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 'live' data, implying real-time or current information, but fails to specify data sources, update frequency, rate limits, authentication needs, or error handling. For a tool with zero annotation coverage, 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 a single, efficient sentence that front-loads the core purpose ('Get live Bittensor (TAO) price, network stats') and adds relevant details ('breakdown of passive income options including staking yield') without any wasted words. Every part of the sentence contributes to understanding the tool's function.

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 (simple data retrieval with 0 parameters) and lack of annotations/output schema, the description is minimally complete—it states what data is returned but omits behavioral details like data freshness, sources, or format. It's adequate for basic use but leaves operational questions unanswered, fitting a score of 3 as the minimum viable.

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 with 100% schema description coverage, so the schema fully documents the lack of inputs. The description adds no parameter-specific information, which is acceptable since no parameters exist. A baseline of 4 is appropriate as the description doesn't need to compensate for any parameter gaps.

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 live Bittensor (TAO) price, network stats') and resources ('Bittensor (TAO)', 'passive income options'), distinguishing it from siblings like get_crypto_price (general crypto) or get_stock_quote (stocks). It explicitly identifies the target cryptocurrency and the types of data returned.

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 specifying 'Bittensor (TAO)' and 'passive income options including staking yield', which guides when to use this tool (for TAO-specific data with income insights) versus siblings like get_crypto_price (general prices) or get_global_crypto_market (broad market data). However, it lacks explicit exclusions or named alternatives.

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. It discloses key behavioral traits: it fetches live data, supports a wide range of coins, and uses CoinGecko as the source. However, it does not mention rate limits, error handling, or authentication needs, leaving gaps in operational context for a tool that likely queries an external API.

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 with the core purpose and efficiently lists the returned metrics and supported coins in a single, dense sentence. Every element (e.g., data points, coin examples, source) serves to clarify usage without redundancy, making it highly concise and well-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 the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers what the tool does and the data returned, but lacks details on output format, error cases, or performance constraints, which are important for an API-based tool without structured output documentation.

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 (coin_id and currency) thoroughly. The description adds marginal value by reinforcing the CoinGecko ID usage and listing example coins, but does not provide additional syntax or format details beyond what the schema specifies, aligning with the baseline for high 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 specific action ('Get live crypto price') and resources ('Bitcoin, Ethereum, TAO (Bittensor) and 10,000+ coins'), distinguishing it from siblings like get_bittensor_stats (specific to Bittensor) or get_stock_quote (stocks). It explicitly lists the data returned (price, 24h/7d change, market cap, volume, ATH), making the purpose highly specific and differentiated.

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 by specifying the data source ('via CoinGecko ID') and supported coins, which helps guide usage. However, it does not explicitly state when to use alternatives like get_multiple_crypto_prices (for multiple coins) or get_top_movers (for trending data), so it lacks explicit exclusions or comparisons to siblings.

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. It discloses the 0-100 scale interpretation and historical data scope, but doesn't mention rate limits, data freshness, source reliability, or error conditions. The behavioral disclosure is adequate but lacks operational 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?

Single sentence efficiently conveys purpose, scale interpretation, data scope, and use context. Every element earns its place with zero redundant information. The structure is front-loaded with the core function.

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 zero-parameter tool with no annotations and no output schema, the description provides good context about what data is returned (current index, 7-day history, interpretation). However, it doesn't specify the exact return format (e.g., JSON structure) or whether the interpretation is textual or categorical.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately focuses on what the tool returns rather than inputs. A baseline of 4 is appropriate for zero-parameter tools.

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 ('Get'), resource ('Crypto Fear & Greed Index'), and scope ('with 7-day history and investment interpretation'). It distinguishes from sibling tools like get_crypto_price or get_global_crypto_market by focusing on this specific sentiment index rather than price or market 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 implies usage context by mentioning 'investment interpretation' and the 0-100 scale with emotional extremes, suggesting this tool is for market sentiment analysis. However, it doesn't explicitly state when to use this versus alternatives like get_top_movers for volatility or get_global_crypto_market for broader metrics.

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 data is returned but does not mention critical behavioral traits such as data sources, update frequency, rate limits, authentication needs, or error handling. For a data-fetching tool with zero annotation coverage, 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 front-loads the purpose ('Get global crypto market overview') and lists key metrics without unnecessary words. Every element earns its place, making it highly concise and well-structured for quick understanding.

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 simplicity (0 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the purpose and output metrics but lacks behavioral context (e.g., data freshness, limitations). For a straightforward read-only tool, it meets minimum viability but could benefit from additional operational details.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately focuses on the tool's purpose and output metrics, adding value by specifying what data is retrieved without redundant parameter details. Baseline is 4 for zero-parameter tools.

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 ('Get') and resource ('global crypto market overview'), listing the exact metrics returned: total market cap, BTC dominance, 24h volume, and active coin count. It distinguishes this tool from siblings like get_crypto_price (single price) or get_top_movers (movement-focused) by emphasizing a comprehensive market overview.

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 obtaining a broad market snapshot, but does not explicitly state when to use this tool versus alternatives like get_fear_greed_index (sentiment) or get_portfolio_summary (personal holdings). It provides context but lacks explicit guidance on exclusions or comparisons 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'live prices' and 'at once', hinting at real-time data and batch processing, but lacks critical details like rate limits, data freshness, error handling, or response format. For a tool fetching financial data, 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 highly concise with two sentences that directly convey the core functionality and a key use case. Every word earns its place, and it's front-loaded with the primary purpose. No wasted verbiage 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 the tool's moderate complexity (batch price fetching), lack of annotations, and no output schema, the description is minimally adequate but incomplete. It covers the 'what' but misses behavioral details (e.g., rate limits, data sources) and output expectations. With 100% schema coverage, it meets a basic threshold but doesn't fully compensate for the missing structured data.

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 fully documents both parameters (coin_ids and currency). The description adds no additional parameter semantics beyond what's in the schema, such as examples of valid coin IDs beyond the schema's 'bitcoin,ethereum,bittensor' or clarification on currency support. Baseline 3 is appropriate when the schema does all the work.

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 a specific verb ('Get') and resource ('live prices for multiple cryptocurrencies'), distinguishing it from single-crypto tools like 'get_crypto_price'. However, it doesn't explicitly differentiate from portfolio-related siblings like 'get_portfolio_summary' beyond the 'portfolio snapshot' hint.

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 'Perfect for a portfolio snapshot', suggesting when this tool might be preferred. However, it doesn't provide explicit guidance on when to use this versus alternatives like 'get_crypto_price' (single coin) or 'get_portfolio_summary' (broader portfolio data), nor does it mention any exclusions or prerequisites.

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 mentions 'real-time P&L' calculation and return format ('per-asset breakdown and total portfolio performance'), but doesn't specify data sources, update frequency, accuracy limitations, or error handling. For a financial calculation tool with no annotation coverage, this leaves significant behavioral aspects undocumented.

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 sentence states the core functionality, and the second specifies the return format. No wasted words, and the information is front-loaded with the primary purpose stated immediately.

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 calculation tool with no annotations and no output schema, the description provides adequate basic information but leaves gaps. It covers what the tool does and what it returns at a high level, but doesn't address important contextual details like calculation methodology, data freshness, error conditions, or performance characteristics that would be important 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%, providing complete parameter documentation. The description adds minimal value beyond the schema - it mentions 'portfolio of crypto and/or stocks' which aligns with the asset parameter descriptions, but doesn't provide additional context about parameter usage, validation rules, or edge cases. 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: 'Calculate real-time P&L for a portfolio of crypto and/or stocks.' It specifies the verb (calculate) and resource (portfolio P&L), and distinguishes from siblings by focusing on portfolio-level analysis rather than individual asset data. However, it doesn't explicitly contrast with all sibling tools like get_top_movers which might also involve portfolio-related metrics.

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 ('for a portfolio of crypto and/or stocks') but doesn't provide explicit guidance on when to use this tool versus alternatives. For example, it doesn't clarify if this should be used instead of get_multiple_crypto_prices for portfolio calculations or how it differs from get_top_movers in portfolio analysis scenarios. The context is clear but lacks specific when/when-not instructions.

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 states the tool provides 'real-time' quotes, which is useful context, but doesn't cover other important aspects like rate limits, data sources, error handling, or what the output looks like (e.g., price, volume, timestamp). For a tool with zero 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 extremely concise and front-loaded, with every sentence earning its place. The first sentence states the core purpose, and the second provides essential examples without unnecessary details. There's zero waste, 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 the complexity of financial data tools, no annotations, and no output schema, the description is incomplete. It lacks information on return values (e.g., what fields are included in the quote), error cases, authentication needs, or performance characteristics. While concise, it doesn't provide enough context for reliable use without additional assumptions.

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, with the 'symbol' parameter clearly documented. The description adds value by providing concrete examples (e.g., 'AAPL', 'RELIANCE.NS') and clarifying regional distinctions (US vs. Indian NSE stocks), which helps users understand valid inputs beyond the schema's generic description. However, it doesn't explain parameter constraints or formats in depth.

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 a specific verb ('Get') and resource ('real-time stock quote'), making it immediately understandable. However, it doesn't explicitly distinguish this tool from its siblings (like 'get_crypto_price' or 'get_top_movers'), which would require mentioning it's specifically for stocks rather than other asset classes.

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 lists example symbols but doesn't explain why to choose this over sibling tools like 'get_crypto_price' for cryptocurrencies or 'get_top_movers' for market trends. There's no mention of prerequisites, limitations, or comparative 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes what data is retrieved but lacks details on behavioral traits such as rate limits, authentication needs, data freshness, or error handling. For a data-fetching tool with zero annotation coverage, 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 a single, efficient sentence that front-loads the core functionality without unnecessary details. Every word contributes to understanding the tool's purpose, making it appropriately sized and well-structured for quick comprehension.

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 low complexity (one optional parameter, no output schema), the description is minimally complete. It covers the what and scope but lacks context on behavioral aspects like performance or data sources. Without annotations or an output schema, more detail on return values or usage constraints would improve completeness for this data retrieval tool.

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, with the 'limit' parameter fully documented in the schema. The description adds no parameter-specific information beyond what the schema provides, such as constraints on the limit value or how gainers/losers are ranked. With high schema coverage, the baseline score of 3 is appropriate.

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 top gaining and losing cryptocurrencies in the last 24h from the top 100 by market cap.' It specifies the verb ('Get'), resource ('cryptocurrencies'), timeframe ('last 24h'), and scope ('top 100 by market cap'). However, it doesn't explicitly differentiate from siblings like 'get_global_crypto_market' or 'get_crypto_price', which could provide overlapping market 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 implies usage context by specifying 'top gaining and losing' and 'last 24h', suggesting it's for tracking short-term market movements. However, it provides no explicit guidance on when to use this tool versus alternatives like 'get_global_crypto_market' for broader metrics or 'get_crypto_price' for specific assets. The usage is clear but lacks comparative guidance.

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