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 states this is a query operation (implying read-only), but doesn't mention authentication requirements, rate limits, error handling, response formats, or any constraints like data freshness or availability. For a financial API tool with 6 parameters, this leaves significant 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise - just two sentences that efficiently communicate the tool's purpose and scope. The first sentence states the core function, and the second enumerates the data categories without unnecessary elaboration. Every word earns its place, and the information is front-loaded appropriately.

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

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

For a financial data query tool with 6 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns, how results are structured, any limitations or constraints, or how this tool differs from similar financial data tools in the server. The description provides only basic purpose information without the contextual details needed for effective tool selection and use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, so all parameters are documented in the schema. The description adds minimal value beyond the schema - it mentions the categories of data (stock, forex, crypto, economic indicators) which loosely maps to parameter usage, but doesn't provide additional semantic context about how parameters interact or when specific combinations are needed. The baseline of 3 is appropriate given the comprehensive schema documentation.

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

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

The description clearly states the tool's purpose: 'Query financial market data from Alpha Vantage' with specific examples of data types (stock time series, forex rates, crypto prices, economic indicators). It uses a specific verb ('query') and identifies the resource ('financial market data'), but doesn't distinguish this tool from sibling tools like 'finnhub_query' or 'fmp_financial' which appear to serve similar financial data domains.

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

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

The description provides no guidance on when to use this tool versus alternatives. While it lists data categories (stock, forex, crypto, economic indicators), it doesn't specify when to choose this over sibling tools like 'finnhub_query' or 'fmp_financial', nor does it mention any prerequisites, limitations, or typical use cases beyond the broad category of financial data querying.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions privacy features and result structure, but does not cover important behavioral traits such as rate limits, authentication needs, error handling, or response format details. This leaves significant gaps for a tool with potential API constraints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded and concise, consisting of two sentences that efficiently convey the tool's function and key features. Every sentence adds value without redundancy, making it easy to understand at a glance.

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

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

Given the complexity of a search tool with 5 parameters and no output schema, the description is incomplete. It lacks details on return values, error cases, or operational constraints. While it covers basic purpose and privacy, more context is needed for effective use, especially without annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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 does not add any additional meaning or context beyond what the schema provides, such as explaining parameter interactions or usage examples. Baseline 3 is appropriate when the schema handles parameter documentation.

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

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 ('Web search via Brave Search API') and resources ('structured results with titles, URLs, descriptions, and snippets'). It distinguishes itself from siblings by emphasizing privacy-focused features ('Privacy-focused, no tracking'), which is unique among the listed tools.

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

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

The description implies usage for web searches with privacy considerations, but does not explicitly state when to use this tool versus alternatives (e.g., other search tools like google_maps_places or semantic_scholar_search). It provides context about privacy but lacks specific exclusions or comparisons.

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

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 mentions what the tool does ('check balance', 'shows remaining USDC credits and wallet address') but lacks behavioral details such as authentication requirements, rate limits, error conditions, or whether it's read-only or has side effects. This is inadequate 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded: two sentences that directly state the tool's function and output. There's no wasted verbiage, and every sentence adds value by explaining what the tool does and what it shows.

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

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

Given the tool has no parameters, no annotations, and no output schema, the description is minimally complete. It explains the purpose and output ('remaining USDC credits and wallet address'), but lacks context like authentication, error handling, or data format. For a simple query tool, this is adequate but leaves gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add parameter semantics, but that's appropriate here. Baseline is 4 for zero parameters, as it avoids unnecessary detail.

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

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

The description clearly states the tool's purpose: 'Check your Clawy x402 credit balance' specifies the verb ('check') and resource ('Clawy x402 credit balance'). It distinguishes itself from siblings by focusing on a specific Clawy service, though it doesn't explicitly contrast with similar tools since none exist 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.

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., authentication), context for use, or exclusions. Given the sibling tools include various financial and query tools, some implicit overlap might exist, but no explicit guidance is provided.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes what information is included but does not mention behavioral traits such as authentication requirements, rate limits, error conditions, or data freshness. For a tool with no annotations, this leaves significant gaps in understanding how it operates.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that efficiently communicates the tool's purpose and scope. It is front-loaded with the main action and resource, followed by specific details, with no wasted words or redundancy.

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

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 scope but lacks details on behavioral aspects and return values. It is complete enough for basic understanding but insufficient for full operational context, especially for a tool with potential complexities like data retrieval from court systems.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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 ('case_number' and 'court_code') with descriptions. The description does not add any parameter-specific semantics beyond what the schema provides, such as format examples or constraints, but the high schema coverage justifies the baseline score of 3.

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

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

The description clearly states the action ('Get detailed information') and the specific resource ('Korean court auction case'), distinguishing it from the sibling tool 'court_auction_search' by focusing on details for a specific case rather than searching for cases. It specifies the scope of information included: property details, appraisal, bid history, and case documents.

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

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

The description implies usage when detailed information for a specific case is needed, but does not explicitly state when to use this tool versus alternatives like 'court_auction_search'. It provides context (e.g., 'specific Korean court auction case') but lacks explicit guidance on prerequisites or exclusions.

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

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 mentions search functionality but lacks critical behavioral details such as pagination behavior (implied by 'page' parameter), rate limits, authentication requirements, or what the response format looks like (no output schema).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured in two sentences: the first states the core purpose, and the second lists search criteria. Every word serves a purpose with zero waste.

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

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

Given the complexity of a search tool with 6 parameters, no annotations, and no output schema, the description is insufficient. It lacks details on behavioral traits (e.g., pagination, rate limits), response format, and error handling, leaving significant gaps for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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 by listing search criteria ('region, property type, price range, or auction date') but does not provide additional semantics beyond what the schema offers.

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

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

The description clearly states the specific action ('Search Korean court auction property listings') and resource ('foreclosed real estate'), with explicit differentiation from sibling tools like 'court_auction_detail' by focusing on search rather than detailed information retrieval.

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

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 ('Find foreclosed real estate by region, property type, price range, or auction date'), but does not explicitly mention when not to use it or name alternatives like 'court_auction_detail' for detailed views.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool is for searching disclosures but doesn't cover key behavioral traits such as whether it's read-only or mutative, authentication requirements, rate limits, pagination behavior, or error handling. For a search tool with zero annotation coverage, this leaves critical information gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured in two sentences, with no wasted words. It front-loads the core purpose and efficiently covers scope. However, it could be slightly improved by integrating more behavioral details without losing conciseness.

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

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

Given the complexity of a search tool with 6 parameters, no annotations, and no output schema, the description is incomplete. It lacks information on behavioral traits, usage guidelines, and output format, which are essential for effective tool invocation. The description does not compensate for the absence of structured data, leaving significant gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, meaning all parameters are documented in the input schema. The description adds minimal value beyond this, mentioning search by 'company code, name, or keyword' which aligns with the schema but doesn't provide additional syntax, format details, or usage examples. The baseline score 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.

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

The description clearly states the tool's purpose: 'Search Korean corporate disclosures (DART/FSS). Find filings by company code, name, or keyword.' It specifies the verb ('search'), resource ('Korean corporate disclosures'), and scope ('covers listed and unlisted Korean companies'). However, it doesn't explicitly differentiate from sibling tools like 'dart_financial' or other search tools, keeping it from a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions the scope ('covers listed and unlisted Korean companies') but doesn't specify use cases, prerequisites, or exclusions. With sibling tools like 'dart_financial' and other search tools present, this lack of comparative guidance is a significant gap.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool retrieves data (implying read-only), but doesn't mention authentication requirements, rate limits, error conditions, response format, or whether the data is real-time/historical. For a financial data retrieval tool with zero annotation coverage, this leaves significant 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that states the core purpose and scope. It's appropriately sized for a straightforward retrieval tool, though it could be slightly more structured by separating purpose from resource listing. 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.

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

For a financial data retrieval tool with no annotations and no output schema, the description is incomplete. It doesn't explain what format the financial statements come in (structured data, PDFs, etc.), whether multiple periods are returned, or what authentication/rate limits apply. The agent would need to guess about the return format and operational constraints.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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 thoroughly. The description adds no parameter-specific information beyond what's in the schema - it doesn't explain the significance of the report codes, format expectations, or provide examples. 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.

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

The description clearly states the tool's purpose: 'Retrieve financial statements for a Korean company from DART' with specific resources listed (income statement, balance sheet, cash flow data). It distinguishes from siblings like 'dart_disclosure' by focusing on financial statements rather than general disclosures. However, it doesn't explicitly differentiate from other financial data tools like 'fmp_financial' or 'finnhub_query' beyond the DART source.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to prefer this over other financial data tools (e.g., 'fmp_financial', 'finnhub_query') or when to use 'dart_disclosure' instead. There are no explicit when/when-not statements or named alternatives, leaving the agent to infer usage context.

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

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

With no annotations, the description carries full burden but provides minimal behavioral insight. It mentions the data source and scope but lacks details on authentication needs, rate limits, error handling, response format, or pagination behavior (implied by page/per_page params). This is inadequate for a query tool with multiple parameters.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose. It could be slightly more structured by separating scope examples, but there's no wasted text.

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

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

For a query tool with 4 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain how to interpret results, handle errors, or navigate the data portal's service structure, leaving significant gaps for an AI agent to use it effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, providing baseline documentation for all 4 parameters. The description adds no additional parameter semantics beyond implying service_id corresponds to data domains listed, but doesn't explain how to find valid service IDs or format params object. Baseline 3 is appropriate given high schema coverage.

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

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

The description clearly states the action ('Query') and resource ('Korean government open data from data.go.kr'), with examples of data domains (real estate, weather, etc.). It distinguishes from siblings by specifying the Korean government data source, but doesn't explicitly contrast with similar query tools like finnhub_query or semantic_scholar_search.

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

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

No guidance on when to use this tool versus alternatives is provided. The description mentions data domains but doesn't specify prerequisites, constraints, or compare with sibling tools that might handle similar data types (e.g., for transportation vs. tmap_route).

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

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 'high-quality, natural-sounding output' but lacks critical details: it doesn't specify rate limits, authentication needs, error handling, or performance characteristics. For a translation tool, this omission is significant, as the agent needs to understand constraints like language support nuances or API costs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is highly concise and front-loaded: it states the core function in the first clause and adds supporting details efficiently. Both sentences earn their place by covering purpose and key features without redundancy, making it easy for an agent to parse quickly.

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

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

Given the tool's moderate complexity (4 parameters, no output schema, no annotations), the description is partially complete. It covers the basic purpose and language support but lacks behavioral context and output details. Without annotations or an output schema, the agent must infer behavior, leaving gaps in understanding how results are structured or what errors might occur.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the input schema already documents all parameters thoroughly. The description adds no additional parameter semantics beyond implying language support and formality options. It doesn't clarify parameter interactions or provide examples beyond what's in the schema, meeting the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose: 'Translate text using DeepL neural machine translation.' It specifies the verb ('translate') and resource ('text'), and mentions key capabilities like supporting 30+ languages and high-quality output. However, it doesn't explicitly differentiate from sibling tools, which are unrelated (e.g., search, financial queries, maps), so the distinction is implicit rather than explicit.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions general capabilities but doesn't specify contexts, prerequisites, or exclusions. For example, it doesn't indicate if it's for real-time translation, batch processing, or specific use cases like documents vs. conversations, leaving the agent with minimal usage direction.

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

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 the tool 'Returns high-quality audio' which gives some output context, but fails to address critical behavioral aspects like rate limits, authentication requirements, cost implications, error handling, or latency. For a cloud-based AI service tool, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately concise with three clear sentences. The first sentence states the core purpose, the second describes output quality, and the third mentions additional capabilities. There's no wasted language, though it could be slightly more structured with bullet points for the features mentioned.

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

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

Given the complexity of a cloud AI service tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what format the audio is returned in (e.g., base64, file URL), doesn't mention authentication requirements, rate limits, or error conditions. For a tool that likely involves API calls with potential costs and limitations, more contextual information is needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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 doesn't add any parameter-specific information beyond what's in the schema. It mentions 'Supports multiple languages and voices' which relates to the voice_id and model_id parameters, but doesn't provide additional semantic context. This meets the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose: 'Convert text to speech using ElevenLabs AI voice synthesis.' It specifies the action (convert), resource (text), and technology (ElevenLabs), though it doesn't differentiate from siblings since there are no other TTS tools in the list. The mention of 'high-quality audio' and 'multiple languages and voices' adds useful context about output quality and capabilities.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, limitations, or scenarios where other tools might be more appropriate. While it hints at capabilities ('Supports multiple languages and voices'), it lacks explicit usage instructions or comparisons with sibling tools.

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

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

No annotations are provided, so the description carries full burden. It mentions 'real-time' data but doesn't disclose rate limits, authentication needs, data freshness, error handling, or response formats. For a financial API tool with no annotation coverage, this leaves significant 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized (two sentences) and front-loaded with the core purpose. Every sentence adds value: the first states the action and source, the second lists data types. No wasted words, though it could be slightly more structured.

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

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

Given no annotations, no output schema, and a general-purpose financial query tool, the description is incomplete. It lacks information on response structure, error conditions, rate limits, and differentiation from sibling tools. The agent would need to guess or trial-and-error.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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 4 parameters. The description adds no parameter-specific information beyond implying 'symbol' is for stocks and 'from/to' for date ranges. 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.

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

The description clearly states the tool's purpose: 'Query real-time financial data from Finnhub' with specific examples of data types (stock quotes, company news, earnings, recommendations, market status). It uses a specific verb ('query') and identifies the resource ('Finnhub'), but doesn't distinguish from sibling financial tools like alpha_vantage_query or fmp_financial.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling financial tools like alpha_vantage_query or fmp_financial, nor does it specify any prerequisites, constraints, or typical use cases. The agent must infer usage from the description alone.

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

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 about cost ('Cost is per page crawled') and the discovery/scraping behavior, which aren't evident from the schema alone. However, it doesn't disclose important traits like rate limits, authentication needs, error handling, or what 'scrapes' entails (e.g., format of returned data).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise (three short sentences) with zero wasted words. It's front-loaded with the core purpose, followed by behavioral context and cost information. Every sentence earns its place by adding distinct value.

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

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

For a 5-parameter tool with no annotations and no output schema, the description provides adequate but incomplete context. It covers the basic purpose and cost implications but lacks details about return values, error conditions, performance characteristics, and how it differs from sibling tools. The schema handles parameter documentation well, but behavioral aspects remain underspecified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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 doesn't add any parameter-specific semantics beyond what's in the schema (e.g., it doesn't explain how 'includePaths' and 'excludePaths' interact). 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.

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

The description clearly states the tool's purpose: 'Crawl a website starting from a URL. Discovers and scrapes linked pages.' It specifies the verb ('crawl') and resource ('website'), and distinguishes it from sibling tools like 'firecrawl_scrape' by emphasizing discovery of linked pages. However, it doesn't explicitly differentiate from 'firecrawl_map' which might have overlapping functionality.

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

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

The description provides implied usage context through the mention of 'Discovers and scrapes linked pages' and the cost warning, suggesting this is for comprehensive website exploration rather than single-page scraping. However, it lacks explicit guidance on when to use this versus alternatives like 'firecrawl_scrape' or 'firecrawl_map', and doesn't mention prerequisites or exclusions.

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

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 mentions 'Fast way to discover site structure' and 'without extracting content', which gives some behavioral context, but lacks details on permissions, rate limits, error handling, or output format. For a tool with no annotations, this leaves significant gaps in understanding its behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two concise sentences with zero waste: the first states the purpose, and the second provides usage context. It's front-loaded with the core functionality and efficiently communicates key information without unnecessary details.

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

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 is minimal but covers the basic purpose. It doesn't explain return values, error cases, or advanced behaviors, which are important for a mapping tool. However, it's adequate for a simple tool with good schema coverage, though more context would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents the two parameters (url and limit). The description adds no additional parameter semantics beyond what's in the schema, such as URL format constraints or limit implications. Baseline 3 is appropriate when the schema handles parameter documentation.

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

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

The description clearly states the tool's purpose: 'Map all URLs on a website without extracting content' specifies the verb (map) and resource (URLs on a website). It distinguishes from sibling 'firecrawl_crawl' and 'firecrawl_scrape' by emphasizing 'without extracting content' and 'discover site structure', though it doesn't explicitly name those alternatives.

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

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

The description implies usage context with 'Fast way to discover site structure', suggesting it's for structural discovery rather than content extraction. However, it doesn't provide explicit guidance on when to use this tool versus alternatives like 'firecrawl_crawl' or 'firecrawl_scrape', nor does it mention any prerequisites or exclusions.

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

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: JavaScript rendering, bypassing common blocks, and markdown optimization for LLMs. However, it lacks details on rate limits, error handling, authentication needs, or what 'clean, structured content' entails beyond markdown.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded and concise with two sentences that efficiently convey core functionality and key features. Every sentence adds value without redundancy, making it easy to parse quickly.

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

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 is moderately complete for a scraping tool. It covers purpose and some behaviors but lacks details on output structure, error cases, or performance constraints, which could be important for an agent invoking this tool effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, providing baseline documentation for all parameters. The description adds minimal semantics by mentioning 'clean, structured content' and 'markdown optimized for LLMs', which loosely relates to the 'formats' parameter but does not elaborate on parameter interactions or default behaviors beyond what the schema states.

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

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 ('scrape', 'extract') and resource ('a single URL'), and distinguishes it from sibling tools like firecrawl_crawl and firecrawl_map by specifying 'single URL' rather than crawling or mapping multiple URLs.

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

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

The description implies usage context ('extract clean, structured content', 'handles JavaScript rendering') but does not explicitly state when to use this tool versus alternatives like firecrawl_crawl for multiple URLs or other scraping tools. No exclusions or prerequisites are mentioned.

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

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

No annotations are provided, so the description carries full burden. It states what data is retrieved but doesn't disclose behavioral traits like authentication requirements, rate limits, error conditions, data freshness, or response format. The description is minimal and lacks operational context needed for effective use.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero wasted words. It front-loads the core purpose and includes key details (statement types and source) without unnecessary elaboration. Every word earns its place.

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

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 is incomplete for a tool with 4 parameters. It doesn't explain what the tool returns (e.g., data format, structure), error handling, or operational constraints. For a financial data retrieval tool, this leaves significant gaps in understanding how to use it effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, providing clear documentation for all parameters. The description adds no parameter-specific information beyond what's in the schema (e.g., it doesn't explain relationships between parameters or provide examples). 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.

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

The description clearly states the action ('Get') and resource ('company financial statements') with specific types listed (income statement, balance sheet, cash flow) and source ('from FMP'). It distinguishes from siblings like fmp_quote (which likely provides price quotes) but doesn't explicitly contrast with other financial data tools like finnhub_query or dart_financial.

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

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

No guidance on when to use this tool versus alternatives is provided. The description doesn't mention when this tool is appropriate compared to other financial data tools (e.g., finnhub_query, dart_financial) or general search tools. Usage is implied by the tool name and description but not explicitly stated.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'real-time and historical' data but doesn't cover critical aspects like rate limits, authentication needs, data freshness, error handling, or response format. For a financial data tool with zero annotation coverage, this leaves the agent with insufficient 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded in a single sentence, efficiently stating the core functionality. It avoids redundancy and wastes no words, though it could benefit from a second sentence for usage context.

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

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

Given the complexity of financial data retrieval, lack of annotations, and no output schema, the description is incomplete. It doesn't address behavioral traits, usage guidelines, or output expectations, leaving the agent under-informed for effective tool invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, so the schema fully documents all parameters. The description adds no parameter-specific information beyond implying historical data availability. This meets the baseline of 3, as the schema does the heavy lifting, but the description doesn't enhance parameter understanding.

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

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

The description clearly states the tool's purpose: 'Get real-time and historical stock quotes from Financial Modeling Prep. Covers US and global markets.' It specifies the action ('Get'), resource ('stock quotes'), and data source ('Financial Modeling Prep'), with market scope. However, it doesn't explicitly differentiate from sibling tools like 'finnhub_query' or 'alpha_vantage_query' that might offer similar financial data.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions coverage of 'US and global markets' but doesn't specify contexts, prerequisites, or exclusions. With sibling tools like 'finnhub_query' and 'alpha_vantage_query' present, the lack of comparative guidance is a significant gap.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool 'Supports driving, walking, bicycling, and transit modes,' which adds some context about capabilities. However, it lacks critical details such as rate limits, authentication requirements, error handling, or what the output looks like (e.g., route steps, duration). For a tool with no annotations, this is insufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise and front-loaded: two sentences that directly state the tool's purpose and key features without any fluff. Every word earns its place, making it easy for an AI agent to parse quickly.

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

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

Given the complexity of a directions tool (involving multiple parameters, modes, and no output schema), the description is incomplete. It lacks details on output format, error cases, or behavioral traits like rate limits. With no annotations and no output schema, the description should provide more context to help an AI agent use the tool effectively, but it falls short.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, meaning the input schema already documents all parameters thoroughly (e.g., 'origin' as 'Starting point (address or lat,lng)'). The description adds minimal value by implying parameter usage through 'between two points' and listing travel modes, but doesn't provide additional semantics beyond what the schema offers. This meets the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose: 'Get directions between two points via Google Maps.' It specifies the verb ('Get directions') and resource ('between two points via Google Maps'), and mentions supported travel modes. However, it doesn't explicitly differentiate from sibling tools like 'google_maps_geocode' or 'google_maps_places', which prevents a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools (e.g., 'google_maps_geocode' for address lookup or 'tmap_route' for alternative routing), prerequisites, or specific contexts where this tool is preferred. The lack of comparative or contextual advice limits its utility for an AI agent.

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

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 the basic functionality but lacks critical details: whether this requires API keys, rate limits, authentication needs, error handling, or what the output format looks like. For a tool that likely interacts with an external service, this is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded with the core functionality.

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

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

Given the tool's complexity (interacting with Google Maps API), lack of annotations, and no output schema, the description is insufficient. It doesn't cover authentication requirements, rate limits, error cases, or return format, leaving significant gaps for an AI agent to use this tool effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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 (address, latlng, language) with clear descriptions. The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score for high schema coverage.

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

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

The description clearly states the tool's dual functionality: converting addresses to coordinates (geocoding) and coordinates to addresses (reverse geocoding) using Google Maps. It specifies the verb (convert) and resource (addresses/coordinates), though it doesn't explicitly differentiate from sibling tools like google_maps_directions or google_maps_places.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like google_maps_directions or google_maps_places, nor does it specify prerequisites, exclusions, or appropriate contexts for geocoding versus reverse geocoding.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the search capability but doesn't cover important aspects like rate limits, authentication requirements, result format, pagination, or error handling. For a search tool with external API integration, this leaves significant gaps in understanding its operational behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately concise with two clear sentences. The first sentence establishes the core functionality, and the second provides basic usage context. There's no unnecessary repetition or fluff, though it could be slightly more structured with explicit separation of functionality and constraints.

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

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

Given the lack of annotations and output schema, the description is incomplete for a 5-parameter search tool. It doesn't explain what the tool returns (formats, fields, limitations), nor does it address authentication, rate limits, or error scenarios. For a Google Maps API integration, users need more context about result structure and API constraints.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, with all 5 parameters well-documented in the schema itself. The description adds minimal value beyond the schema, mentioning 'text query or nearby location' which corresponds to the 'query' and 'location' parameters but doesn't provide additional context about parameter interactions, defaults, or best practices.

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

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

The description clearly states the tool's purpose: 'Search for places using Google Maps Platform' with examples like 'restaurants, shops, landmarks'. It specifies the action (search) and resource (places), but doesn't explicitly differentiate from sibling tools like 'google_maps_directions' or 'kakao_maps_search' beyond mentioning the Google Maps Platform.

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

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

The description provides minimal usage guidance, stating it can search 'by text query or nearby location'. It doesn't specify when to use this tool versus alternatives like 'google_maps_geocode' or 'tmap_poi', nor does it mention prerequisites, exclusions, or typical use cases beyond the basic functionality.

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

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

No annotations are provided, so the description carries full burden. It mentions the API source (Kakao Maps API) and geographic scope (South Korea), but doesn't disclose behavioral traits like rate limits, authentication needs, pagination behavior, error handling, or what the response looks like. For a search tool with 7 parameters and no output schema, this leaves significant gaps in understanding how the tool behaves.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two concise sentences with zero waste. The first sentence states the core purpose, and the second provides targeted usage context. Every word earns its place, and it's front-loaded with essential information.

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

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

Given 7 parameters, no annotations, and no output schema, the description is incomplete. It doesn't cover response format, error conditions, rate limits, or authentication requirements. While it specifies the geographic and linguistic scope, it misses critical behavioral context needed for effective tool invocation, especially for a search tool that likely returns complex results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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 no additional parameter semantics beyond what's in the schema. It doesn't explain relationships between parameters (e.g., how x/y and radius interact) or provide usage examples. With high schema coverage, the baseline 3 is appropriate as the description doesn't add value here.

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

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

The description clearly states the tool's purpose: 'Search Korean places via Kakao Maps API' with specific domains (Korean addresses, local businesses, POI search in South Korea). It distinguishes itself from siblings like google_maps_places by specifying the Korean focus, though it doesn't explicitly name alternatives. The verb 'search' is specific and the resource 'places' is clear.

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

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

The description provides implicit usage context: 'Best for Korean addresses, local businesses, and POI search in South Korea.' This suggests when to use it (Korean-focused searches) but doesn't explicitly state when not to use it or name specific alternatives like google_maps_places. It offers some guidance but lacks explicit comparisons or exclusions.

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

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 adds context beyond the input schema by explaining the smart routing mechanism ('automatically selects the optimal model based on task complexity'), payment model ('pay per call with USDC credits'), and key convenience feature ('No API keys needed'). However, it doesn't cover rate limits, error handling, or response format details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly concise with two sentences that each earn their place: the first explains the core functionality and smart routing, the second covers the business model and key convenience feature. It's front-loaded with the most important information and has zero wasted words.

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

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

Given the tool's complexity (multi-model LLM chat with routing logic) and the absence of both annotations and output schema, the description does a good job covering the essential behavioral aspects. However, it doesn't explain what the tool returns (response format, structure) or potential limitations, 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.

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 doesn't add any parameter-specific semantics beyond what's in the schema (e.g., it doesn't explain 'messages' format or 'temperature' effects). The baseline score 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.

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 ('smart-routed LLM chat completion') and resources ('Claude, GPT, Gemini, Llama'), distinguishing it from siblings by focusing on multi-model LLM chat rather than search, translation, or data queries. It explicitly mentions the core functionality of automatic model selection based on task complexity.

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

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 ('smart-routed LLM chat completion'), but does not explicitly state when not to use it or name alternatives among the sibling tools. It implies usage for chat-based LLM tasks with automatic model routing, but lacks explicit exclusions or comparisons to other tools.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions the return data (titles, abstracts, citation counts, PDF links), it doesn't cover important aspects like rate limits, authentication requirements, error conditions, or pagination behavior. For a search tool with no annotation coverage, this 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly concise with two sentences that each earn their place: the first defines the tool's purpose and scope, the second specifies the return values. No wasted words, and the most important information (what it searches and what it returns) is front-loaded.

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

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

For a search tool with 5 parameters, 100% schema coverage, but no annotations and no output schema, the description provides adequate basic information about purpose and returns. However, it lacks behavioral context (rate limits, errors) and doesn't explain the relationship to sibling tools. The absence of an output schema means the description should ideally provide more detail about the return structure.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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 doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain query syntax or field_of_study options). 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.

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

The description clearly states the specific action ('Search academic papers on Semantic Scholar'), resource ('academic papers'), and scope ('by topic, author, or keyword'). It distinguishes this tool from sibling tools like 'brave_search' or 'google_maps_places' by specifying the academic domain and Semantic Scholar platform.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'brave_search' for general web searches or 'llm_chat' for conversational research. It mentions what the tool does but offers no explicit when/when-not instructions or comparison to sibling tools.

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

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 mentions the tool searches on Tabelog, but doesn't disclose behavioral traits such as rate limits, authentication needs, pagination details (beyond the 'page' parameter in schema), or what the output looks like (e.g., list of restaurants with details). This leaves significant gaps for an agent to understand how the tool behaves.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured in two sentences: the first states the purpose and context, and the second lists search criteria. Every sentence adds value without redundancy, making it easy to parse and front-loaded with key information.

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

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

Given the complexity (a search tool with 5 parameters), no annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like rate limits or output format, and while the schema handles parameters well, the overall context for safe and effective use is lacking. This is inadequate for a tool with multiple inputs and no structured output information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds minimal meaning beyond the input schema, which has 100% coverage with clear parameter descriptions. It lists search criteria ('area, cuisine, rating, or keyword'), aligning with schema parameters, but doesn't provide additional context like format examples or constraints (e.g., rating scale). With high schema coverage, the baseline is 3, and the description doesn't significantly enhance parameter understanding.

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

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

The description clearly states the tool's purpose: 'Search Japanese restaurants on Tabelog — Japan's largest restaurant review site.' It specifies the resource (Japanese restaurants) and the action (search), and provides context about Tabelog. However, it doesn't explicitly differentiate from sibling tools, which are unrelated search tools for different domains (e.g., financial, maps, academic).

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

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

The description implies usage context by listing search criteria ('Find by area, cuisine, rating, or keyword'), suggesting when to use this tool for restaurant searches. However, it lacks explicit guidance on when not to use it or alternatives (e.g., no mention of using other tools for non-restaurant searches). The sibling tools are for different purposes, so differentiation is implied but not stated.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions searching for 'Korean points of interest' and 'Korean address details,' but doesn't cover important aspects like rate limits, authentication needs, pagination behavior, error handling, or what the output looks like. For a search tool with no annotation coverage, this is insufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with two sentences that are front-loaded with the main purpose. However, the second sentence ('Good for finding specific locations with Korean address details.') could be more integrated or omitted if it doesn't add significant value, as it partially overlaps with the first.

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

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

Given the complexity of a search tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits, output format, error conditions, and differentiation from siblings. While the schema covers parameters well, the overall context for an AI agent to use this tool effectively is inadequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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. The description adds minimal value beyond the schema by implying Korean language support for the search keyword, but doesn't provide additional context like format examples or usage tips. This meets the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose: 'Search Korean points of interest via TMap.' It specifies the action (search), resource (points of interest), and geographic focus (Korean). However, it doesn't explicitly differentiate from sibling tools like 'kakao_maps_search' or 'google_maps_places' beyond mentioning TMap as the provider.

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

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

The description provides minimal guidance: 'Good for finding specific locations with Korean address details.' This implies usage for Korean addresses but doesn't specify when to use this tool versus alternatives like 'kakao_maps_search' or 'google_maps_places', nor does it mention any prerequisites or exclusions.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'real-time traffic' and the API source, but fails to disclose critical traits such as rate limits, authentication needs, error handling, or whether the operation is read-only or has side effects. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured in two sentences: the first states the core functionality and API, and the second lists the return values. Every sentence earns its place by providing essential information without redundancy, making it front-loaded and appropriately sized for the tool's complexity.

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

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

Given the tool's moderate complexity (6 parameters, no output schema, no annotations), the description is partially complete. It covers the purpose and return values but lacks usage guidelines, behavioral details, and output schema explanation. Without annotations or output schema, the description should do more to compensate, but it meets a minimum viable level by specifying key aspects like the API and returns.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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 (e.g., startX as 'Start longitude (WGS84)'). The description adds no additional meaning beyond the schema, such as explaining coordinate formats or the optionality of startName/endName. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't detract either.

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

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 ('Get Korean driving routes') and resources ('via TMap Navigation API'), distinguishing it from sibling tools like google_maps_directions by specifying the Korean focus and TMap API. It explicitly mentions the return values (distance, ETA, toll fees, turn-by-turn directions), making the purpose unambiguous.

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

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

The description provides no guidance on when to use this tool versus alternatives like google_maps_directions or kakao_maps_search, nor does it mention any prerequisites or exclusions. It implies usage for Korean driving routes but lacks explicit context for tool selection, leaving the agent without comparative guidance.

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