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

With no annotations, the description carries full burden but only states a generic 'analysis' behavior. It does not disclose whether the tool is read-only, requires authentication, or has side effects like data mutation or rate limits.

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 a clear one-line purpose followed by a parameter list. No extraneous information, though the structure could be improved with section headers.

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?

Without output schema or annotations, the description omits critical context such as return format, error handling, data source specifics, and how results relate to other tools. This is insufficient for a 5-parameter analysis 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 Args section adds basic meaning to each parameter (e.g., exchange examples, timeframe options, ranges), but lacks precision (e.g., 'min_size_increase' as percentage of what? No enum lists for allowed values). Given 0% schema coverage, the description partially compensates but could be more detailed.

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 it performs 'Advanced candle pattern analysis using multi-timeframe data', which is a specific verb+resource combination. However, it does not explicitly differentiate from sibling tools like 'consecutive_candles_scan' or 'multi_timeframe_analysis', leaving some ambiguity.

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, nor any context about prerequisites or expected scenarios. It only lists parameters without explaining 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?

The description discloses that the tool runs a backtest on historical data, optionally including trade log and equity curve. It implies a read-only computation. Without annotations, this is sufficient, though it could mention data sources or performance characteristics.

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?

Well-structured with a clear one-sentence summary followed by an Args list. Slightly verbose but all information is relevant and efficiently presented.

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?

Covers inputs thoroughly but does not describe the output structure beyond mentioning 'institutional-grade metrics' and optional logs. Without an output schema, the description should provide more detail on return format.

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 provides comprehensive parameter documentation with examples (e.g., symbol examples, strategy list, period options), default values, and units. This adds significant meaning beyond the schema, which only has types and defaults.

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?

Describes the tool as backtesting a trading strategy on historical data with institutional-grade metrics. The verb 'backtest' and resource 'strategy' are specific, but the description does not differentiate from closely related siblings like 'compare_strategies' or 'walk_forward_backtest_strategy'.

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 explicit guidance on when to use this tool versus alternatives. The description lists parameters but does not provide context for strategy selection, period, or comparison with other backtesting 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 bears full responsibility for behavioral disclosure. It describes the tool's function (scanning for low BBW) but does not mention side effects, permissions, rate limits, or whether it modifies data. The 'squeeze detection' label adds some context, but overall transparency is limited.

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: a short paragraph followed by a structured list of arguments. Every sentence adds value, and the purpose is front-loaded. No superfluous content.

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 (4 optional params, no required params, scan output), the description covers what the tool does, the assets it works with, and all parameters' meanings. An output schema exists but is not described, which is acceptable. The description is complete for an agent to correctly invoke the 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?

Schema description coverage is 0%, yet the description provides detailed semantics: it lists exchange with specific exchanges for crypto and stocks, timeframe with example values, bbw_threshold with default, and limit with max. This adds significant meaning beyond the schema's types and defaults.

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 scans for assets with low Bollinger Band Width (squeeze detection) and specifies it works with both crypto and stocks. This provides a specific verb and resource, distinguishing it from sibling tools like volume_breakout_scanner or market_snapshot.

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 does not explicitly state when to use this tool versus alternatives. It implies usage for squeeze detection but lacks guidance on exclusions or comparisons to sibling tools. Adequate for basic understanding but missing explicit 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?

No annotations are provided, so the description must disclose behavioral traits. It only states that it returns 'detailed analysis with all indicators and metrics' without mentioning side effects, authentication needs, rate limits, or whether it is read-only.

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 concisely structured with clear args and returns sections. Every sentence adds value, and the examples are compact yet informative. No 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?

The description adequately covers the parameters and returns a detailed analysis, but lacks specifics on what indicators and metrics are included. Given the absence of an output schema, more detail on the output format 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?

The description adds substantial meaning beyond the input schema by providing concrete examples for symbol (crypto and stock formats), exchange (list of valid exchanges), and timeframe (allowed intervals). With 0% schema coverage, this is essential and well-executed.

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?

Description clearly states it provides detailed analysis for a specific asset on an exchange and timeframe, with examples of symbols and exchanges. However, it does not explicitly differentiate from sibling tools like advanced_candle_pattern or bollinger_scan, which also provide analysis.

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. The description lists examples but does not explain when to prefer coin_analysis over other analysis tools like multi_timeframe_analysis or market_snapshot.

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 bears full responsibility. While it labels the tool as a 'POWER TOOL', it does not disclose behavioral traits such as rate limits, cost, required permissions, or what the combined output looks like (e.g., a summary or raw 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 brief without unnecessary words, but it is too minimal. It could benefit from a structured output format or bullet points. It earns its place but lacks depth.

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 (combining three data sources), the absence of output schema, and 0% schema coverage, the description is severely incomplete. It fails to explain what is returned, how results are presented, or any limitations (e.g., if news only covers certain markets).

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 0%, so the description must add meaning. It provides example values for symbol, exchange, and timeframe but does not explain the role of each parameter (e.g., how exchange affects data sources) or acceptable formats beyond the examples.

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 it combines TradingView technical analysis, Reddit sentiment, and financial news, making the purpose specific and actionable. However, it does not differentiate from siblings like market_sentiment or financial_news, which could cause confusion.

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 market_sentiment or financial_news. It lacks context on prerequisites or situations where the combined analysis is preferable.

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 exist, so the description carries full burden. It discloses use of Yahoo Finance data and runs exactly 6 strategies, but doesn't detail pagination, output format, rate limits, or what happens on missing 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 concise, front-loads the main action (run all 6 strategies, return leaderboard), and uses a clean Args list with minimal but sufficient detail. No 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 no output schema, the description should clarify the leaderboard structure. It only says 'ranked leaderboard' without specifying columns or metrics. This is a minor gap. Otherwise, for a 4-param tool with good param descriptions, it's fairly complete.

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 coverage is 0%, but the description provides meaningful context for all parameters: symbol is a Yahoo Finance symbol with examples, period lists accepted values with defaults, initial capital is described as starting capital in USD, and interval is explained as daily or hourly. This adds value beyond the schema's bare types.

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 explicitly states it runs all 6 named strategies and returns a ranked leaderboard, making the tool's purpose specific and distinguishable from siblings like backtest_strategy which likely focuses on a single strategy.

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 the tool is for comparing multiple strategies, but does not provide explicit when-not-to-use or alternative tools. However, from sibling names, it's clear when this should be preferred over backtest_strategy or bollinger_scan.

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 does not mention authorization needs, rate limits, side effects, or return format. The description essentially lists parameters without explaining behaviors beyond parameter semantics.

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, starting with a clear one-line purpose followed by a parameter list. It avoids unnecessary words, but the parameter list is somewhat dense without formatting. Still, it is efficient and 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?

Given the tool has 6 parameters and no output schema or annotations, the description covers the purpose and parameter semantics adequately. However, it lacks details on return value format, error handling, and edge cases, which are important for a scanning tool. This is adequate but has clear 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 input schema has 0% description coverage, leaving parameter meanings entirely to the description. The description provides clear, human-readable explanations for all 6 parameters, including acceptable values (e.g., 'bullish' vs 'bearish', timeframes). This fully compensates for the schema's lack of descriptions.

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 scans for coins with consecutive growing/shrinking candles pattern, which is a specific verb and resource. It distinguishes itself from sibling tools like 'advanced_candle_pattern' and 'bollinger_scan' by focusing on consecutive candles. However, it does not explicitly differentiate from all siblings, so a 4 is appropriate.

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 does not mention exclusions, prerequisites, or context for use. The agent is left to infer usage from the name and 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, the description carries full behavioral transparency burden, but it only lists parameters. It does not disclose any side effects, dependencies, or computational details, leaving agents without essential 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 with a clear purpose statement followed by a structured parameter list. It wastes no words, but the format is human-readable rather than optimized for quick agent parsing.

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?

The description lacks any mention of output format or return values, which is critical given no output schema. It also omits broader context about Fibonacci retracement levels or interpretation, making it incomplete for effective 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?

Despite 0% schema coverage, the description documents all three parameters (symbol, lookback, timeframe) with examples and default values. This adds significant meaning beyond the raw schema, though it could elaborate on how these values affect the calculation.

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 explicitly states 'Fibonacci retracement analysis for EGX stocks', clearly identifying the tool's purpose and resource. While it differentiates from siblings like 'egx_index_analysis' through its specific name, no explicit comparison is made.

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 is provided on when to use this tool versus alternatives, such as other EGX analysis tools. There is no mention of prerequisites, limitations, or appropriate use cases.

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 must disclose behavioral traits. It only mentions 'full indicators' without clarifying side effects, output format, or if it's read-only. The vague term 'full indicators' lacks specificity about what operations are performed.

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 a one-sentence purpose followed by a parameter list. It is front-loaded and efficient, but the list format could be more clearly structured with separators or explanations.

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 0% schema coverage, the description is insufficient. It lacks details on output behavior, constraints, and the nature of 'full indicators'. For a tool with three parameters, more context 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 coverage is 0%, so the description must compensate. It specifies allowed values for index and timeframe, and defines limit as 'Number of stocks to show in detail'. This adds significant meaning beyond the schema, though it could detail what 'full indicators' entails.

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 it analyzes an EGX index constituent performance with full indicators, and lists specific index values. It distinguishes from siblings like egx_market_overview (broader market) and egx_stock_screener (individual stocks).

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 valid index choices and parameters but does not explicitly state when to use this tool over alternatives like egx_market_overview or egx_sector_scan. It implies usage for constituent-level index analysis but lacks explicit guidance.

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 must convey behavioral traits. It only mentions 'comprehensive overview' without detailing what the output contains, data freshness, or any side effects. This is insufficient for a mutation-free tool.

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: one line for purpose followed by bullet-style parameter explanations. It is front-loaded and avoids unnecessary wording, though the Args section could be 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 the lack of output schema and annotations, the description is incomplete. It does not describe the return structure (e.g., which categories, metrics). The tool's complexity (market overview) requires more detail to be fully useful.

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 explains both parameters: timeframe (with allowed values and default) and limit (with max value). This adds significant value beyond the schema, which has no descriptions and no enums, compensating for the 0% 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 'Get a comprehensive overview of the Egyptian Exchange (EGX) market' with a specific verb and resource. However, no differentiation from sibling tools like egx_index_analysis or egx_sector_scanner is provided.

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. The description does not specify appropriate contexts or exclusions, leaving the agent to infer usage from the name 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, the description partially compensates by noting that leaving 'sector' empty lists all sectors. However, it does not disclose output format, side effects, or other behavioral traits.

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 a clear two-line summary followed by bulleted parameter details. Every sentence provides value without repetition.

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?

The description covers basic functionality and parameter semantics but omits the return format or example output. Given no output schema, this gap reduces completeness for a scanning 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?

Schema description coverage is 0%, but the description fully compensates by explaining each parameter: sector names (examples), timeframe options, and limit max. This adds crucial meaning beyond the schema defaults.

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 it scans EGX stocks by sector and shows available sectors if none specified. However, it does not differentiate from the sibling tool 'egx_sector_scanner', which could cause confusion.

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 is given on when to use this tool versus alternatives like 'egx_sector_scanner' or other scanning tools. The description only states functionality without 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?

No annotations are provided, so the description must disclose behavioral traits. It states the tool 'scans' and 'identifies', implying read-only behavior, but does not explicitly confirm non-destructiveness, performance implications, or authentication needs.

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

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

The description is highly concise: one sentence for purpose, followed by a clean parameter list. No redundant information, every sentence is useful.

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 output schema, the description should explain the return value format (e.g., list of sectors, scores, stock picks). It only mentions 'identifies hot/cold sectors and top picks' without details on structure or data fields, leaving ambiguity.

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?

With 0% schema description coverage, the description effectively documents all 4 parameters: allowed values (e.g., timeframe options), ranges (e.g., top_n_sectors 1-18), and defaults. This adds clear meaning beyond schema titles and defaults.

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 is a 'Sector rotation scanner for EGX — identifies hot/cold sectors and top picks.' This is specific and actionable. However, it does not differentiate from the sibling tool 'egx_sector_scan', which may have similar 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?

No guidance is given on when to use this tool versus alternatives like egx_sector_scan or egx_market_overview. The description only lists parameters but lacks contextual advice about scenarios 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?

No annotations are provided to indicate read-only or destructive behavior. The description does not disclose data source, latency, rate limits, or whether results are real-time. For a ranking engine, these details are important for proper usage.

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 a clear purpose statement followed by a structured list of parameters. There is no unnecessary text. However, it could be better organized with distinct sections.

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?

No output schema is provided, and the description does not explain the return format (e.g., list of stocks with scores, key fields). For a screening tool, this is a significant gap that reduces 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?

Despite 0% schema coverage, the description documents each parameter with allowed values and defaults (e.g., timeframe options, min_score range 0-100, index_filter list, limit max 50). This adds significant meaning beyond the bare schema.

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 it is a 'stock ranking engine for EGX' that 'finds strong stocks with actionable setups'. This differentiates it from siblings like 'top_gainers' or 'volume_breakout_scanner', though it could be more specific about what constitutes 'actionable setups'.

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 alternative scanners or analysis tools. It does not mention prerequisites, exclusions, or scenarios where other tools would be more appropriate. Usage is only implied by the purpose statement.

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 disclosure. It only states the tool generates a trade plan, without revealing behavioral traits such as whether it is read-only, requires real-time data, or has side effects. The lack of detail on what a 'full trade plan' entails further reduces 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 extremely concise and front-loaded: one sentence for purpose, then clean parameter documentation. No unnecessary words or sentences, every piece 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 output schema and no annotations, the description is incomplete. It fails to describe what the output (the trade plan) contains, such as entry/exit points, risk levels, or supporting analysis. The tool's complexity warrants more detail for the agent to understand the return value.

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 has 0% description coverage, so the description must compensate. It adds examples for symbol and explicitly lists allowed timeframe values with a default, providing useful context beyond the bare schema. However, it does not explain the impact of different timeframes on the trade plan.

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 generates a full trade plan for a specific EGX stock, with a specific verb and resource. It distinguishes itself from sibling tools like egx_fibonacci_retracement and egx_index_analysis by focusing on trade plan generation.

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 does not mention prerequisites, contexts, or when not to use it, leaving the agent to infer usage without explicit 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, the description must disclose side effects, auth needs, or rate limits. It only mentions 'real-time' and RSS feeds but does not state read-only nature, result structure, or any constraints. The description is insufficient for fully understanding 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 short, directly states purpose, and lists parameters in a clear argument block. Every sentence adds value with no redundancy or filler.

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 output schema and low complexity (3 optional params), the description covers the tool's inputs and purpose. However, it omits what the return data looks like (e.g., news title, date, source) and any pagination or ordering details. A simple 'returns list of news items with headline, source, and URL' 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?

The input schema has 0% description coverage, so the description must compensate. It explains each parameter: symbol is an optional filter with example values, category lists possible feed categories ('crypto', 'stocks', 'all'), and limit sets max items. This adds meaningful context beyond the schema's names and defaults.

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 it provides 'real-time financial news from RSS feeds' and lists specific sources (Reuters, CoinDesk). It distinguishes itself from sibling tools that perform analysis (e.g., backtest_strategy, market_sentiment) by focusing on raw news fetching.

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 real-time news is needed but provides no explicit guidance on when to use this tool versus alternatives like market_sentiment or market_snapshot. No 'when not to use' or sibling comparisons are given.

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 the full burden. It indicates the tool provides 'real-time' sentiment analysis but does not elaborate on behavior such as data source reliability, update frequency, or potential limitations. The transparency is minimal but not misleading.

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, consisting of a one-line purpose statement followed by a brief structured list of arguments. Every sentence adds value with no wasted words. The most important information 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?

Given the tool has 3 parameters and no output schema, the description adequately explains inputs but fails to describe the return format or what 'sentiment analysis' precisely yields. It is adequate for basic use but lacks completeness for complex decision-making.

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 provides only types and defaults, with 0% coverage. The description adds significant meaning by explaining each parameter: symbol with examples, category with explicit options, and limit as the number of posts. This compensates fully for the schema's lack of 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 'Real-time Reddit sentiment analysis for stocks and crypto' using a specific verb and resource. It distinguishes this tool from siblings as no other sibling provides Reddit sentiment analysis, making its purpose unique and well-defined.

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 sentiment analysis but does not provide explicit guidelines on when to use this tool versus alternatives, nor does it mention conditions where it should not be used. Some context is given by the parameter explanations, but no direct usage guidance.

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 must carry the full burden. It mentions being 'Powered by Yahoo Finance' but does not disclose data freshness, latency, rate limits, or other behavioral traits that affect agent decisions.

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 one substantive sentence plus a data source note. It could be slightly more structured, but it efficiently conveys the tool's content.

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 output schema, the description should explain the return format or structure more comprehensively. It lists categories but lacks specifics on which items are included or how data is presented, leaving ambiguity for the 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?

The tool has no parameters and 100% schema coverage. The description adds value by listing the categories of data returned (indices, crypto, FX, ETFs), which is more informative than the empty schema. Baseline 4 for 0 params 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 it provides a global market overview of major indices, top crypto, FX rates, and key ETFs. It distinguishes itself from more specialized sibling tools like 'coin_analysis' or 'egx_market_overview' by focusing on a broad snapshot.

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 use for a quick market check but does not provide explicit guidance on when to use this tool vs alternatives, or when not to use it. Given the many sibling tools, this is a 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?

The description discloses the core behavior: a structured debate among three AI agents culminating in a trading decision. With no annotations provided, this is adequate but lacks details on data sources (real-time vs historical) or rate limits.

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, front-loaded with the purpose, and uses a clear Args/Returns structure. Every sentence adds value with no 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 complexity of a multi-agent debate tool, the description covers inputs well with examples but omits details about the debate structure or output format. However, it states the outcome (final trading decision), which is sufficient for basic usage.

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 significant meaning beyond the bare input schema by providing concrete examples for each parameter (e.g., symbol 'BTCUSDT', exchange 'KUCOIN', timeframe '15m') and clarifying acceptable values across asset classes. This compensates for the schema's lack of descriptions and enums.

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 ('Run a multi-agent debate'), the resource ('Technical, Sentiment, Risk'), and the scope ('for a specific symbol'). It distinguishes itself from sibling technical indicator tools by focusing on a debate of agents.

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 this tool is for multi-perspective analysis but does not explicitly state when to use it over alternatives like combined_analysis or coin_analysis. No exclusions or when-not 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. It only describes inputs and timeframes, lacking details on what the analysis entails (e.g., indicators, outputs), whether it's read-only, or any side effects.

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 with purpose. The args section is efficiently formatted without extraneous content. However, the example-heavy approach for parameters slightly reduces 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 lack of output schema and behavioral info, the description is incomplete for a multi-timeframe analysis tool. It fails to differentiate from 27 sibling tools or explain what results to expect.

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 0%, so the description must add meaning. It provides example values for symbol and exchange but does not explain constraints, formats beyond examples, or purpose of each parameter, offering only superficial guidance.

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 states 'Multi-timeframe alignment analysis (Weekly → Daily → 4H → 1H → 15m)', which conveys the tool's function but lacks an explicit verb. It does not distinguish from sibling tools like combined_analysis or multi_agent_analysis, making it somewhat vague.

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 siblings. The description implies it's for multi-timeframe analysis but gives no context about prerequisites, alternatives, or scenarios where other tools are preferred.

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 does not disclose behavioral traits beyond the basic filtering operation. It omits information about rate limits, authentication needs, or whether the tool modifies any state, which is important for a read-like tool.

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 with a one-line summary followed by structured parameter explanations. It wastes no words, though the 'Args:' prefix is slightly redundant.

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 presence of an output schema, the description adequately covers input semantics. However, it lacks details on potential errors, case sensitivity for exchange names, or whether the output is sorted. This is acceptable for a filter tool but not fully complete.

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 has 0% description coverage, but the description compensates by explaining exchange examples, timeframe options, rating meanings (e.g., -3=Strong Sell), and a limit maximum. This adds significant meaning beyond the raw schema.

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 filters coins by Bollinger Band rating, using a specific verb and resource. However, it does not differentiate itself from the sibling tool 'bollinger_scan', which may 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?

No explicit guidance is provided on when to use this tool versus alternatives like bollinger_scan or multi_timeframe_analysis. The description only lists parameters, leaving the context of appropriate usage implicit.

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 provided, so description must cover behavior. It describes input parameters but does not disclose that the tool is read-only, non-destructive, or any potential rate limits. The output schema exists but is not detailed in the description.

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 brief and front-loaded with the purpose. The Args list is concise and includes defaults. Every sentence is necessary and well-formatted.

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 (5 parameters, output schema exists), the description adequately covers input param semantics. It does not explain the exact combination logic (e.g., AND/OR conditions) but defaults are clear from schema. Slightly incomplete on return value usage, but output schema handles that.

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 significant meaning beyond the input schema titles. It explains 'min_volume_ratio' as minimum volume multiplier, 'min_price_change' as percentage, and defines 'rsi_range' values (oversold, overbought, neutral). This fully compensates for the 0% schema description 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 it is a 'Smart volume + technical analysis combination scanner,' using specific verbs and resources. It distinguishes itself from sibling tools like volume_breakout_scanner and bollinger_scan by combining volume and technical analysis.

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 does not explicitly state when to use this tool over alternatives. It implies use for combined volume and technical scanning but lacks clear context or exclusion criteria.

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 use of Bollinger Bands but does not disclose behavioral traits such as data freshness, rate limits, or whether the operation is read-only.

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 with the main purpose. It uses a clear docstring format. Minor redundancy in parameter lists could be trimmed.

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?

The description covers inputs adequately for an agent to decide to call the tool. Since an output schema exists, return value details are not needed here. However, the algorithm basis is vague, and no context on data recency or sorting is provided.

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 coverage is 0%, but the description compensates by explaining each parameter: valid exchange values, timeframe periods, and the limit meaning 'Number of rows to return (max 50)'. This adds significant meaning beyond the schema's titles and defaults.

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 verb 'Return' and the resource 'top gainers' with a specific method 'Bollinger Band analysis'. It distinguishes from the sibling 'top_losers' by focusing on gainers, but does not explicitly differentiate from other scanning 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 provides valid parameter values (exchanges, timeframes, limit) which imply usage contexts, but lacks explicit guidance on when to use this tool versus alternatives like bollinger_scan or coin_analysis.

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 explains the tool returns top losers but does not disclose sorting order, pagination behavior, or error handling. Adequate but basic.

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?

Two concise sentences, front-loaded with the core purpose. Every sentence adds value without 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 simple listing tool with an output schema, the description is adequate but incomplete. It fails to specify sorting order, acceptable timeframe values, or how 'limit' is applied. Missing details reduce 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 0%, and the description does not detail parameter semantics. It lists supported exchanges but does not explain 'timeframe' options, 'limit' usage, or default behaviors beyond the schema defaults.

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 returns top losers for an exchange and timeframe, listing supported exchanges. It distinguishes from sibling 'top_gainers' by explicitly focusing on losers.

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 interested in losers but provides no explicit when-to-use or when-not-to-use guidance relative to siblings like 'top_gainers' or 'market_snapshot'.

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 provided, so description must supply behavioral context. Lacks details on real-time vs historical, data source, rate limits, or output format details. Only parameter defaults hint at 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?

Description is well-structured with an Args block. It is concise and front-loaded with the purpose. However, the Args block could be slightly more compact.

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 5 parameters and an output schema, the description covers the necessary input context. Output schema exists, so return values are not required in description. Acceptable for a scanner 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?

All 5 parameters are explained in the description with examples (e.g., exchange names like KUCOIN, BINANCE) and defaults. Since schema coverage is 0%, this description compensates well.

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?

Clearly states the tool detects coins with volume breakout and price breakout, using a specific verb and resource. It distinguishes from sibling tools like smart_volume_scanner and bollinger_scan.

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 explicit guidance on when to use this tool vs alternatives. Does not provide when-not usage or comparisons to siblings. Users must infer from parameter descriptions.

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 for behavioral disclosure. It only says 'detailed volume confirmation analysis' without explaining what that entails operationally, such as data sources, side effects, or output format.

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 a single-line purpose followed by an Args list. It is appropriately front-loaded, but could add slight detail without bloat.

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 output schema and no annotations, the description lacks information about return values and behavioral context. The agent has insufficient information to use the tool confidently.

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 0%, and the description merely repeats parameter names with example values. It does not explain valid timeframes, symbol format constraints, or how parameters affect analysis. Compensation is minimal.

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 states it performs 'Detailed volume confirmation analysis for a specific coin', clearly indicating verb and resource. However, it does not differentiate from sibling tools like volume_breakout_scanner or smart_volume_scanner, which also analyze volume.

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 is provided on when to use this tool versus alternatives. There is no mention of prerequisites, exclusions, or context, leaving the agent to guess.

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 bears full burden. It describes the backtest process but does not disclose behavioral traits such as whether it modifies data, permissions needed, or if it's read-only. Lacks explicit safety information.

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 a one-line summary and a clear Args list. Every sentence adds value, and the structure is front-loaded with the core purpose.

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?

While the description covers parameters well, it omits mention of the return value/output, which is important given no output schema. Also lacks context on execution time or data prerequisites. Moderately complete but missing key 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?

With 0% schema coverage, the description fully explains all 9 parameters in the Args section, including examples for symbol, strategy options, period recommendations, and defaults. This adds significant meaning beyond the schema.

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 states it performs a walk-forward backtest to detect overfitting, validating on unseen data. This clearly specifies the verb 'validates' and the resource 'strategy', and distinguishes it from sibling tools like simple backtest_strategy.

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 overfitting detection but does not explicitly state when to use this tool vs alternatives like backtest_strategy or compare_strategies. No when-not or alternative 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 bears full responsibility. It mentions 'real-time' but does not clarify data freshness, rate limits, or whether it returns only the price or a full quote object. The behavioral disclosure is minimal.

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: one sentence for purpose, then a clear args section. No filler, every word adds 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 simple one-parameter tool with no output schema, the description is adequate but could be more complete by stating the return format (e.g., current price or full quote object). It tells the agent what it does but not what to expect as output.

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?

With 0% schema description coverage, the description compensates well by explaining that the 'symbol' parameter accepts Yahoo Finance symbols, and provides clear examples (e.g., AAPL, BTC-USD). This adds essential meaning beyond the bare schema.

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 it provides a 'real-time price quote from Yahoo Finance' for stocks, crypto, ETFs, or indices. The verb 'quote' and resource 'price' are specific, and the list of asset types distinguishes it from sibling analysis 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 when needing a real-time price quote but does not explicitly state when to use or avoid it, nor does it reference alternatives. Siblings are quite different, but no guidance is given.

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