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

With no annotations, the description carries the full burden. It describes the analysis behavior and return value, implying a non-destructive scan. However, it does not explicitly state read-only semantics, data freshness, rate limits, or error behavior. It provides basic transparency but leaves out significant behavioral context.

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

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

The description is well-structured with separate Args and Returns sections. It is concise, front-loaded with the main purpose, and each sentence contributes meaningful information. No unnecessary verbosity or redundancy.

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

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

The description is reasonably complete for a basic scan tool, but there are notable gaps. It lacks return format details (only says 'Coins with progressive candle size increase patterns'), no guidance on result interpretation, and no differentiation from similar siblings. The parameter explanations are good, but the overall operational context is incomplete for an agent.

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

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

The schema has 0% description coverage, so the description's Args section is crucial. It explains all five parameters concisely (e.g., 'pattern_length: Number of consecutive periods to analyze (2-4)') with meaningful details like allowed ranges. This adds value beyond the raw schema, even if some like min_size_increase could be more precise about percentages.

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' and specifies the output: 'Coins with progressive candle size increase patterns'. This identifies a specific functionality, but it does not explicitly differentiate from sibling tools like consecutive_candles_scan or volume_breakout_scanner.

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 any use cases, exclusions, or alternatives such as consecutive_candles_scan. The only hint is the tool name and parameter names, which is insufficient for an agent to make a selection decision.

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 of behavioral disclosure. It discloses the return metrics and optional outputs (trade log, equity curve), but it does not describe important behavioral traits such as data source reliability, potential look-ahead bias, execution assumptions, or the fact that no data is modified. It implies read-only behavior but does not state it explicitly.

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 a Returns section, using bullet points and bolded parameter names. It is longer than necessary but every line adds value, especially the strategy definitions. The purpose is front-loaded. It could be slightly more concise, but the structure aids 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 9 parameters, no output schema, and no annotations, the description is quite complete. It covers all parameters, provides examples, and lists the returned metrics. However, it does not specify the exact structure or format of the trade log and equity curve, nor does it mention any limitations (e.g., data availability for certain symbols). This is a minor gap but not critical for a backtest 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 no descriptions (0% coverage), so the description fully compensates by providing thorough explanations for all 9 parameters, including valid values (e.g., symbol examples like 'AAPL', 'BTC-USD'), strategy definitions with explanations, default values, and the effect of each parameter. This goes far 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 the tool's purpose: 'Backtest a trading strategy on historical data with institutional-grade metrics.' It specifies the verb (Backtest), the resource (trading strategy), and the output (metrics). It distinguishes from sibling tools like compare_strategies and walk_forward_backtest_strategy by focusing on a single strategy backtest.

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

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

The description provides clear context about what the tool does and lists many strategies, but it does not explicitly state when to use this tool versus alternatives like compare_strategies or walk_forward_backtest_strategy. There are no usage exclusions or prerequisites, but the use case is implied through the description. No explicit 'when not to use' or 'use this instead' guidance is present.

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

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

With no annotations provided, the description carries the full burden. It discloses the scanning behavior (low BBW filter), valid exchanges and timeframes, the meaning of bbw_threshold, and the limit constraint (max 100). It does not mention output format or pagination, but the operation is clearly a read-only scan and these gaps are partially covered by the output schema.

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

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

The description is well-structured with a brief summary followed by a labeled args list. It is slightly verbose due to listing all exchange values, but every sentence adds necessary information and nothing is extraneous.

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

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

Given the tool's moderate complexity (4 optional params, no required fields) and the presence of an output schema, the description is complete. It explains all parameters and the core scan logic, making it sufficient for correct invocation and interpretation.

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 fully compensates by explaining every parameter: exchange lists valid values, timeframe lists options, bbw_threshold states its filtering meaning, and limit defines its purpose. This is exceptionally clear and exceeds the baseline.

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 uses a specific verb and resource: 'Scan for assets with low Bollinger Band Width (squeeze detection).' It clearly distinguishes this tool from siblings like volume_breakout_scanner or market_snapshot, and explicitly mentions supported asset types (crypto and stocks).

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

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

The description provides clear context on when to use the tool: to detect Bollinger Band squeezes. It also specifies supported exchanges and timeframes, giving users concrete usage boundaries. However, it does not explicitly exclude alternatives or mention when not to use it, stopping short of a full when/when-not guideline.

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 discloses that the tool returns 'detailed analysis with all indicators and metrics,' implying a read-only, informative operation and a comprehensive result set. However, it does not mention data sources, freshness, potential failures, or any operational limitations. It is not contradictory, but it is not rich in behavioral detail.

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: a one-sentence purpose, then a clean Args list with examples, and a Returns line. Every section earns its place, with no wasted words. It is appropriately concise for a 3-parameter tool.

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 three parameters, no output schema, and no annotations, the description provides sufficient information for invocation by specifying all parameters and return type at a high level. It could be more complete by detailing what 'all indicators and metrics' includes, but the tool is still usable and understandable in its current form.

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 compensates by documenting all three parameters with explicit examples and allowed values: symbol examples for crypto and stocks, a list of supported exchanges, and timeframe intervals. It adds meaning beyond the raw schema by providing concrete usage patterns, making correct invocation much easier.

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 a specific action: 'Get detailed analysis for a specific asset (coin or stock) on specified exchange and timeframe.' It clearly identifies the resource (asset) and scope, distinguishing it from bulk scanners/overviews. However, it does not explicitly distinguish itself from similar sibling tools like combined_analysis or multi_agent_analysis, so it falls short of a perfect 5.

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?

Usage context is implied: use this tool when you need detailed analysis for a single symbol on a particular exchange/timeframe. The presence of analyzer siblings like multi_timeframe_analysis and combined_analysis makes this guidance implicit rather than explicit. There is no 'when not to use' or mention of alternatives, so the agent must infer selection criteria from the examples.

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 lists input sources and parameters but does not describe what the output looks like, whether data is live or delayed, or any rate limits or side effects. The absence of any return-value detail is a significant gap for an aggregation tool.

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

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

The description is extremely concise and front-loaded: a two-line summary followed by a tight argument list. Every sentence earns its place, and the 'POWER TOOL' label immediately signals high-level aggregation.

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?

Input parameters are well covered, but the tool has no output schema and the description does not explain what the returned analysis contains or how the confluence is presented. Given the tool's moderate complexity, this omission leaves the agent guessing about the tool's actual deliverable.

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

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

Schema description coverage is 0%, and the description compensates by defining all three parameters with examples and allowed values: symbol examples like 'AAPL' and 'BTCUSDT', exchange list, and timeframe list. It adds meaning beyond the bare schema, though it doesn't discuss defaults or formatting constraints.

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 combines TradingView technical analysis, Reddit sentiment, and financial news into a 'confluence analysis' for a 'complete market picture.' This distinguishes it from more specialized siblings like financial_news or market_sentiment, though it lacks a single explicit verb like 'retrieve' or 'generate.'

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 phrase 'Confluence analysis of all signals for a complete market picture' implies when to use the tool, but it never explicitly contrasts it with alternatives such as multi_timeframe_analysis or multi_agent_analysis. There is no when-not-to-use guidance, making the usage context clear but not fully explicit.

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 clearly explains that the tool runs strategies and returns a leaderboard, which is a read-only analysis. However, it does not explicitly state non-destructiveness, rate limits, or computation time, which would enhance transparency for agent decision-making.

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

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

The description is efficiently structured with a clear purpose statement followed by an organized Args block. While slightly longer than strictly necessary, each sentence provides useful information and no redundancy is present.

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

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

The description covers the main return value (leaderboard vs buy-and-hold benchmark) and all parameters, which is sufficient given the absence of an output schema. It lacks details on ranking criteria (e.g., return vs. risk) but overall provides adequate context for the agent to invoke the tool.

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

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

Schema description coverage is 0%, so the description fully compensates by explaining every parameter: symbol with examples, period with allowed values, initial_capital with default, and interval with default. This goes well beyond the schema's bare titles and provides actionable semantics.

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 uses a specific verb 'Run' and clearly states the resource ('all 6 strategies') and the output ('ranked performance leaderboard'). It lists the exact strategies, which distinguishes it from sibling tools like backtest_strategy that focus on a single strategy.

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

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

The description implies usage for comparing all six strategies on the same symbol, but it does not explicitly state when to use this tool versus alternatives such as backtest_strategy or walk_forward_backtest_strategy. No exclusions or preferred scenarios are given, 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?

With no annotations, the description must disclose behavioral traits. It implies a read-only scan but does not explicitly state this, nor does it mention data source, freshness, pagination, rate limits, or error behaviors. The description is a simple docstring with args/returns, leaving significant behavioral context undisclosed.

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 with an opening purpose sentence followed by formatted Arguments and Returns sections. Every parameter has a one-line explanation with no redundant text, making it efficient 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?

Despite having no output schema, the description states that the return is a 'List of coins' and explains all input parameters. It does not elaborate on the structure of each coin in the list (e.g., growth percentages, symbols), which would improve completeness, but overall it covers the essential aspects for a scanning tool with six parameters.

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 fully compensates by providing clear meanings for all six parameters: exchange, timeframe, pattern_type, candle_count, min_growth, and limit. It even specifies valid values (e.g., 'bullish'/'bearish', 2-5) beyond the schema's default-only definitions.

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 a specific verb+resource: 'Scan for coins with consecutive growing/shrinking candles pattern.' This clearly distinguishes it from general scanners, though it does not explicitly name alternatives like 'advanced_candle_pattern' or other sibling tools. The pattern_type parameter clarifies bullish/bearish, making the purpose concrete.

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

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

No guidance is given on when to use this tool versus alternatives. It does not mention exclusions, prerequisites, or specific scenarios (e.g., 'use this when you need to identify consecutive candle streaks'). The description only states what it does, not when to choose it.

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

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It clearly explains what the tool calculates (retracement/extension levels, trend direction, price position) and what it returns. It does not mention limitations or data source, but for a read-only analysis tool, the core behavior is well covered.

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 opening sentence, a detailed Args section, and a Returns summary. Every sentence provides useful information with no fluff or redundancy. The front-loaded purpose makes it easy to understand quickly.

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

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

Given the tool's moderate complexity (3 parameters, no output schema), the description is largely complete. It explains the analysis approach, parameters, and return values in broad terms. While the return format is not deeply specified, the description gives enough context for an agent to understand what to expect.

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

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

Schema description coverage is 0%, so the description's Args section is essential. It provides meaningful explanations for all three parameters, including example values (e.g., 'COMI'), valid enumeration for lookback and timeframe, and defaults. This fully compensates for the schema's lack of detail.

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

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

The description uses a specific verb ('analysis') and identifies the resource ('EGX stocks') with clear scope: identifying support/resistance at standard Fibonacci ratios. It also lists the specific ratios and zones, making it distinct from sibling tools like egx_sector_scan 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 usage is implied: it performs Fibonacci retracement analysis, but there is no explicit guidance on when to use this tool versus alternatives like egx_stock_screener or multi_timeframe_analysis. No exclusions or alternative tools are mentioned, so it only implicitly conveys its purpose.

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 states the tool returns index-level statistics and per-stock breakdown, but does not disclose whether it is read-only, any authentication or rate-limit requirements, or potential data freshness issues. This is minimal behavioral disclosure for a tool with no annotation support.

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 one-sentence summary, a return-type line, and a clear Args list. Each sentence earns its place, and the Args section is detailed yet not bloated. The front-loading of the purpose makes it immediately understandable.

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 tool has no output schema or annotations, so the description needs to cover purpose, parameters, and return value. It does all three reasonably well. It could add examples or state that it is a read-only analysis, but the core information is present for a tool of this complexity.

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 thoroughly explains each parameter: index with enumerated values and descriptions, timeframe with allowed values and default, and limit with meaning and max value. This adds significant value 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 'Analyze an EGX index showing constituent performance with full indicators' and specifies outputs ('index-level statistics and per-stock breakdown'). It uses a specific verb and resource, but does not explicitly differentiate from sibling tools like egx_market_overview 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?

The description implies usage context (when you need EGX index analysis) but provides no explicit guidance on when to use this tool versus alternatives, nor any exclusions. Sibling tools like egx_market_overview or egx_sector_scan could overlap, and the description does not clarify 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?

The description uses 'Get' to imply a read-only operation and lists the data categories, but it does not explicitly state safety characteristics, response structure, pagination, or data freshness. Since no annotations are provided, more behavioral disclosure would be beneficial, though nothing contradicts the likely safety profile.

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, then directly transitions to an Args section with clear formatting. Every sentence earns its place, with no redundant or filler 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?

The description covers the tool's purpose and parameter semantics thoroughly, which is sufficient for a simple market-overview tool. However, without an output schema, it does not describe the return format (e.g., a list of dictionaries), and it lacks explicit usage guidance relative to sibling tools. Still, the core information is present and well-structured.

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

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

The Args section provides detailed semantic meaning for both parameters: timeframe lists all valid values and defaults ('One of 5m, 15m, 1h, 4h, 1D, 1W, 1M (default 1D for stocks)'), and limit clarifies its purpose and upper bound ('Number of stocks per category (max 20)'). This fully compensates for the 0% schema description coverage.

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

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

The description clearly states the tool's function: 'Get a comprehensive overview of the Egyptian Exchange (EGX) market' and specifies exactly what it shows (top gainers, top losers, most active). This distinguishes it from sibling tools like top_gainers/top_losers, which are narrower in scope.

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 'comprehensive overview' phrasing implies usage for a broad market snapshot, but there is no explicit guidance on when to prefer this tool over its siblings or when not to use it. No alternative tools are named, leaving the agent to infer the appropriate context.

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

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

With no annotations provided, the description carries the full transparency burden. It mentions that leaving sector empty lists available sectors, but does not disclose whether the operation is read-only, what the return format looks like, or any authentication or rate-limit considerations.

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 a clear one-sentence purpose and then organizes parameter details in an Args block. The long sector list is necessary because the schema lacks enums, but the structure remains readable 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?

There is no output schema, so the description should explain the return format, but it does not. It also lacks context on pagination, error handling, or relationship to sibling tools, making it incomplete for a scanning tool with no annotations.

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

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

Schema description coverage is 0%, and the description fully compensates by enumerating all valid sector values, valid timeframe options, and the maximum limit of 50. This adds significant meaning beyond the bare schema.

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

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

The description clearly states the tool scans EGX stocks by sector, with an explicit verb and resource. However, it does not distinguish itself from the sibling tool 'egx_sector_scanner', so it lacks sibling differentiation.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'egx_sector_scanner'. It implies usage via the name and 'Shows available sectors if none specified', but gives no explicit when/when-not or alternative recommendations.

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 discloses that it scans all 18 sectors, ranks by momentum, and returns a heatmap and rotation signals, which is useful. However, it does not mention limitations such as data freshness, interpretation of signals, or any potential side effects, leaving some transparency gaps.

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

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

The description is a compact model: a one-sentence summary followed by structured Args and Returns sections. Every sentence adds value and the format is easy to parse, making it appropriately sized 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?

The Returns section explicitly lists the output components ('Weighted market view, sector heatmap, top picks, and rotation signals'), which is helpful given there is no output schema. It does not explain how to interpret rotation signals or any prerequisites, but the tool is a scanner with clear functionality, so the description is nearly 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 fully compensates by documenting every argument with allowed values and defaults: 'timeframe: One of 5m, 15m, 1h, 4h, 1D, 1W, 1M (default 1D)', 'top_n_sectors: Number of top sectors to show stock picks for (1-18, default 5)', etc. This adds complete meaning beyond the bare schema.

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

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

The description clearly states it is a 'Sector rotation scanner for EGX' that 'identifies hot/cold sectors and top picks.' It goes on to explain it scans all 18 sectors, ranks by momentum, and surfaces best stocks, which is a specific verb+resource+scope. This distinguishes it from the sibling egx_sector_scan, which likely just scans sectors without momentum ranking or stock picks.

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

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

The description implies use when you want sector rotation analysis ('Sector rotation scanner') but does not explicitly state when to use this tool vs alternatives like egx_sector_scan or egx_market_overview. There are no exclusions or comparison guidance, so the context is implied rather than explicit.

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 details the 100-point scoring model, the 70+ threshold for trade setups, and the return structure, which is substantial. However, it does not mention potential rate limits, data freshness, or any restrictions beyond the scoring logic, so it is not a perfect 5.

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 bullet points for the scoring model, front-loading the purpose, and every sentence adds value. It is appropriately detailed for a complex tool without being bloated.

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

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

The description covers all essential aspects: purpose, scoring method, parameter semantics, and return values (ranked stocks with score breakdown, trade plans for high-scorers). Given there is no output schema, the 'Returns' section is sufficient and complete.

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

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

Schema coverage is 0%, but the description fully documents all four parameters: timeframe with valid values and default, min_score with range and default, index_filter with specific indices and empty-for-all, and limit with max and default. This completely compensates for the missing schema 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 is a 'production stock ranking engine for EGX' that 'finds strong stocks with actionable setups.' It specifies the resource (EGX stocks) and distinguishes itself from siblings like sector scans or index analysis.

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

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

The description implies use for stock screening and provides scoring details, but it does not explicitly say when to use this tool versus alternatives or mention exclusion criteria. There is no comparison to the many sibling tools, so usage guidance is only implied.

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 present, the description carries the burden of behavioral disclosure. It details the outputs thoroughly and implies a read-only analysis, but it never explicitly states that it is non-mutating or how it handles data freshness/real-time feeds. This is adequate but not fully transparent about side-effect boundaries.

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 with an intro, a Produces list, an Args section, and a Returns section. Every sentence adds value, though the Returns section partially repeats the Produces list, making it slightly less concise than ideal.

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 thoroughly covers what the tool returns (score breakdown, entry points, S/R levels, etc.). It also explains parameter values. It lacks details on error conditions or prerequisites (e.g., whether the symbol must be active), but for selection and invocation purposes it is nearly 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 compensate. It does so admirably by listing examples for symbol (COMI, TMGH, FWRY) and enumerating all allowed timeframes for the timeframe parameter. This goes beyond the schema's bare 'string' type definitions.

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 a specific action ('Generate a full trade plan') for a specific resource ('a specific EGX stock'). It enumerates the outputs (score, entry points, stop-loss, targets, etc.) which distinguishes it from siblings like egx_market_overview or egx_stock_screener that serve different 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 context is clear: use when a trade plan is needed for a specific EGX stock. It implicitly excludes use for market overviews or scanning multiple stocks. However, it does not explicitly name alternative tools or state when-not-to-use, so it falls short of a 5.

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

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

No annotations are provided, so the description carries the full burden. It mentions the data source (RSS feeds) but fails to disclose output format, potential latency, rate limits, or whether results are sorted. For a simple read-only tool, this is minimal but not sufficient.

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

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

The description is concise, front-loaded with the main purpose, and uses a clear Args list. Every sentence earns its place with no fluff.

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 tool is simple and params are well-documented, but with no output schema, the description should mention what the response contains (e.g., article titles, links, dates). Without this, the description is incomplete for an agent expecting structured output.

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

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

Schema coverage is 0%, but the description fully compensates by defining each parameter with examples: symbol (optional filter with examples), category (feed choices), and limit (max items). This adds 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 provides real-time financial news from RSS feeds, naming specific sources (Reuters, CoinDesk). This distinguishes it from sibling market analysis tools and gives a specific verb+resource.

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 clear context (use for news), but does not explicitly mention alternatives or when-not scenarios. Given the distinct purpose, this is sufficient for basic usage guidance but lacks exclusionary info.

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 for behavioral disclosure. It mentions 'real-time' but doesn't describe output format, whether it's read-only, limitations, or how sentiment is computed. The description adds no details about side effects or expected response 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 extremely concise: one sentence for purpose, then a compact args list. Every line is informative, no filler or redundant phrasing. Front-loaded purpose makes it immediately scannable.

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 clear purpose and param docs, the description is incomplete given no output schema and no annotations. It fails to explain what the tool returns (e.g., sentiment score, post list), which is critical for correct invocation. Also lacks any mention of rate limits or usage prerequisites.

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 descriptions are empty (0% coverage), so the description must compensate. It does so fully by listing each parameter with examples and meaning: symbol examples, category allowed values, and limit as 'Number of posts to analyze'. This adds significant value 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 explicitly states 'Real-time Reddit sentiment analysis for stocks and crypto' with a specific resource (Reddit posts) and scope (stocks/crypto). It clearly distinguishes itself from sibling tools like market_snapshot or financial_news by focusing on sentiment from Reddit.

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

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

The description implies use for sentiment analysis on Reddit discussions, but doesn't explicitly state when to use it versus alternatives like financial_news or market_snapshot. No exclusions or alternative recommendations are provided, so guidance is implied rather than explicit.

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 responsibility for behavioral disclosure. It mentions 'Powered by Yahoo Finance' and lists data categories, which adds some transparency about source and coverage, but it does not disclose latency, update frequency, or return 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 and well-structured, with the first sentence front-loading 'Global market overview' and enumerating covered asset classes, and the second sentence crediting Yahoo Finance. Every word adds value 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?

Given the tool's simplicity (zero parameters, no output schema, no annotations), the description adequately conveys the data scope and source. It could mention the return format, but for a broad snapshot tool with low complexity, the provided detail is largely sufficient.

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

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

The tool has zero parameters, and the input schema is empty. The baseline score of 4 applies because there are no parameter semantics to enhance; the description correctly does not attempt to explain nonexistent parameters.

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 tool's scope as a global market overview, listing specific indices, cryptos, FX rates, and ETFs. However, it lacks an explicit verb such as 'fetches' or 'lists', and does not differentiate itself from the similarly named sibling tool egx_market_overview.

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

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

The phrase 'Global market overview' implies usage for obtaining a broad market snapshot, but there is no explicit guidance on when to choose this tool over alternatives like market_sentiment or egx_market_overview. No exclusions or alternative suggestions 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 carry the burden. It does disclose the output ('A structured debate between 3 AI agents culminating in a final trading decision'), which is helpful. However, it does not state whether the tool is read-only, whether it makes external calls, or any latency/cost implications. This is typical for analysis tools but still a gap without annotations.

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 compact docstring with 'Args' and 'Returns' sections. Every line provides useful information, no fluff. It is front-loaded with the core purpose and then details parameters.

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

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

Given the tool's moderate complexity (3 arguments, no output schema), the description sufficiently explains inputs and the high-level return value. It could mention more about the structure of the debate or the final decision format, but the provided information is adequate for an agent to understand what to expect and when to use it.

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 with specific examples: symbol includes 'BTCUSDT' and 'COMI', exchange lists KUCOIN/BINANCE and stock exchanges, timeframe lists intervals like '15m'. This adds meaning beyond the raw schema and helps the agent construct valid arguments.

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 function: 'Run a multi-agent debate (Technical, Sentiment, Risk) for a specific symbol.' This is a specific verb with a distinct resource (multi-agent debate) and scope (symbol). It differentiates from sibling analysis tools by naming the three agent perspectives.

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 practical examples of accepted symbol and exchange values, implying when to use the tool (e.g., for a known symbol). However, it does not explicitly mention alternatives or when not to use it, such as saying 'use combined_analysis for a single-perspective view'.

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 burden of behavioral disclosure. It details the analysis process and return value but does not explicitly state read-only behavior, data sources, or limitations. Some transparency is present, but gaps remain.

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 compact, front-loaded, and well-organized into summary, args, and returns. Every sentence contributes value without unnecessary detail, earning a top score.

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 and absence of an output schema, the description adequately covers the input parameters, analytical scope, and return type. It does not elaborate on the alignment score calculation, but that is an implementation detail. Slightly more detail on output structure would make it 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%, so the description's Args section is crucial. It explains symbol and exchange with concrete examples for both crypto and stock markets, enriching the bare schema and enabling correct parameter usage.

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 tool as 'Multi-timeframe alignment analysis' and explains it 'Analyzes a symbol across all key timeframes to find confluence and optimal entry timing.' This specific verb and resource distinguish it from sibling tools by focusing on timeframe alignment.

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

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

The description provides clear context for when to use the tool: 'Best trades happen when Weekly, Daily, and lower timeframes all align in the same direction.' This implies the primary use case but does not explicitly name alternatives or exclusion cases, so it is slightly below a perfect score.

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 of behavioral disclosure. It mentions 'filter' but does not state whether this is a read-only operation, what the output contains, or any side effects or prerequisites. The rating scale in Args adds some context but does not cover the tool's behavioral traits.

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

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

The description is a single concise purpose sentence followed by a well-structured Args block. Every line is informative and necessary, 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 covers the input parameters well and the output schema likely covers return values. However, it lacks contextual details like whether this is a live scan, how results are ordered, or when to use it instead of sibling tools. Given no annotations, more context would be needed for full completeness.

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

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

Schema description coverage is 0%, and the description fully compensates by explaining each parameter: exchange with examples, timeframe with exact allowed values, rating with the full -3 to +3 scale and meanings, and limit with a max value. This adds significant value 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 filters coins by Bollinger Band rating, using a specific verb and resource. However, it does not explicitly differentiate itself from sibling tools like bollinger_scan, which may serve a similar purpose.

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. The description only states what it does, not when it should be preferred or how it relates to sibling scanning 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 the full burden of behavioral disclosure. It reveals that the tool uses volume ratio, price change, and RSI ranges as filter criteria, but it does not describe the output format, whether it's read-only, or any side effects or limitations. The behavior of combining volume and technical analysis is only hinted at, not detailed.

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

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

The description is concise and front-loaded with a clear purpose, followed by a structured parameter list. Each line is useful and the format is easy to scan. The parameter explanations are somewhat redundant with the schema's defaults but still add semantic value.

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

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

Despite having an output schema, the description is thin on context. It lacks insight into how the combination works, what 'smart' entails, when to choose this over other scanners, and what the actual output looks like (though the output schema may cover return format). The tool's sophistication warrants more explanation than a simple parameter list.

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

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

The schema has zero description coverage, and the description fully compensates by explaining each parameter's meaning, default values, and for rsi_range, the possible values with their thresholds. This adds significant value beyond the schema fields, which are just names 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 clearly states the tool is a 'Smart volume + technical analysis combination scanner', specifying its function as a scanner combining volume metrics with technical indicators. This is more specific than simply 'scanner', but it doesn't explicitly differentiate it from sibling scanners like volume_breakout_scanner, so it falls short of a 5.

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

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

The description provides no guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites. It only lists parameters, leaving the user to infer when it might be appropriate; this is essentially no 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 carries the burden of behavioral disclosure. It reveals that the tool uses bollinger band analysis and imposes a max limit of 50, which adds value beyond the name. However, it does not explicitly state read-only behavior or data freshness, though 'return' implies a safe read operation.

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

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

The description is concise: a single overview sentence followed by a clean, organized Args section. Every sentence serves a purpose, with no fluff, making it easy to scan and understand.

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 (3 parameters, no required fields) and the presence of an output schema, the description covers purpose, parameters, and methodology sufficiently. It lacks only niche details like exact data source or update frequency, which are not critical for correct invocation.

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

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

The schema provides no descriptions (0% coverage), but the description fully compensates by explaining each parameter in detail: exchange lists allowed crypto and stock exchanges, timeframe lists all allowed timeframes, and limit specifies max rows. This is excellent parameter documentation.

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

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

The description clearly states the tool's function: 'Return top gainers for an exchange and timeframe using bollinger band analysis.' It identifies a specific verb (Return), resource (top gainers), and method (bollinger band analysis), which also differentiates it from siblings like top_losers and bollinger_scan.

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

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

The description provides clear context about what the tool does and the parameters (exchange, timeframe, limit), but it does not explicitly name alternatives or state when not to use it. The usage context is clear enough, but exclusions or alternative scenarios are not 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?

With no annotations, the description must carry the burden of behavioral disclosure. It only mentions supported exchanges, but does not explain the meaning of 'top losers' (e.g., by percentage decline), the timeframe format, default behavior, or any other operational details. This is insufficient without annotations.

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

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

The description is a single concise sentence that front-loads the core purpose. No wasted words or redundant details.

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

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

For a simple data retrieval tool with an output schema and all-optional parameters, the description covers the essentials but leaves gaps in timeframe format and limit semantics, and lacks usage guidance. It is minimally viable but not comprehensive.

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

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

The schema has zero description coverage, so the description must compensate. It adds meaning for 'exchange' by naming allowed values, and for 'timeframe' by naming it, but it does not explain the timeframe format or the 'limit' parameter at all. This partial compensation earns a score below baseline.

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 returns top losers for an exchange and timeframe, using the specific verb 'Return' and the resource 'top losers'. It also lists supported exchanges, distinguishing it from sibling tools like top_gainers.

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 explicit guidance on when to use this tool versus alternatives. It does not mention top_gainers or other similar tools, nor does it state any exclusions or preferred 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 provided, the description carries the full burden of behavioral disclosure. It only restates the detection logic and parameter meanings, but does not disclose any side effects, limitations, error scenarios, or operational context (e.g., rate limits, data source, or output behavior). This is minimal transparency beyond the tool's name.

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

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

The description is concise and front-loaded with a clear purpose sentence, followed by a well-organized argument list. Every sentence earns its place, and there is no redundant or filler 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?

The output schema exists, so return values need not be described. However, the description lacks usage context, examples, or edge-case handling, and does not address the absence of annotations. For a 5-parameter tool, the description is functional but not fully complete for an agent to invoke it confidently in all 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?

The schema description coverage is 0%, so the description must compensate. The Args block provides meaningful explanations for all five parameters, such as 'How many times the volume should be above normal level' for volume_multiplier. This adds value beyond the bare schema titles and defaults, though some explanations are somewhat self-evident.

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: 'Detect coins with volume breakout + price breakout.' This is a specific verb+resource combination that distinguishes it from generic tools, though it doesn't explicitly differentiate among sibling scanners like volume_confirmation_analysis or smart_volume_scanner.

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 neither states prerequisites nor mentions exclusions, leaving the agent to infer usage solely from the parameter list. This is a significant gap given the number of sibling scanning 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 must disclose behavioral traits such as read-only nature, return format, or side effects. It only restates 'detailed volume confirmation analysis' and lists parameters, adding no transparency about what happens when the tool is invoked. The lack of any context about output or data sources 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 brief and front-loaded with the purpose, followed by a compact args list. It avoids unnecessary fluff and is easy to scan. However, the args section is somewhat redundant with the schema and could be integrated more naturally, so it does not earn a perfect score.

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, no annotations, and a tool with three parameters, the description is incomplete. It omits what the analysis returns, what inputs are valid (e.g., supported exchanges/timeframes), and how it differs from sibling tools. The context is insufficient for an agent to decide confidently when to invoke 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?

Schema description coverage is 0%, so the description must fully compensate. It provides only minimal explanations: symbol gets an example (BTCUSDT), but exchange and timeframe are merely restated by name with generic glosses ('Exchange name', 'Time frame for analysis'). No allowed values, formats, or additional semantics are given, despite defaults existing 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 states the tool performs 'Detailed volume confirmation analysis for a specific coin,' which clearly identifies the verb (analysis) and resource (volume confirmation for a coin). It is more specific than a tautology but does not explicitly distinguish itself from sibling volume-related tools like volume_breakout_scanner or smart_volume_scanner, lacking detail on what 'confirmation' entails.

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

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

No guidance is provided on when to use this tool versus alternatives. There is no mention of scenarios, prerequisites, or comparison to the many sibling tools. The description only lists arguments, leaving the agent to infer usage from the name alone.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It thoroughly explains the fold splitting, train/test ratios, robustness score calculation, and the meaning of each score threshold (ROBUST, MODERATE, WEAK, OVERFITTED). It also describes the return value structure, giving the agent a clear mental model of the tool's operation.

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 summary, fold explanation, threshold list, and parameter breakdown. Despite its length, every section adds necessary information and the formatting (bullet points, bold thresholds) makes it easy to scan. No fluff or redundancy.

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

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

Given the tool's complexity (9 params, no output schema, no annotations), the description is remarkably complete. It covers the algorithm, all parameter semantics, return values, and threshold interpretations. The agent has enough context to select and invoke the tool correctly without additional documentation.

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

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

The schema description coverage is 0%, but the description's 'Args' section provides complete semantics for all 9 parameters, including allowed values for 'strategy' (e.g., 'rsi | bollinger | macd'), examples for 'symbol', defaults, and constraints (e.g., 'max: 10' for n_splits). This adds substantial meaning beyond the raw schema.

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

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

The description opens with a specific verb and resource: 'Walk-forward backtest to detect overfitting — validates strategy on unseen data.' This clearly states what the tool does and distinguishes it from siblings like 'backtest_strategy' and 'compare_strategies' by emphasizing walk-forward methodology and overfitting detection.

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 clearly implies when to use this tool: to detect overfitting and validate on unseen data. It also provides concrete guidance, such as recommending '2y' period for meaningful splits. However, it does not explicitly list when not to use it or name alternative tools, so it falls short of the highest standard.

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 convey safety and behavior. It states 'real-time price quote,' implying a read-only fetch, which covers the basic safety profile. However, it doesn't disclose potential limitations such as delayed data, symbol validation errors, or whether additional fields (bid/ask, volume) are returned.

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 one-sentence summary front-loads the purpose, followed by a well-organized list of symbol examples. Every line adds value, 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?

For a single-parameter price quote tool with no output schema, the description covers the essential usage: which symbols are accepted and what the tool returns (a real-time price). It lacks explicit detail on output format or error behavior, but given the simplicity, it is nearly complete.

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

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

The input schema only provides 'symbol' with no description. The description compensates with a detailed 'Args:' section containing symbol formats and examples for stocks, crypto, ETFs, indices, FX, and Turkish securities, providing essential semantics 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 opens with 'Real-time price quote from Yahoo Finance for any stock, crypto, ETF or index,' clearly stating the verb (get price quote), resource (Yahoo Finance), and scope. This distinguishes it from sibling tools like scanners and analysis tools.

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

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

The description gives extensive examples of valid symbols across asset classes, establishing clear context for when to use this tool (whenever a single real-time price is needed). It does not explicitly name alternative tools or exclusions, but the asset-class coverage implicitly defines its scope.

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