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 lacks details on side effects, authentication needs, or return behavior. The description merely parameterizes the analysis without disclosing any 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 uses a structured docstring format with parameter list, which aids readability. However, it is slightly verbose for the content delivered, and the 'Args' block could be integrated into a more concise narrative.

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 five parameters and no output schema, the description should clarify return format and tool behavior. It does not explain what 'analysis' produces (e.g., list of patterns, scores). The minimal parameter descriptions leave gaps in understanding full functionality.

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 must add meaning. It provides brief explanations for each parameter (e.g., min_size_increase is a percentage), but these are minimal and do not clarify how parameters affect pattern detection. For instance, what constitutes a pattern_length of 2 vs 4?

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', specifying the task and resource. However, it could be more precise about what patterns are detected, keeping it from a perfect score.

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

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

No guidance on when to use this tool versus siblings like 'consecutive_candles_scan' or 'bollinger_scan'. The description only lists parameters without context on 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 carries the full burden. It discloses parameter details including defaults and a specific constraint for certain strategies, but does not mention the return format, error behavior, or whether the operation is read-only. The phrase 'institutional-grade metrics' is vague and lacks specifics, reducing 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 well-structured with an Args section and bullet-like parameter list. It is concise but includes necessary details. Minor reduction for not being even more succinct, e.g., removing redundant 'Args:' header.

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 9 parameters and no output schema, the description covers all inputs adequately but fails to explain the output (return values, metrics). For a complex tool, this is a significant gap. The inclusion of parameter defaults and strategy-specific notes helps, but missing output details 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?

The description adds substantial meaning beyond the input schema, which has 0% coverage. It provides example values for symbol (e.g., AAPL, BTC-USD), lists all strategy options with enum-like clarity, and explains defaults for numeric parameters. 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's purpose: backtesting a trading strategy on historical data with institutional-grade metrics. It provides a verb (Backtest) and specific resource (trading strategy + historical data). While it distinguishes from sibling tools like compare_strategies by focusing on single strategy backtesting, it does not explicitly differentiate from walk_forward_backtest_strategy, 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 lacks explicit guidance on when to use this tool versus alternatives. It only provides a constraint note for rsi_pullback and triple_ema strategies needing period >= '1y'. No mention of when not to use it, prerequisites, or comparison with sibling tools like compare_strategies or walk_forward_backtest_strategy.

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?

Clearly describes return fields and assessment labels. Implies read-only nature. No annotations provided, but description adequately covers 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?

Concise three-paragraph structure: purpose, usage, return fields. No superfluous 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 zero parameters and no output schema, description fully explains what is returned and how to use it for crypto analysis.

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?

No parameters, so description need not add value. Baseline 4 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?

Clearly states it provides macro BTC context including price, dominance, total market cap, and risk assessment. Distinguishes from siblings like yahoo_price by offering single-call context.

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?

Explicitly says to use whenever analyzing any cryptocurrency for broader market frame, and contrasts with chaining multiple calls. Provides concrete scenario with BTC vs alts.

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 present, so description carries full burden. It explains the tool scans an exchange, gives canonical name, and details parameters. However, it does not explicitly mention whether the tool is read-only or any rate limits, but given the context, it is adequately transparent.

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 well-structured with a main paragraph, example, and an args list. It is not overly verbose, though could be slightly more concise. However, it front-loads the key 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?

Given that an output schema exists (context signal indicates has_output_schema: true), the description does not need to explain return values. It covers parameters, usage guidelines, and sibling differentiation comprehensively.

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 provides extensive parameter details: lists exchange values, timeframe options with typical squeeze thresholds, default for bbw_threshold, and limit max. 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 the tool scans for assets with low Bollinger Band Width (squeeze detection) and works with crypto and stocks. It distinguishes itself from 'coin_analysis' by specifying that this tool scans a whole exchange, not a single symbol.

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?

Explicitly says when to use (scan whole exchange for squeezes) and when not (for single symbol analysis, call coin_analysis). Provides an example with parameters and default values.

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 does not disclose behavioral traits such as whether the tool is read-only, requires authentication, has rate limits, or any side effects. It only states what it returns, lacking transparency about system interactions.

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 well-structured with sections (Args, Returns) and an example. It is longer than necessary but each sentence adds value. Minor redundancy: "Detailed analysis with all indicators and metrics" could be more specific.

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 3 parameters, no output schema, and a large sibling set, the description covers all essential aspects: purpose, parameters, usage guidelines, and error behavior. Missing details on return structure or pagination, but adequate 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?

Schema coverage is 0%, yet the description adds comprehensive meaning to all parameters: explains symbol format (crypto vs stock examples), exchange list with error handling hint, timeframe options. This greatly enriches 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 detailed analysis for a specific asset on an exchange and timeframe. It distinguishes itself from siblings like multi_timeframe_analysis and combined_analysis, making the purpose unmistakable.

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?

Explicitly tells when to use this tool versus alternatives: use multi_timeframe_analysis for trend alignment across timeframes, combined_analysis for news sentiment. Also provides an example call and detailed parameter 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 provided, so description must disclose behavior. It describes the tool as a composite of multiple data sources, implying a potentially heavy call, but does not specify read-only nature, rate limits, or side effects. Adequate but incomplete for full 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?

Well-structured with bold title, usage guidance, alternatives, example, and parameter list. The 'POWER TOOL' prefix is slightly excessive but harmless. Overall 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 no output schema or annotations, the description covers purpose, usage, and parameters well. It lacks output details but is sufficient for tool selection among siblings. A minor gap in return format specification.

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 has 0% description coverage, but description fully compensates by explaining each parameter: symbol format, allowed exchanges, timeframe values, and provides a concrete example. This adds essential 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?

Description clearly states it combines technical analysis, news sentiment, and financial news. It distinguishes from siblings: coin_analysis for indicators only and multi_timeframe_analysis for trend alignment, making the purpose specific and unambiguous.

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

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

Explicitly states when to use (when TA + sentiment + news desired) and when not (indicators only → coin_analysis, trend alignment → multi_timeframe_analysis), providing clear usage guidance and 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, the description discloses an important behavioral trait: strategies with SMA200 warmup may contribute zero trades if period < 1y. This is valuable beyond the schema.

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

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

Two paragraphs are efficient: first line states purpose, second block format lists arguments. Every sentence adds value, though the structure could be slightly more formal.

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 explains input constraints well but lacks details about the output (e.g., leaderboard fields). Given 4 parameters and no output schema, it is moderately complete but could provide more return value context.

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?

Although schema coverage is 0%, the description adds meaning by listing valid period options, recommending >=1y for certain strategies, and explaining defaults for interval and initial_capital.

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 runs all 9 specified strategies and returns a ranked leaderboard. This distinguishes it from single-strategy backtest tools like 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 it is for quick comparison of multiple strategies but does not explicitly state when to use it over alternatives. No when-not or exclusions are 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 provided, so the description carries the full burden. It describes parameters but doesn't mention side effects, rate limits, or data freshness. For a read-only scan, this is adequate but could be improved.

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?

One-line purpose followed by a clean bullet list of parameters. Every sentence adds value, no fluff. Front-loaded and efficient.

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, but the return behavior (list of matching coins) is implied. The description could specify ordering or result details, but for a simple scanner it is reasonably 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 description coverage is 0%, but the description lists all 6 parameters with concise explanations (e.g., 'pattern_type: bullish (growing candles) or bearish (shrinking candles)'), adding 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 'Scan for coins with consecutive growing/shrinking candles pattern,' providing a specific verb (scan), resource (coins), and pattern type. This differentiates it from sibling tools like 'advanced_candle_pattern' or '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?

The description gives parameter details but does not provide guidance on when to use this tool vs. alternatives. With many sibling scanners, explicit context for selection is lacking.

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 present, so the description must carry full behavioral disclosure. It only says 'analysis' without indicating whether it is read-only, what output to expect, or any side effects. This is insufficient for understanding the tool's 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 and front-loads the core purpose. However, the 'Args:' section is somewhat mechanical and could be integrated more naturally. Overall, it is efficient 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?

The description adequately explains the three input parameters but does not describe the output or the nature of the retracement analysis (e.g., levels, percentages). Given no output schema, more detail on return values 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 provides 0% description coverage. The description compensates by listing allowed values and defaults for 'lookback' and 'timeframe' (e.g., '1M', '3M', '52W', 'ALL' and '5m', '15m', '1h', etc.). The 'symbol' parameter is described simply as 'EGX stock symbol,' which is basic but adds some context.

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 'Fibonacci retracement analysis for EGX stocks,' specifying the analysis type and market. It distinguishes well from sibling tools, none of which duplicate this function.

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 technical analysis of EGX stocks but provides no explicit guidance on when to use it versus other analysis tools or alternatives. No exclusions or prerequisites are mentioned.

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

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

No annotations exist, and the description fails to disclose behavioral traits such as read-only nature, data freshness, error conditions, or performance implications for large 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 a short sentence plus an Args section. It is front-loaded and efficiently structured, though the Args section adds minimal formatting.

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?

With 3 parameters, no output schema, and no annotations, the description omits return format, data freshness, and error handling. It is adequate for basic use but incomplete for nuanced scenarios.

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 lists valid values for index and timeframe, and explains limit meaning (number of stocks, max 100). This adds beyond the schema's defaults but does not clarify 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 states the action 'analyse' and resource 'EGX index', specifying output as 'constituent performance with full indicators'. This clearly differentiates from sibling EGX tools like egx_market_overview or egx_sector_scan, though 'full indicators' is 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 explicit guidance on when to use this tool versus alternatives. Among many EGX-related siblings, no comparator or exclusion criteria are 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 must fully convey behavioral traits. It states 'comprehensive overview' but omits that the operation is read-only, does not describe the response structure, and lacks any mention of rate limits, permissions, or potential 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 extremely concise: one sentence for purpose followed by a brief parameter list. No fluff or redundant information. The structure is clean and easy to parse.

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 defines the two parameters but lacks any mention of the output format or the specific data returned by the 'comprehensive overview'. Without an output schema, the agent cannot infer what to expect from the tool's response. Additional context about scope (e.g., does it cover all EGX listed stocks?) 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 significant meaning beyond the schema by listing allowed values for 'timeframe' (5m, 15m, 1h, 4h, 1D, 1W, 1M), specifying defaults (1D for stocks), and noting the maximum for 'limit' (20). This compensates well for the schema's 0% description coverage, though explicit enum formatting would be clearer.

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 identifies the target resource ('Egyptian Exchange (EGX) market') and the action ('Get a comprehensive overview'). However, it does not specify what facets of the market are included (e.g., indices, sectors, top stocks), which would help differentiate it from siblings like 'egx_index_analysis' or 'egx_sector_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 guidance is provided on when to use this tool over alternatives. With many sibling tools focused on EGX (egx_index_analysis, egx_sector_scan, egx_stock_screener) and general market overviews (futures_market_overview), users receive no context about the appropriate selection 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?

With no annotations, the description must disclose behavioral traits. It mentions that results are limited to 50 per sector and that leaving sector empty shows all sectors, but it lacks details on data source, freshness, error handling, or side effects, leaving significant gaps.

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

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

The description is concise and well-structured: a single-sentence purpose followed by a clear 'Args:' section with each parameter explained. No redundant or extraneous 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 absence of annotations and output schema, the description covers parameter details and basic purpose. However, it lacks information about the output format, sorting, or how results relate to the sibling 'egx_sector_scanner', leaving some contextual 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?

All three parameters are explained in the description with examples and constraints (e.g., sector names, timeframe values, limit max). Since the schema had no descriptions (0% coverage), the description effectively compensates by adding 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 clearly states that the tool scans EGX stocks by sector and lists sectors if none specified. However, it does not differentiate from the similarly named sibling 'egx_sector_scanner', leaving the agent unsure which to use.

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 only hints at usage by noting that leaving sector empty lists sectors, but it provides no explicit guidance on when to use this tool vs alternatives, nor any when-not-to-use advice.

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 the tool 'identifies' data without mentioning read-only nature, permissions, or side effects. Minimal disclosure.

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 opening sentence and an organized list of parameters. 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?

No output schema exists, yet the description fails to describe the return format (e.g., whether output lists sectors with scores). For a scanning tool, this gap 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?

The description adds meaningful details for each parameter, including acceptable ranges and defaults (e.g., timeframe options like 5m, 15m). This goes beyond the schema's titles and defaults, providing clear value.

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 sector rotation scanner for EGX that identifies hot/cold sectors and top picks, providing a specific verb and resource. However, it does not differentiate from sibling tools like 'egx_sector_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 guidance is provided on when to use this tool versus alternatives such as 'egx_sector_scan' or 'egx_stock_screener'. The description lacks contextual cues for selection.

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, and the description does not disclose behavioral traits beyond basic function. It does not mention read-only nature, rate limits, data freshness, or any side effects. For a production tool, 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 extremely concise: one purpose sentence and a bulleted parameter list. It is front-loaded and contains no wasted words, making it easy to scan.

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 parameters but lacks output description (e.g., what the results look like) and usage context among many similar sibling tools. Given no output schema, more detail on return values 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?

With 0% schema description coverage, the description fully explains all 4 parameters: timeframe options, min_score range, index_filter values, and limit max. This adds substantial meaning beyond the schema, though could include data types or 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 is a stock ranking engine for EGX that finds strong stocks with actionable setups. This distinguishes it from similar sibling tools like 'stock_screener' by specifying the EGX market and using the term 'ranking engine'.

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 EGX stock screening but does not explicitly state when to use this tool versus alternatives like 'stock_screener' or 'egx_index_analysis'. 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?

With no annotations, the description bears full burden for behavioral disclosure. It fails to explain what a 'full trade plan' entails (e.g., entry, exit, risk parameters), whether it requires internet access, or any side effects. Minimal behavioral detail is provided.

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 the main purpose in one line, followed by a clean list of arguments. No unnecessary words, and the structure is easy to scan.

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, but lacks information about the output (what a trade plan contains) and any usage context (e.g., prerequisites, limitations). Given the absence of an output schema, the description should ideally describe the 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?

Schema coverage is 0%, so the description compensates well by explicitly listing valid timeframe values (5m, 15m, 1h, etc.) and providing example symbols for the symbol parameter. 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 the tool generates a full trade plan for a specific EGX stock, using a specific verb and resource. It distinguishes from sibling tools like egx_index_analysis or egx_market_overview, which cover broader scopes.

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 scenarios where it is appropriate or inappropriate, nor does it reference sibling tools for 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. It mentions 'Real-time' and 'licensed' but does not disclose behavioral traits like read-only nature, rate limits, pagination, or data freshness. The description lacks detail on what happens if no news is found or if the source is unavailable.

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 and front-loaded with the purpose. The Args section is clearly formatted inline. While concise, it could be slightly more structured (e.g., bullet points), but it serves its purpose without unnecessary text.

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

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

With 3 parameters and no output schema, the description covers the parameters adequately but lacks details on return format (e.g., what fields each news item contains), pagination, or error handling. Given the tool's simplicity, it is minimally viable 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?

Schema description coverage is 0%, so the description must provide meaning. It explains each parameter: symbol ('Optional symbol filter... None = all news'), category ('News category...'), limit ('Max number of news items'). This adds significant context beyond the schema's type and default values.

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 provides 'Real-time financial news via Marketaux (licensed).' It specifies the resource (financial news), verb (implied get), and source. Among many sibling tools focused on technical analysis or market data, this is the only news tool, making its purpose distinct.

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 explains the parameters and their defaults, providing context for usage (e.g., filter by symbol, category, limit). However, it gives no explicit guidance on when to use this tool versus alternatives or when not to use it. The context of sibling tools being mainly technical analysis implies but does not specify exclusions.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool returns OHLCV quotes, implying a read-only operation, but does not discuss potential side effects, authentication needs, or rate limits. The description is adequate but lacks depth.

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 well-structured with a clear first sentence defining the purpose, followed by Args and Returns sections. It is concise but could be slightly tighter (e.g., the example symbols list, while helpful, adds length). Overall, it earns its brevity.

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 (one parameter, returns OHLCV quotes), the description covers purpose, parameters, and return type without needing an output schema. It lacks details on pagination or limits, but for a snapshot tool this is acceptable.

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 compensate. It enumerates valid categories (equity_index, energy, etc.) and provides example symbols, adding significant meaning beyond the schema's minimal 'string' type. However, it does not specify the default value or format expectations beyond 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 that the tool quotes all major front-month contracts in a specific futures category, with the verb 'quote' and resource 'futures category snapshot'. It provides a clear list of categories and example symbols. However, it does not explicitly differentiate from sibling tools like 'futures_market_overview' or 'futures_watchlist', which could have similar purposes.

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 OHLCV quotes for a category of futures, but it lacks explicit guidance on when to use this tool versus alternatives. No exclusions or preferred contexts are mentioned, leaving the agent to infer usage from the tool's 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?

No annotations provided, so description carries full burden. It discloses return format (Dict with total_available and list of contracts with OHLCV + % change) but does not mention whether the operation is read-only, rate limits, or side effects. Adequate but not thorough.

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?

Reasonably concise, with clear Args/Returns structure. The parameter enumeration is a bit lengthy but appropriate for clarity. No superfluous sentences.

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 adequately covers return structure and parameter details. However, it does not address how this tool fits among many siblings, slightly reducing 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%, yet the description fully explains all four parameters: category with enumerated values, exchanges with regional mapping, limit with default, volume_min with meaning. This provides rich semantics 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?

Description states 'Top futures contracts sorted by trading volume', clearly specifying the verb (list/sort) and resource (futures contracts). It distinguishes from siblings like futures_category_snapshot by focusing on volume-based ranking.

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 such as futures_top_movers or futures_watchlist. The description only lists parameters, leaving the agent to infer 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 provided, so description carries full burden. It explains it is a read operation returning a list of futures with OHLCV data, and details parameter defaults like volume_min filtering illiquid contracts. No contradictions, but could mention any auth requirements.

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?

Very concise: one-line summary, Args list with each parameter on one line, and Returns line. Front-loaded with purpose, 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 annotations or output schema, the description explains return type (list with % change and OHLCV) and all parameters with defaults. Missing details like specific return fields or today's data scope, but sufficient for agent 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?

Schema has 0% description coverage, but the description provides clear explanations for all parameters: direction (gainers/losers), exchanges (us/global), limit (max results), volume_min (minimum volume filter with default 10). Adds meaning beyond 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 returns 'Futures contracts with the biggest percentage moves today,' specifying the verb (get) and resource (futures top movers). It implicitly distinguishes from stock-focused siblings like top_gainers and top_losers by focusing on futures.

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 (e.g., futures_category_snapshot or futures_market_overview). The description implies usage for top movers but lacks 'when not to use' or explicit comparisons.

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

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

No annotations provided; description lacks details on behavioral traits (e.g., data freshness or mutability) but adequately describes the output as a list.

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 main purpose, 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 zero parameters and no output schema, the description sufficiently conveys what the tool returns and how to use it, though could mention if the list is exhaustive.

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?

No parameters exist; description adds value by listing categories and usage hints 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?

Explicitly states it returns a categorized list of front-month futures symbols, lists categories, and distinguishes from siblings by suggesting use with futures_category_snapshot or coin_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?

Provides clear context by recommending use with specific sibling tools for deeper analysis, though no explicit when-not-to-use 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?

With no annotations, the description must disclose behavioral traits. It mentions 'licensed Marketaux entity sentiment' but does not cover data freshness, rate limits, error behavior, or whether it is read-only. This leaves significant gaps.

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

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

The description is short with a clear one-line summary followed by parameter explanations in a structured Args block. Every sentence adds value, though it could be 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 no output schema, the description should explain return values and any limitations. It does not describe the sentiment format (e.g., score range) or how analysis is performed. This is incomplete for an intelligent agent.

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

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

Schema description coverage is 0%, so the description adds basic meaning for each parameter: symbol examples, category options, and limit as max articles. This is helpful but minimal; no format constraints or validation rules are provided.

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 provides 'news sentiment for stocks and crypto', which is a specific verb and resource. It is distinct from siblings like 'financial_news' which likely provides raw news, but it does not explicitly differentiate itself.

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. It does not describe prerequisites, use cases, or exclusions, leaving the agent to infer usage from 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 provided, so description carries full burden. It mentions 'Powered by Yahoo Finance' indicating data source. However, it does not disclose refresh rate, data freshness, or any limitations. Scope is defined but behavioral traits are 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?

Two sentences: one functional description and one source attribution. No wasted words, efficient and clear.

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 zero parameters and no output schema, the description adequately explains what the tool returns (major indices, crypto, FX, ETFs). It is sufficient for an agent to understand the tool's product, though more details on specific items would enhance 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?

No parameters exist, and schema coverage is 100% (trivially). Baseline 4 is appropriate since description adds no extra param info 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?

Description clearly states it provides a global market overview covering major indices, top crypto, FX rates, and key ETFs. It is specific about the resource but could be more action-oriented (e.g., 'Retrieve'). Distinguishes from siblings like bitcoin_market_pulse or futures_category_snapshot by being 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?

No guidance on when to use this tool versus alternatives. Among many sibling market tools, the description lacks context for selection.

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?

Describes the outcome (structured debate, final trading decision) and names the three agent types. No annotations present, so description carries full burden. Missing potential costs (e.g., rate limits, latency) but sufficiently transparent for selection.

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?

Four lines total, front-loaded with purpose, then parameter descriptions with examples, then return value. 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 and param schema lacking descriptions, the tool description explains the return (structured debate, final decision) and parameter examples. Lacks exact return field details, but sufficient for an AI to decide to use this 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?

Adds meaning beyond schema types with concrete examples (e.g., 'BTCUSDT', 'KUCOIN', '15m'). Schema coverage is 0%, so description compensates fully, giving practical values 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?

Clearly states the tool runs a multi-agent debate (Technical, Sentiment, Risk) for a symbol. This differentiates it from siblings like 'coin_analysis' or 'market_sentiment' which are single-agent or simpler.

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?

Implied usage through examples (symbol, exchange, timeframe) but no explicit when-to-use vs alternatives. Does not mention prerequisites or exclusions.

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

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

With no annotations, the description carries full burden and adequately discloses behavioral traits: it works on multiple timeframes (Weekly to 15m), requires a symbol and exchange, and includes symbol formatting rules. While it does not mention side effects or rate limits, for a non-destructive analysis tool the transparency is high.

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 well-structured: a concise first sentence defining purpose, followed by usage guidance, an example, and detailed parameter explanations. Every sentence adds unique value, and the formatting with bullet points for examples enhances readability.

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 2 parameters, no output schema, and no annotations, the description is quite complete: it covers symbol formatting for multiple exchanges, provides a working example, and clarifies scope. It does not describe the output, but that is reasonable given no output schema; however, a brief note on return format would elevate 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 only titles and default value, providing no descriptions. The description extensively compensates with detailed explanations for both parameters: symbol (bare ticker, no exchange prefix, with examples across crypto and stocks) and exchange (lists valid values). This far exceeds the schema's minimal information.

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 multi-timeframe alignment analysis for cross-timeframe trend alignment on one symbol. It distinguishes itself from sibling tools like coin_analysis (single-timeframe) and combined_analysis (TA+sentiment+news), making the purpose specific and unique.

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?

Explicitly states when to use (cross-timeframe trend alignment on ONE symbol) and when not to (use coin_analysis for single-timeframe, combined_analysis for TA+sentiment+news). Provides a clear example and alternative tool names, giving strong 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?

With no annotations provided, the description adequately discloses the return type ('list[dict]' or an error envelope) and the filtering behavior. However, it does not mention any side effects, rate limits, permissions, or whether the operation is read-only, which are important for safe invocation.

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 purpose and uses a clear structured format (Args section) for parameters. It is reasonably concise, though the parameter list could be slightly more compact without losing clarity.

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 output schema existence and the tool's straightforward nature, the description covers the essential aspects: purpose, parameters, return type, and error handling. It could improve by specifying that it targets cryptocurrency markets, but the exchange examples imply this context.

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 description coverage, the Args section thoroughly explains all four parameters: exchange (with example values), timeframe (list of options), rating (with rating scale), and limit (max 50). It adds substantial meaning beyond the schema's defaults and 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 'Filter coins by Bollinger Band rating,' which is a specific verb-resource pair. It clearly differentiates from siblings like 'bollinger_scan' by focusing on rating-based filtering rather than pattern scanning.

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 such as 'bollinger_scan' or 'coin_analysis.' It lacks explicit context about prerequisites or ideal use cases, leaving the agent to infer usage from parameter details 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?

No annotations are provided, so the description carries full burden. It discloses return type (list[dict]) and error envelope on total failure, and mentions inheritance from inner call. However, it does not state whether the tool is read-only or destructive, nor any rate limits or auth requirements.

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 structured as a docstring with clear Args and Returns sections, making it easy to parse. The first sentence is concise, but the full docstring is somewhat verbose. It could be more compact without losing clarity.

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 5 optional parameters and an output schema (not shown), the description covers parameters and the return value structure, including error cases. It does not explain the contents of the returned list[dict] (likely covered by output schema), but missing usage context relative to sibling scanners.

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 compensate. It provides clear explanations for all 5 parameters, including types, defaults, and allowed values (e.g., rsi_range options 'oversold', 'overbought', 'neutral', 'any'). It also adds constraints like limit max 30, which is not in 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 clearly states it's a combination scanner of volume and technical analysis (RSI). It specifies the verb 'scanner' and the resource, but doesn't explicitly use a verb like 'scan' and doesn't differentiate from siblings such as volume_breakout_scanner or 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 guidance on when to use this tool versus alternatives. Siblings like volume_breakout_scanner likely serve a similar purpose without RSI filtering, but the description does not help the agent choose.

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 fully bears the burden of behavioral disclosure. It explains that during the regular session, post_market will be null; on weekends/holidays, it returns the most recent data. It details the computed changes (change_vs_previous_close_pct, change_vs_regular_close_pct) and the return structure. This gives a comprehensive understanding of the tool's behavior without contradictions.

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 well-structured and front-loaded with the purpose. It uses bullet points for return values, making it easy to parse. Every sentence adds value, explaining usage scenarios, session behavior, and computed fields. It is concise given the necessary detail.

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?

With no output schema, the description thoroughly explains the return format (pre_market, regular, post_market with fields and nullable states) and contextual fields (previous_close, currency, exchange, market_state). It covers edge cases (regular session null, weekends/holidays) and the meaning of each computed percentage change. This is complete for a single-parameter 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 0% description coverage, but the parameter description in the tool description adds significant meaning: 'symbol: US stock symbol — AAPL, NVDA, TSLA, SPY, ^GSPC, etc.' This clarifies acceptable values. However, it could specify whether case matters or include additional format details, which prevents a perfect score.

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 provides 'Real-time pre-market and after-hours prices for a US stock symbol.' It specifies the verb (get real-time prices) and resource (extended hours data). The purpose is distinct from siblings like stock_prices (regular hours) and yahoo_price, as it explicitly focuses on pre-market and after-hours sessions.

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 explicitly says 'Use this when the user asks about a stock outside the regular 9:30am-4pm ET session' and gives concrete examples: earnings reactions, overnight news, 'what is X doing in after-hours?', 'how did Y open in pre-market?'. It also clarifies when not to use (regular hours). This provides clear guidance for an AI agent.

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

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

Despite no annotations, the description fully discloses the tool's behavior: it returns underlying price/change, requested expiry, available expiries, counts, and detailed options data. It also covers error handling for invalid expiry dates.

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 well-structured with a concise introductory paragraph followed by clear Args and Returns sections. 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?

The description is complete for the tool's complexity: it covers input constraints, output shape (including nested objects), and edge cases (no expiry, invalid expiry). No additional information is needed for correct 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 input schema has no descriptions (0% coverage), but the description adds rich meaning: symbol is described as a US stock symbol with examples, and expiry is explained as an optional ISO date that must match Yahoo's available expiries, including error behavior.

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 returns the full options chain (calls + puts) for a US stock symbol and one expiry. It differentiates from siblings by focusing on the complete chain for a given expiry, and even clarifies behavior when no expiry 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?

The description provides clear usage examples ('what's the options chain for X?', 'show me AAPL puts expiring next Friday', etc.) and explains the fallback behavior when no expiry is given. It implicitly contrasts with siblings like unusual activity 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 fully discloses behavioral traits: it scans soonest few expirations, filters illiquid strikes (min_volume), returns top-N sorted by V/OI descending, and provides aggregate volume. It does not mention destructive actions or rate limits, but these are not expected for a read-only screener.

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 well-organized: one-line purpose, usage examples, behavioral explanation, parameter list, and return description. Every sentence adds value, no redundancy. It is appropriately sized for the tool's complexity.

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

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

Despite no output schema, the description fully documents return fields (underlying_price, expiries_scanned, volume totals, unusual list with detailed fields). It explains filtering, sorting, and aggregation logic. The tool is complex, but the description is 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%, so the description must explain each parameter, which it does thoroughly: symbol (with examples), top_n (count, default), min_volume (purpose, default), expiries (definition, default). All parameters are clearly defined beyond what the schema provides.

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: providing top strikes by volume/open-interest ratio as an institutional positioning signal. It gives specific user query examples ("any unusual options activity on X?", "where is the smart money positioned on NVDA before earnings?") and distinguishes itself from sibling tools like stock_options_chain by focusing on V/OI screening.

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 explicitly states when to use the tool, including specific queries and contexts. It does not provide explicit when-not-to-use or direct comparisons with siblings, but the context (smart money, unusual activity) and sibling list imply when alternatives like stock_options_chain or smart_volume_scanner might be more appropriate.

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 fully explains the return structure (envelope dict with fields and not_found list) and input constraints. It implicitly indicates a read-only operation but does not mention rate limits or caching 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 concise with a one-sentence summary followed by structured Args and Returns sections. Every sentence adds value without redundancy, and the key 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 simple single-parameter input and absence of an output schema, the description covers all essential aspects: input format, constraints, return fields, and error handling via 'not_found' list. No critical gaps remain.

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 single parameter 'tickers' has 0% schema description coverage, but the tool description provides extensive semantics: comma-separated exchange-prefixed symbols, maximum 2000 items, and an example. This fully compensates for the missing schema documentation.

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

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

The description clearly states the tool provides 'Current price + daily % change for specific stock symbols', with specific input format and output structure. It distinguishes itself from siblings by focusing on real-time price data rather than analysis or scanning.

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 specifies the required ticker format (exchange:SYMBOL), maximum batch size (2000), and example usage. However, it does not explicitly state when to use this tool versus alternatives like 'yahoo_price' or 'stock_extended_hours'.

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 fully discloses important behaviors: single upstream request, max 2000 rows, ranking by market cap, OTC handling, compact mode, return envelope details, and local currency pricing.

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 well-structured with Args and Returns sections and front-loaded with purpose, though it could be slightly more concise.

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 is highly complete, covering return format, country list, parameter defaults, edge cases (OTC, currency), and ranking order, despite no output schema.

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

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

Despite 0% schema coverage, the description adds rich meaning to all 5 parameters, including country market names, stock_type options, limit constraints, exclude_otc logic, and compact output details.

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 screens stocks by share type and references TradingView's filter, but does not explicitly differentiate from sibling tools like egx_stock_screener.

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 stock screening but provides no explicit guidance on when to use this tool versus the many sibling tools, nor any exclusions.

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

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

No annotations provided, so the description must disclose behavior. It states the tool returns data based on Bollinger Band analysis and describes the return format (list of dicts on success, error envelope on failure). However, it does not explicitly state it is a read-only operation or mention any side effects, leaving a small 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 concise (about 100 words) and well-structured with 'Args' and 'Returns' sections. 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?

Given the output schema exists, the description adequately covers the tool's purpose, parameters, and return format. It lists all parameter options, making it easy for an agent to invoke. Minor omission: no details about sorting or whether results are top N by gain.

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 has 0% description coverage, but the description compensates fully by listing all valid exchange names and timeframes, and stating the maximum limit (50). This adds critical 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 returns top gainers using Bollinger Band analysis for a given exchange and timeframe. It distinguishes from siblings like top_losers and bollinger_scan by specifying the exact use case.

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 (e.g., top_losers, bollinger_scan). The description lists allowed exchanges and timeframes, which aids selection, but lacks 'when not to use' or comparison to similar 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, the description carries full burden. It discloses return format (list[dict] on success, error envelope on failure) and mentions 'total upstream failure'. It lacks details on side effects, rate limits, or authentication, but the read-only nature is implied.

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 sentences, no fluff, front-loaded with the core purpose. Every sentence adds value, and the error handling note is relevant.

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 an output schema exists (context indicates), the description need not detail return fields. It covers supported exchanges, return type, and error behavior. Minor gap: no mention of default parameter values, but those are in the schema.

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

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

Schema has 0% description coverage, so the description must compensate. It adds meaning by listing possible exchange values (KUCOIN, BINANCE, MEXC, EGX, BIST, NASDAQ) and referencing timeframe, but does not enumerate timeframe options or explain the 'limit' parameter. Partial value added.

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 specific supported exchanges (crypto and stock). It distinctly differentiates from sibling tools like 'top_gainers' and market overview 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?

It explicitly mentions supported exchanges and the purpose (top losers), implying when to use it. However, it does not explicitly state when not to use it or mention alternatives, though the sibling list provides 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?

Without annotations, the description fully discloses return formats, error handling, and the meaning of an empty list. It addresses potential edge cases (rate-limit cliffs surface explicitly), providing good transparency beyond basic parameter definitions.

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 well-structured with separate sections for arguments and return behavior. It is concise but includes all necessary details 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?

Given the presence of an output schema (not shown), the description adequately covers return types and error cases. However, it could benefit from a usage example or explicit notes on prerequisites like API keys, though not critical for functionality.

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, but the description compensates fully by explaining each parameter's purpose, acceptable values (e.g., exchange list, timeframe options), and defaults. This adds substantial meaning beyond the schema's type and default fields.

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 detects coins with volume breakout and price breakout. It specifies key parameters like exchange, timeframe, and thresholds, but does not directly contrast with sibling scanners such as smart_volume_scanner or bollinger_scan, which may have overlapping capabilities.

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

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

The description explains return behavior (empty list means no matches, error envelope for upstream failures) but lacks explicit guidance on when to use this tool versus alternatives. No when-not-to-use advice 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 must disclose behaviors. It only says 'analysis' without indicating read-only status, data requirements, side effects, or any limitations. For a tool performing analysis, this is insufficient.

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

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

The description is brief, but it lacks structure beyond a one-line purpose and an argument list. It could be more organized, but it is not verbose. The conciseness comes at the cost of completeness.

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 or annotations, the description should cover return values, interpretation, and constraints. It does not explain what 'volume confirmation analysis' entails or what the output looks like, leaving the agent without crucial context.

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's Args block merely restates parameter names with examples, adding no meaning beyond the schema. With 0% schema description coverage, it fails to explain allowed values, formats, or constraints (e.g., symbol format, valid timeframes).

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', which clearly identifies the verb (analysis) and resource (coin). However, it does not differentiate from sibling tools like 'volume_breakout_scanner' or 'smart_volume_scanner', which may also involve volume 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. It does not mention prerequisites, typical use cases, or when not to use it. The description is purely functional.

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 fully disclose behavior. It explains the walk-forward validation concept and parameters, but does not mention whether the tool modifies any data, has state, or what the output format is. The agent knows it's a computation but lacks details on side effects or return structure.

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

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

The description is concise and well-structured: a one-sentence summary followed by a clear bulleted list of arguments. Every sentence serves a purpose, and there is no superfluous text. The format aids quick scanning by an AI agent.

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 (9 parameters, no annotations, no output schema), the description covers inputs well but omits any description of the output. An agent selecting this tool needs to know what it returns (e.g., metrics, fold results) to decide if it suits the task. This gap 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?

The description provides thorough explanations for all 9 parameters, including allowed values (e.g., strategy enum, period formats), defaults, and recommendations. Since the input schema has 0% description coverage, the description fully compensates by adding essential meaning beyond property titles.

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 core function: 'Walk-forward backtest to detect overfitting — validates strategy on unseen data.' This is a specific verb-resource pair. However, it does not explicitly differentiate from sibling backtest tools like backtest_strategy, leaving the agent to infer when to choose this over alternatives.

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

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

The description explicitly lists two strategies (rsi_pullback, triple_ema) that are NOT supported in this tool and directs users to 'use run_backtest with period='2y'' instead. This provides clear when-not-to-use guidance. However, it lacks a general statement about when to prefer walk-forward over a standard backtest.

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 burden. It mentions 'real-time' but does not elaborate on frequency, restrictions, rate limits, or any other behavioral 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?

The description is concise and front-loaded with the main purpose. Every sentence is meaningful, with no redundancy.

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

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

For a simple one-parameter tool with no output schema, the description covers the core purpose and parameter examples. It lacks mention of output format or error handling, but is reasonably 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% (no description in schema). The description compensates well by providing concrete examples for different asset types (e.g., AAPL, BTC-USD, ^GSPC, THYAO.IS), clarifying the symbol format.

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 provides a real-time price quote from Yahoo Finance for stocks, crypto, ETFs, or indices. The verb is implicit but the resource and action are specific.

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 like stock_prices or coin_analysis. The description lacks context for selection among many sibling tools.

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