distinguishes this from other search tools by specifying the catalog scope.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It adds valuable context beyond basic functionality: it mentions that affiliate URLs are returned for monetization ('返却される affiliate_url をユーザーへの購入リンクとして使用してください'), that related products are automatically suggested ('関連商品（同シリーズ・近いサイズ）も自動で提案されます'), and emphasizes the importance of the intent parameter ('【重要】intentには、なぜこの詳細が必要かを記述してください'). This covers key behavioral aspects like output structure and parameter usage guidance.
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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by important usage notes and behavioral details. Every sentence adds value—none are redundant or wasteful. The structure uses emphasis markers (【重要】, 【収益化】) to highlight key points efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is largely complete. It covers purpose, parameter guidance, and behavioral traits like affiliate URLs and related product suggestions. However, it doesn't detail the exact structure of the returned 'フルスペック' (full specs) or potential error cases, which could be helpful for an AI agent. With no output schema, some additional output clarity would improve completeness.
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 description coverage is 100%, so the schema already documents both parameters (id and intent). The description adds meaningful semantics: it clarifies that 'id' is for a specific product, and provides examples for 'intent' ('購入前の最終確認、サイズの詳細確認、他商品との比較') and marks it as required with importance ('【重要】'). This goes beyond the schema's basic descriptions, offering context and usage guidance for the parameters.
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's purpose: '商品IDを指定して、特定の家具・収納商品のフルスペック（寸法・価格・在庫・素材など）を取得します' (specify a product ID to get full specifications for a specific furniture/storage product). It uses specific verbs ('取得します' - get/retrieve) and resources ('家具・収納商品' - furniture/storage products), and distinguishes itself from siblings like 'get_related_items' or 'search_products' by focusing on detailed specs for a single product.
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 provides clear context for when to use this tool: when detailed specs (dimensions, price, stock, materials) are needed for a specific product ID. It explicitly mentions use cases like '購入前の最終確認、サイズの詳細確認、他商品との比較' (final check before purchase, detailed size confirmation, comparison with other products). However, it doesn't explicitly state when NOT to use it or name specific alternatives among siblings, though the context implies it's for single-product details rather than searching or comparing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden and provides good behavioral context: it separates required vs. recommended items, includes Rakuten search results for immediate proposals, expands to depth=2 for 'related of related', and mandates that required items must be communicated to the user. However, it doesn't mention rate limits, error handling, or authentication needs.
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?
Front-loaded with purpose, followed by key behavioral details in a single, dense sentence. Every clause adds value: separation of item types, Rakuten integration, depth expansion, and user communication requirement. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 5 parameters, 100% schema coverage, no annotations, and no output schema, the description provides strong context on behavior and usage. It covers the tool's role in workflow, output structure (separated items with search results), and expansion logic. Minor gap: no mention of output format or error cases.
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 description coverage is 100%, so the schema already documents all parameters well. The description adds minimal value beyond schema: it mentions depth=2 behavior but doesn't explain other parameters like intent or include_rakuten beyond what's in schema descriptions. Baseline 3 is appropriate when schema does heavy lifting.
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's purpose: to propose 'what else is needed' after finding a product via search_products, returning required and recommended accessories/parts with Rakuten search results. It distinguishes from siblings like search_products (which finds products) and get_product_detail (which provides details).
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?
Explicitly states when to use: 'after search_productsで商品を見つけた後' (after finding a product with search_products) and for the purpose of '他に何が必要？を提案する' (proposing what else is needed). It distinguishes from alternatives by specifying its unique role in the workflow.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what the tool returns (31 categories with product counts and brands) and that it can filter to specific categories, but doesn't mention rate limits, authentication requirements, error conditions, or response format details. It provides basic operational context but lacks comprehensive behavioral information.
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 efficiently structured in three sentences: first establishes the entry point purpose, second specifies what data is returned and filtering capability, third provides the usage workflow. Every sentence adds value with zero waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a relatively simple read-only tool with 2 parameters and no output schema, the description provides good context about what data is returned and how to use it in the workflow. However, without annotations or output schema, it could benefit from more detail about the response structure and any limitations.
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?
With 100% schema description coverage, the baseline is 3. The description adds value by explaining the practical use of category_filter with examples ('キッチン収納', 'デスク') and clarifying that intent is required for understanding user purpose, which goes beyond the schema's technical documentation.
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's purpose: it's an entry point tool that returns all 31 categories with product counts and handled brands, and can also retrieve products for a specific category. It distinguishes itself from siblings like search_products by focusing on category discovery rather than product search.
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 provides explicit usage guidance: use when users ask 'what can be searched?' or 'what categories exist?', first use this tool to understand categories, present them to users, then proceed to search_products with the selected category. It clearly differentiates from the search_products sibling tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses several behavioral traits: rotation-fit handling ('回転フィット対応'), automatic coordination plan generation ('コーディネーションプランも自動生成'), and affiliate URL presentation ('affiliate_urlをユーザーに提示'). However, it doesn't mention rate limits, authentication needs, or error conditions.
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 perfectly front-loaded and efficient. The first sentence establishes the use case with an example, the second explains core functionality, and subsequent sentences add important behavioral details. Every sentence earns its place with zero wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 6 parameters, no annotations, and no output schema, the description does well to explain the tool's unique behaviors (rotation-fit, coordination plans, affiliate URLs). However, it doesn't describe the return format or what happens when no products are found, which would be helpful given the lack of output schema.
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 description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal parameter semantics beyond the schema - it mentions dimensions ('寸法(mm)を指定') and cross-category search ('カテゴリ横断で返す'), but these are already implied in the schema descriptions. Baseline 3 is appropriate when schema does the heavy lifting.
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's purpose: suggesting products that fit a given space across categories. It uses specific verbs ('探す', '返す', '生成') and resources ('製品', 'コーディネーションプラン'), and distinguishes itself from siblings by focusing on space-first search rather than product-first or layout tools.
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 explicitly states when to use this tool: 'スペース起点で探すときに呼ぶ' (call when searching based on space). It provides a concrete example ('洗面所の幅45cm×奥行30cmの隙間に何か置きたい') and implicitly distinguishes from siblings like search_products or get_popular_products by emphasizing space-first rather than product-first queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: grid-based simulation, returns coordinates and rotation status, and explicitly states limitations (doesn't consider doors/circulation). However, it doesn't mention performance characteristics, error conditions, or what happens with impossible configurations.
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 perfectly concise with three sentences that each earn their place: first establishes use case with example, second explains inputs and simulation method, third provides crucial limitations. No wasted words, front-loaded with the primary purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex simulation tool with 7 parameters, no annotations, and no output schema, the description does well by explaining the simulation approach, inputs, outputs, and limitations. However, it could provide more detail about the output format and what constitutes success/failure in the simulation to be fully complete.
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?
With only 43% schema description coverage, the description adds significant value by explaining the core parameters: room dimensions in mm, furniture list with width/depth/count, and the simulation approach. While it doesn't detail all 7 parameters, it provides essential context about what the simulation does with these inputs, compensating well for the low schema coverage.
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's purpose: performing a grid-based simulation to check furniture placement feasibility in a room. It specifies the input (room dimensions and furniture list) and output (coordinates and rotation status), distinguishing it from siblings like search_products or get_product_detail which handle product information rather than spatial simulation.
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 explicitly states when to use this tool ('家具の配置可否を確認するときに呼ぶ' - call when checking furniture placement feasibility) and provides important limitations: it doesn't consider doors or circulation paths, and should be treated as a rough guide. This gives clear context for when to use it versus more detailed alternatives.
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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