MonteWalk

by N-lia

Overview Schema Related Servers Score Discussions

Python

Hybrid

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.0.0
Disambiguation3/5
Most tools have distinct purposes, but there is some overlap that could cause confusion. For example, 'get_price' and 'get_crypto_price' both retrieve price data but for different asset classes, and 'analyze_sentiment' and 'get_symbol_sentiment' both handle sentiment analysis with slightly different scopes. The descriptions help clarify, but an agent might misselect between similar tools.
Naming Consistency4/5
Tool names predominantly follow a consistent verb_noun pattern (e.g., 'add_to_watchlist', 'cancel_order', 'get_account_info'), with only minor deviations like 'flatten' (a verb without a noun) and 'var' (an abbreviation). The naming is readable and predictable overall, though not perfectly uniform.
Tool Count2/5
With 31 tools, the count is excessive for a trading/analysis server, leading to potential bloat. Many tools (e.g., 'max_drawdown', 'rolling_stats', 'var') could be consolidated into broader functions, and the inclusion of placeholders like 'get_orderbook' adds unnecessary complexity. A more focused set of 15-20 tools would be more appropriate.
Completeness4/5
The toolset covers a wide range of trading and analysis functions, including market data, order management, portfolio analysis, and backtesting. However, there are minor gaps, such as the lack of tools for modifying watchlist entries or handling more advanced order types (e.g., stop-loss). Overall, it supports core workflows effectively with few dead ends.
Average 3.1/5 across 31 of 31 tools scored. Lowest: 1.8/5.
See the Tool Scores section below for per-tool breakdowns.
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This repository includes a README.md file.
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{
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  ]
}
```
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. The description reveals nothing about how the tool behaves - whether it's read-only or mutative, what data sources it uses, what permissions are required, whether it has rate limits, what format the output takes, or any error conditions. This is completely inadequate for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - just three words. While this is efficient, it's arguably too brief given the complexity of VaR calculations and the complete lack of other documentation. However, it does front-load the core purpose without unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that this is a financial risk calculation tool with no annotations, 0% schema description coverage, and multiple similar sibling tools, the description is woefully incomplete. While an output schema exists (which might help with return values), the description doesn't explain what data the tool operates on, what methodology it uses, or how it differs from other risk tools. This leaves too many unanswered questions for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, meaning the single parameter 'confidence' has no documentation in the schema. The description provides no information about parameters at all - it doesn't mention that confidence is a parameter, what it represents, what range is valid, or how it affects the calculation. For a tool with one undocumented parameter, this represents a significant gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Calculates Value at Risk (VaR)' is a tautology that essentially restates the tool name 'var' (which is an abbreviation for VaR). It doesn't specify what resources or data it operates on (e.g., portfolio data, market data), nor does it distinguish this tool from sibling tools like 'portfolio_risk' or 'monte_carlo_simulation' that might also perform risk calculations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
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. There are multiple sibling tools that appear related to risk analysis (portfolio_risk, monte_carlo_simulation, max_drawdown), but the description doesn't explain when this specific VaR calculation is appropriate versus those other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions optimization and testing phases but lacks critical details: whether this is a read-only or mutating operation, computational intensity, rate limits, authentication needs, or what the output contains. The phrase 'Optimizes (Fast, Slow) on Train, tests on Test' implies a computational process but doesn't specify behavioral traits beyond the basic workflow.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just two sentences with zero wasted words. It's front-loaded with the main purpose and follows with implementation details. However, the brevity comes at the cost of completeness, making it somewhat under-specified rather than optimally concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of walk-forward analysis (a sophisticated financial modeling technique), 5 parameters with 0% schema coverage, no annotations, and an output schema (which helps but doesn't compensate for missing behavioral context), the description is inadequate. It mentions the analysis type and phases but omits critical context about parameters, behavioral characteristics, and usage guidance that would be essential for proper tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning none of the 5 parameters have descriptions in the schema. The tool description provides no information about any parameters - it doesn't mention 'symbol', date ranges, or train/test month parameters. With 0% coverage and 5 parameters, the description fails to compensate for the complete lack of parameter documentation in the structured schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs 'Walk Forward Analysis on MA Crossover' with optimization of 'Fast, Slow' parameters on training data and testing on test data. This specifies both the action ('Performs Walk Forward Analysis') and the resource/algorithm ('MA Crossover'), though it doesn't explicitly differentiate from sibling tools like 'run_backtest' or 'monte_carlo_simulation' beyond the specific analysis type.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 'run_backtest' or 'monte_carlo_simulation' for similar financial analysis tasks. It mentions the optimization and testing phases but offers no context about prerequisites, appropriate scenarios, or exclusions for this specific analysis method.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('Adds') but does not reveal critical traits such as whether it requires authentication, if it's idempotent, what happens on duplicate adds, rate limits, or the response format. This leaves significant gaps for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words, making it appropriately sized and front-loaded. Every word contributes directly to stating the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema (which reduces the need to describe return values) but no annotations and low parameter coverage, the description is minimally complete. It states the basic action but lacks details on behavior, parameters, and usage context, making it adequate only for very simple scenarios.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 1 parameter with 0% description coverage, and the description does not add any meaning beyond the parameter name 'symbol'. It fails to explain what 'symbol' represents (e.g., ticker format, asset type) or any constraints, which is inadequate given the low schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool's purpose with a clear verb ('Adds') and resource ('a symbol to the monitoring watchlist'), but it does not differentiate from its sibling tool 'remove_from_watchlist' or specify what type of symbols (e.g., stocks, crypto) or watchlist context (e.g., user-specific, global) it operates on, making it somewhat vague.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 'remove_from_watchlist' or other monitoring-related tools, nor does it mention prerequisites such as authentication or watchlist existence. It lacks explicit when/when-not instructions or context for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'Retrieves' data, implying a read-only operation, but doesn't mention authentication needs, rate limits, data freshness, or error handling. For a tool with no annotations, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with three sentences: purpose, args, and returns. It's front-loaded with the core function. However, the 'Returns' section is redundant given the output schema exists, slightly reducing efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (financial data retrieval), no annotations, and an output schema present, the description is minimally adequate. It covers the basic purpose and parameter, but lacks context on data scope (e.g., which fundamentals), alternatives, or behavioral traits. The output schema reduces the need to explain returns, but more completeness is needed for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds minimal semantics: it lists 'symbol' as 'The ticker symbol', which matches the schema's title 'Symbol'. With 0% schema description coverage, the description compensates slightly by naming the parameter, but doesn't provide format details (e.g., exchange prefixes), examples, or constraints beyond what's implied. Baseline is 3 due to the single parameter being documented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'Retrieves core financial and fundamental data', which provides a clear verb ('Retrieves') and resource ('core financial and fundamental data'). However, it doesn't differentiate from siblings like get_price, get_technical_summary, or get_account_info, which also retrieve financial data. The purpose is clear but 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.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to prefer get_fundamentals over get_price (for price data) or get_technical_summary (for technical indicators), nor does it specify prerequisites or exclusions. Usage is implied by the name but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the action is a removal, implying a destructive mutation, but doesn't disclose behavioral traits such as permissions needed, whether the operation is reversible, error handling (e.g., if symbol isn't in watchlist), or rate limits. This leaves significant gaps for an agent to understand 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with zero waste—it directly states the tool's action and target. It's appropriately sized for a simple tool and front-loaded with the essential information, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, no annotations, but has an output schema), the description is minimally complete. It states what the tool does, but lacks details on usage, parameters, and behavioral context. The output schema may cover return values, but the description doesn't provide enough guidance for effective use, making it adequate but with clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 1 parameter with 0% description coverage, and the tool description doesn't add any semantic details about the 'symbol' parameter. It doesn't explain what format the symbol should be in (e.g., ticker symbol like 'AAPL'), examples, or constraints. With low schema coverage, the description fails to compensate, leaving the parameter poorly documented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the action ('removes') and resource ('a symbol from the watchlist'), which is clear but basic. It doesn't differentiate from sibling tools like 'add_to_watchlist' beyond the verb, and lacks specificity about what type of watchlist or symbols are involved (e.g., stocks, crypto). This makes it vague in context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. It doesn't mention prerequisites (e.g., the symbol must already be in the watchlist), exclusions, or related tools like 'add_to_watchlist' for managing the watchlist. The description is standalone with no contextual usage hints.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It only states what the tool does without any information on performance characteristics, computational requirements, data sources, or error handling. For a financial calculation tool, this is a significant gap, as it doesn't address reliability, limitations, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any fluff. It's front-loaded with the key action and method, making it easy to parse. Every word contributes to understanding the tool's function, adhering to the principle of conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of financial calculations and the lack of annotations, the description is incomplete. It doesn't explain the output (though an output schema exists, which mitigates this), data requirements, or error conditions. For a tool with no behavioral transparency and minimal parameter guidance, it fails to provide sufficient context for reliable use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 1 parameter with 0% description coverage, so the description must compensate. It implies that 'tickers' are used for the calculation but doesn't explain their role, required format, or constraints. Since there's only one parameter, the baseline is 4, but the description adds minimal semantic value beyond what's inferred from the tool name, so it scores slightly lower.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'calculates' and the resource 'weights', specifying the method as 'Inverse Volatility (Naive Risk Parity)'. This distinguishes it from sibling tools like 'mean_variance_optimize' or 'portfolio_risk' by focusing on a specific risk-based weighting approach. However, it doesn't explicitly mention what the weights are for (e.g., portfolio allocation), 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.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, such as needing historical data or specific market conditions, nor does it compare to sibling tools like 'mean_variance_optimize' for different optimization methods. This lack of context makes it hard for an agent to decide when this tool is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 but offers minimal information. It mentions what the tool does but doesn't describe how it behaves—no details about data sources, timeframes, rate limits, error conditions, or what the calculation entails. For a tool with computational output, this leaves significant gaps in understanding its operational characteristics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with a clear purpose statement followed by parameter explanations in a structured format. Every sentence serves a purpose, though the parameter section could be slightly more detailed given the schema's limitations.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (calculating technical indicators with 2 parameters), no annotations, and an output schema present, the description is minimally adequate but incomplete. It covers the basic purpose and parameters but lacks behavioral context and usage guidance. The output schema reduces the need to describe return values, but more operational details would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds some semantic context by explaining that 'symbol' is a 'Ticker symbol' and 'indicators' is a 'List of indicators' with examples, which provides meaning beyond the bare schema (which has 0% description coverage). However, it doesn't fully compensate for the schema's lack of descriptions—for instance, it doesn't specify valid indicator names beyond examples or explain parameter interactions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Calculates') and resource ('technical indicators for a symbol'), making it immediately understandable. However, it doesn't distinguish this tool from potential sibling tools that might also calculate indicators, such as 'get_technical_summary' which appears in the sibling list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 'get_technical_summary' or other analysis tools in the sibling list. There's no mention of prerequisites, context, or exclusions that would help an agent choose between similar tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return is 'Formatted order history' but does not specify if this is read-only, requires authentication, includes pagination, or has rate limits, leaving significant gaps for a tool that likely accesses sensitive order data.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief and structured with 'Args' and 'Returns' sections, making it easy to parse. It could be slightly more front-loaded by stating the purpose more prominently, but it is efficient with minimal waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema, the description does not need to detail return values. However, for a tool with no annotations and low schema coverage, it lacks information on authentication, error handling, or behavioral traits, making it only minimally adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning by explaining the 'status' parameter values ('all', 'open', or 'closed'), which is not covered in the input schema (0% schema description coverage). However, it does not elaborate on the implications of these statuses or other potential parameters, so it partially compensates but not fully.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and resource 'order history from Alpaca', making the purpose evident. However, it does not explicitly differentiate from sibling tools like 'get_positions' or 'place_order', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 'get_positions' for current holdings or 'place_order' for new orders. The description lacks context for usage decisions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 fetching news and calculating sentiment but lacks details on behavioral traits like rate limits, data sources, time frames for 'recent', error handling, or authentication needs. This leaves significant gaps for a tool that performs analysis.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured with three sentences: purpose, args, and returns. Each sentence serves a clear function, and there's no wasted text, though it could be slightly more detailed without losing efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (fetching and analyzing news), no annotations, and an output schema that likely covers return values, the description is minimally complete. It states what the tool does but lacks depth on behavior, usage, or integration with siblings, making it adequate but with clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds minimal semantics beyond the input schema: it specifies that 'symbol' is a 'ticker symbol', which the schema only labels as 'Symbol'. However, with 0% schema description coverage and only one parameter, this is adequate but not comprehensive, aligning with the baseline for low coverage without full compensation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('fetches') and resource ('recent news for a symbol'), and it adds the action of calculating aggregate sentiment. However, it doesn't explicitly differentiate from sibling tools like 'get_news' or 'analyze_sentiment', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 'get_news' (which fetches news without sentiment) or 'analyze_sentiment' (which might analyze sentiment without fetching news). There's no mention of prerequisites, context, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool performs analysis and returns a signal, but lacks details on how the analysis is conducted (e.g., timeframes, data sources), whether it's read-only or has side effects, error handling, or performance characteristics like rate limits. This is a significant gap for a tool that likely involves complex calculations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured in two sentences: the first states the action and indicators, the second specifies the output. There's no wasted text, and it's front-loaded with the core purpose. However, it could be slightly more efficient by combining ideas, but it's already very tight.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (technical analysis with multiple indicators) and the presence of an output schema (which likely covers return values), the description is minimally adequate. It explains what the tool does and the signal output, but lacks details on behavior, parameters, and usage context. With no annotations and low schema coverage, it should do more to compensate, but the output schema helps mitigate some gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 1 parameter ('symbol') with 0% description coverage, meaning the schema provides no semantic details. The description doesn't add any parameter-specific information beyond what's implied by the tool's purpose (e.g., that 'symbol' likely refers to a financial instrument). It doesn't explain format, constraints, or examples, leaving the parameter minimally documented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: performing a technical analysis summary using specific indicators (RSI, MACD, Moving Averages) and returning a trading signal. It specifies the verb ('Performs'), resource ('technical analysis summary'), and outcome ('Buy', 'Sell', or 'Neutral' signal). However, it doesn't explicitly differentiate from sibling tools like 'compute_indicators' or 'run_backtest', which might also involve technical analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose this over sibling tools like 'compute_indicators' (which might compute raw indicators) or 'get_symbol_sentiment' (which might provide sentiment-based signals). There's no context on prerequisites, such as needing historical data or specific market conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the optimization method ('Mean-Variance Optimization (Max Sharpe)') but lacks details on computational behavior, such as whether it requires historical data, handles constraints, or outputs specific metrics. For a tool with no annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'Calculates optimal portfolio weights using Mean-Variance Optimization (Max Sharpe).' It is front-loaded with the core purpose and uses no unnecessary words, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (portfolio optimization with 2 parameters) and the presence of an output schema (which reduces the need to describe return values), the description is minimally adequate. However, with no annotations and 0% schema coverage, it lacks details on behavior and parameters. The description covers the basic purpose but falls short in providing a complete context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning parameters 'tickers' and 'lookback' are undocumented in the schema. The description adds no information about these parameters, such as what 'tickers' represents (e.g., stock symbols) or how 'lookback' is used (e.g., time period for data). With low coverage and no compensation in the description, this score reflects inadequate parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Calculates optimal portfolio weights using Mean-Variance Optimization (Max Sharpe).' It specifies the verb ('calculates'), resource ('optimal portfolio weights'), and method ('Mean-Variance Optimization (Max Sharpe)'), which distinguishes it from other portfolio-related tools like 'portfolio_risk' or 'risk_parity'. However, it doesn't explicitly differentiate from all siblings, such as 'monte_carlo_simulation' or 'run_backtest', which might also involve portfolio optimization.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context, or exclusions, nor does it reference sibling tools like 'portfolio_risk' or 'risk_parity' that might be related. Without such guidance, users must infer usage based on the tool 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.
Behavior2/5
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 searches and returns a formatted string, but lacks details on behavioral traits like rate limits, authentication needs, error handling, or whether it's a read-only operation. This is a significant gap for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with the core purpose stated first. The Args and Returns sections are structured clearly. However, the 'Returns' section is somewhat vague ('Formatted string with search results'), and the overall text could be slightly more efficient, but it avoids unnecessary fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema (which should cover return values), the description doesn't need to explain returns in detail. However, with no annotations and only one parameter, it provides basic purpose and parameter info but lacks usage guidelines and behavioral context, making it minimally adequate but incomplete for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds some meaning beyond the input schema, which has 0% description coverage. It explains that 'query' is a 'Search term (e.g., 'bitcoin', 'BTC', 'ethereum')', providing examples and clarifying its purpose. However, it doesn't detail constraints like length, case-sensitivity, or special characters, leaving room for improvement.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Searches for cryptocurrencies by name or symbol.' It specifies the verb ('searches') and resource ('cryptocurrencies'), and the scope ('by name or symbol') is helpful. However, it doesn't explicitly differentiate from sibling tools like 'get_crypto_market_data' or 'get_trending_crypto', which might offer overlapping functionality, preventing 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.
Usage Guidelines2/5
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. For example, it doesn't compare to 'get_trending_crypto' for trending searches or 'get_crypto_market_data' for broader data. There's no mention of prerequisites, exclusions, or specific contexts, leaving usage ambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the calculation purpose but lacks details on inputs (e.g., data format, time series requirements), outputs (though an output schema exists), error handling, or computational characteristics (e.g., performance, assumptions). This is inadequate for a tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It's front-loaded with the core action ('Calculates') and resource ('Maximum Drawdown'), making it immediately clear. Every word earns its place, achieving optimal brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a financial calculation), no annotations, and an output schema (which covers return values), the description is minimally complete. It states what the tool does but lacks context on usage, behavioral traits, or integration with sibling tools, leaving gaps that could hinder an agent's effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the absence of inputs. The description doesn't need to add parameter details, but it could mention implicit inputs (e.g., data context). Since no parameters exist, a baseline of 4 is appropriate, as the description doesn't contradict the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Calculates Maximum Drawdown' clearly states the tool's function with a specific verb ('Calculates') and resource ('Maximum Drawdown'), which is a financial metric. However, it doesn't differentiate from sibling tools like 'portfolio_risk', 'var', or 'rolling_stats' that also compute financial risk metrics, leaving ambiguity about when to use this specific calculation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. With siblings like 'portfolio_risk', 'var', and 'rolling_stats' that handle related financial analyses, there's no indication of context, prerequisites, or exclusions to help an agent choose appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While it mentions the tool 'Gets' data (implying read-only), it doesn't address important behavioral aspects like rate limits, authentication requirements, data freshness, error conditions, or what 'comprehensive' specifically includes. The description is minimal and leaves critical behavioral questions unanswered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with three focused sections: purpose statement, parameter documentation, and return value description. Each sentence earns its place, though the 'Returns' section could be more informative given the output schema exists. The structure is clear and front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations, 0% schema description coverage, but with an output schema, the description is minimally adequate. It covers the basic purpose and parameter semantics, but lacks behavioral context and usage guidance. The existence of an output schema means the description doesn't need to detail return values, but it should provide more operational context for effective tool selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage and only one parameter, the description provides essential semantic context by specifying that 'coin_id' requires a 'CoinGecko ID' with examples ('bitcoin', 'ethereum'). This adds meaningful value beyond the bare schema, though it doesn't explain where to find these IDs or validate them. For a single parameter tool, this is reasonably helpful.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Gets') and resource ('comprehensive market data for a cryptocurrency'), distinguishing it from simpler price-only tools like 'get_crypto_price'. However, it doesn't explicitly differentiate from other market data tools like 'get_fundamentals' or 'get_technical_summary' that might provide different aspects of market data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. With siblings like 'get_crypto_price', 'get_fundamentals', 'get_technical_summary', and 'get_trending_crypto' that all provide different types of market-related data, there's no indication of what makes this tool's 'comprehensive market data' unique or when it should be preferred over more specialized tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 computing rolling mean and volatility but doesn't disclose behavioral traits such as data source, time frame, output format, error handling, or computational requirements. This leaves significant gaps for a tool with parameters and an 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single sentence that directly states the tool's function. It is front-loaded and wastes no words, 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.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema, the description doesn't need to explain return values. However, with no annotations, 2 parameters, and 0% schema coverage, the description is minimal and lacks context on usage, behavior, or parameter details, making it adequate but incomplete for effective tool selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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, but it adds no information about parameters beyond what the schema provides (symbol and window). It implies these parameters are used for the computation but doesn't explain their roles, units, or constraints, resulting in a baseline score due to the lack of enhancement.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool computes rolling mean and volatility, which is a specific verb (computes) and resource (rolling statistics). It distinguishes from siblings like 'compute_indicators' or 'get_technical_summary' by focusing specifically on rolling calculations, though it doesn't explicitly contrast with them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 'compute_indicators' or 'get_technical_summary', nor does it mention prerequisites or context for application. It only states what it does, not when it's appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 implementation ('FinBERT on Modal via Public Endpoint'), which adds some context, but lacks critical behavioral details: it doesn't disclose rate limits, authentication needs, error handling, or what 'polarity, confidence, and classification' entail. For a tool with no annotations, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and concise. It uses three sentences: one for the purpose, one for the parameter, and one for the return value. Each sentence adds value without redundancy, and the information is front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (sentiment analysis with 1 parameter), no annotations, and an output schema (implied by 'Returns'), the description is moderately complete. It covers purpose, parameter semantics, and return structure, but lacks behavioral context like rate limits or error handling. The output schema likely details the return dictionary, so the description doesn't need to explain return values further.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful semantics beyond the input schema. The schema has 0% description coverage and only defines 'text' as a string. The description clarifies the parameter's purpose ('Text to analyze') and provides an example ('e.g., news headline, article'), which compensates for the low schema coverage. With 1 parameter, this is adequate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Analyzes the sentiment of a given text using FinBERT on Modal (via Public Endpoint).' It specifies the verb ('analyzes'), resource ('sentiment'), and implementation details. However, it doesn't explicitly differentiate from sibling tools like 'get_symbol_sentiment', which appears to be a related sentiment analysis tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal usage guidance. It includes an example ('e.g., news headline, article') but doesn't specify when to use this tool versus alternatives like 'get_symbol_sentiment' or other text-processing tools. There's no explicit context, exclusions, or prerequisites mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool cancels an order, implying a destructive mutation, but does not cover critical aspects like required permissions, rate limits, error conditions (e.g., if order is already canceled), or what happens upon success. This is a significant gap for a mutation tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and well-structured: a clear purpose statement followed by brief, formatted sections for Args and Returns. Every sentence earns its place, with no redundant information, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a destructive mutation with 1 parameter) and the presence of an output schema (which handles return values), the description is moderately complete. It covers the basic purpose and parameter semantics but lacks behavioral details like error handling or usage context. With no annotations and incomplete behavioral transparency, it meets minimum viability but has clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful context for the single parameter: 'order_id: The Alpaca order ID to cancel.' This clarifies the parameter's purpose and format (Alpaca-specific ID), which is valuable since schema description coverage is 0% (no schema descriptions). With 0% coverage, the description compensates well, though it could specify ID format or constraints for a perfect score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Cancels a specific open order.' It specifies the verb ('cancels') and resource ('a specific open order'), making the action unambiguous. However, it does not explicitly differentiate from sibling tools like 'place_order' or 'get_order_history', which would require a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites (e.g., order must be open), exclusions (e.g., cannot cancel filled orders), or refer to sibling tools like 'place_order' for creating orders or 'get_order_history' for checking status. This leaves usage context implied but not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It implies a read-only operation by using 'Get', but doesn't specify if it's safe, has rate limits, requires specific permissions, or what the output format entails. For a tool with zero annotation coverage, this is a significant gap in 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the key action and details. Every word earns its place by specifying the tool's function and key data points without any waste or fluff, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 0 parameters, 100% schema coverage, and an output schema exists, the description is minimally adequate. However, with no annotations and sibling tools that might overlap, it lacks completeness in behavioral and usage context. It meets the baseline for a simple read tool but doesn't provide enough guidance for optimal agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and the input schema has 100% coverage (though empty). The description doesn't need to explain parameters, so it appropriately avoids redundancy. A baseline of 4 is assigned as it doesn't add unnecessary param info, but it doesn't fully compensate for other gaps, so it's not a 5.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('Alpaca account information'), and lists key data fields (equity, buying power, day trade status). However, it doesn't explicitly differentiate from sibling tools like 'get_positions' or 'get_order_history', which might also retrieve account-related data, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context, or exclusions, such as whether it requires authentication or if it's for real-time vs. historical data. This leaves the agent without usage direction beyond the basic purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions retrieval and the return format ('JSON string of news articles with titles and publishers'), but lacks details on rate limits, authentication needs, error handling, or data freshness (e.g., how 'recent' is defined). For a tool with no annotation coverage, this leaves significant gaps in understanding its operational behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the core purpose, followed by clear sections for arguments and returns. Every sentence adds value without redundancy, 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.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no annotations, but has an output schema), the description is partially complete. It covers the purpose and parameters adequately, and the output schema likely details the return structure, reducing the need for return value explanation. However, it lacks behavioral details and usage guidelines, leaving room for improvement in overall context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful context beyond the input schema, which has 0% description coverage. It explains that 'symbol' is a 'Ticker symbol' and 'max_items' controls the 'Maximum number of news items to return', clarifying their roles. However, it does not specify constraints like valid symbol formats or max_items range, so it doesn't fully compensate 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Retrieves recent news headlines for a given symbol.' This specifies the verb ('retrieves'), resource ('news headlines'), and scope ('for a given symbol'). However, it does not explicitly differentiate from sibling tools like 'get_symbol_sentiment' or 'get_fundamentals', which might also involve news or data retrieval, so it falls short of 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.
Usage Guidelines2/5
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 sibling tools like 'get_symbol_sentiment' (which might analyze news sentiment) or 'search_crypto' (which could include news), nor does it specify prerequisites or exclusions. Usage is implied only by the purpose statement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses key behavioral traits: it's a read operation ('fetches'), it has limitations ('does not provide Level 2 data'), and it's essentially non-functional ('placeholder', 'returns message indicating unavailability'). This gives the agent important context about what to expect.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded with the core purpose. The note about limitations is important context. The Args/Returns sections are structured but could be more integrated with the main description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema (though not shown here), the description doesn't need to detail return values. It adequately explains the tool's purpose, limitations, and parameters for a simple placeholder tool. The completeness is reasonable for this context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description adds the meaning of the single parameter: 'symbol: The ticker symbol.' This compensates somewhat for the schema gap. However, it doesn't provide format examples or constraints beyond what's obvious from the parameter name.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Fetches the current order book' which is a clear verb+resource combination, but it doesn't distinguish this from potential sibling tools like 'get_price' or 'get_crypto_market_data'. The purpose is understandable but lacks differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. While it mentions that 'yfinance does not provide Level 2 data' and calls it a 'placeholder', this doesn't help an agent decide when to invoke it versus other data-fetching tools in the sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions the tool 'Gets' data and returns a 'Formatted string', but lacks details on behavioral traits such as rate limits, authentication needs, data sources, or error handling. This leaves significant gaps in understanding how the tool behaves in practice.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and well-structured, consisting of two brief sentences that directly state the purpose and return value. Every word earns its place, with no wasted information, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, output schema provided), the description is minimally adequate. It covers the basic purpose and return format, but with no annotations and a lack of behavioral details, it doesn't fully prepare an agent for potential complexities like data freshness or formatting specifics.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter information is needed. The description appropriately doesn't discuss parameters, which is efficient and avoids redundancy, earning a high score for not adding unnecessary details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Gets') and resource ('top trending cryptocurrencies in the last 24 hours'), making it easy to understand what it does. However, it doesn't explicitly distinguish itself from potential sibling tools like 'get_crypto_market_data' or 'search_crypto', which might also provide related cryptocurrency information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context, or exclusions, leaving the agent to infer usage based on the purpose alone without any explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns health status but doesn't elaborate on what that entails (e.g., uptime, metrics, error details), response format, or any operational constraints like rate limits or authentication needs. This leaves significant gaps in 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with zero wasted words, making it highly concise and front-loaded. It efficiently communicates the core purpose without any fluff or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, no annotations) and the presence of an output schema, the description is minimally adequate. However, it lacks details on what 'health status' includes, which could be important for an agent to interpret the results, even with an output schema. This makes it somewhat incomplete for full contextual understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't mention parameters, which is correct for a parameterless tool, earning a baseline score of 4 for not adding unnecessary information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Returns') and resource ('health status of the MonteWalk server'), making it immediately understandable. However, it doesn't distinguish this from any sibling tools, which is reasonable since no other tools appear to serve a similar health-check function among the listed siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 under what conditions or scenarios it should be invoked. It lacks any mention of prerequisites, frequency, or contextual triggers, leaving usage entirely implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the simulation uses Geometric Brownian Motion and that 'visualize' returns a histogram, but it omits critical details like computational intensity, assumptions (e.g., log-normal distribution), error handling, or output format beyond the histogram. This is inadequate for a simulation tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: the first sentence states the purpose, followed by a bulleted list of parameters with clear explanations. Every sentence earns its place without redundancy, making it efficient and easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (simulation with mathematical modeling) and the presence of an output schema, the description is partially complete. It covers the purpose and parameters but lacks details on behavioral aspects like performance or assumptions. The output schema likely handles return values, so the description's focus on parameters is adequate but not fully comprehensive for such a tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful semantics beyond the input schema, which has 0% description coverage. It explains that 'simulations' is the 'Number of paths to simulate', 'days' is 'Number of days to project forward', and 'visualize' returns 'a histogram of final outcomes'. This compensates well for the schema's lack of descriptions, though it could elaborate on units or constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool 'runs a Monte Carlo simulation using Geometric Brownian Motion (Log Returns)', which is a specific verb ('runs') with resource ('Monte Carlo simulation') and method details. It distinguishes from siblings like 'portfolio_risk' or 'var' by specifying the simulation method, though not explicitly contrasting them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 'portfolio_risk', 'var', or 'run_backtest'. It lacks context on typical use cases (e.g., financial forecasting, risk assessment) or prerequisites, offering only implied usage through parameter descriptions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns a value but doesn't explain computational methods, assumptions, data sources, or potential limitations. For a risk calculation tool with zero annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function without any wasted words. It's front-loaded and appropriately sized for a simple tool with no parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 0 parameters, 100% schema coverage, and an output schema exists, the description is minimally adequate. However, as a risk calculation tool with no annotations, it lacks details on methodology or context that would help an agent use it correctly, especially compared to siblings like 'var'.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and schema description coverage is 100%, so there's no need for parameter details in the description. The baseline for this scenario is 4, as the description appropriately focuses on the tool's purpose without redundant parameter information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Returns') and resource ('annualized volatility of the portfolio'), making it immediately understandable. However, it doesn't explicitly differentiate from siblings like 'var' or 'max_drawdown', which are also risk-related tools, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. There's no mention of prerequisites, context, or comparisons to sibling tools like 'var' or 'max_drawdown', leaving the agent to infer usage based on the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states it 'Gets the current price' but doesn't mention source reliability (CoinGecko), rate limits, error handling, or whether it's a read-only operation. For a financial data tool with zero annotation coverage, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and appropriately sized, with a clear purpose statement followed by parameter details in a formatted 'Args' and 'Returns' section. It's front-loaded and wastes no words, though the 'Returns' section could be slightly more specific.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity, two parameters, and the presence of an output schema (which handles return values), the description is mostly adequate. However, the lack of behavioral context (e.g., data source, limitations) and usage guidelines relative to siblings leaves room for improvement.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant value beyond the input schema, which has 0% description coverage. It explains that 'coin_id' uses CoinGecko IDs with examples ('bitcoin', 'ethereum', 'solana') and clarifies 'vs_currency' is for comparison with a default. This compensates fully for the schema's lack of documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Gets') and resource ('current price of a cryptocurrency'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_price' or 'get_crypto_market_data', which likely serve related but distinct purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. With siblings like 'get_price', 'get_crypto_market_data', and 'get_fundamentals', there's no indication of what makes this tool unique or when it's the appropriate choice, leaving usage context ambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 return structure but lacks critical behavioral details: whether this requires authentication, if it's read-only (implied by 'Retrieves' but not explicit), rate limits, latency, or error conditions. For a financial tool with no annotations, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded: the first sentence states the core purpose, and the second clarifies the return format. Every sentence earns its place with no wasted words, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters, an output schema exists (implied by 'Returns' section), and annotations are absent, the description is minimally complete. However, for a financial data retrieval tool, it lacks context on prerequisites (e.g., authentication), data freshness, or error handling, which could hinder an agent's effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and schema description coverage is 100% (though empty). The description doesn't need to explain parameters, so it appropriately focuses on output. Baseline for 0 params is 4, as it efficiently describes the tool's function without unnecessary parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Retrieves') and the exact resources ('current state of all held positions and cash balance'), distinguishing it from siblings like get_account_info (likely broader account data) or get_order_history (historical orders). It precisely defines what the tool does without ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. For example, it doesn't clarify if this is for real-time portfolio snapshots versus historical data, or how it differs from get_account_info which might include similar information. The description only states what it does, not when it's appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 logs for audit purposes, which implies a write operation, but doesn't disclose behavioral traits like whether it's idempotent, what permissions are needed, how logs are stored, or rate limits. For a write tool with zero annotation coverage, this is a significant gap, though it at least hints at the audit context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: the first sentence states the purpose, followed by a brief, structured parameter explanation. Every sentence adds value without waste, 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.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (2 parameters, no nested objects) and the presence of an output schema (which means return values don't need explanation), the description is mostly complete. It covers purpose and parameters, but lacks behavioral details like error handling or audit specifics, which holds it back from a perfect score.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
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 adds meaning by explaining both parameters: 'action_type' as a category with examples (e.g., 'REASONING', 'TRADE_DECISION', 'ERROR') and 'details' as a description of the action. This clarifies semantics beyond the bare schema, though it could provide more detail on format or constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Logs an agent action or reasoning step for audit purposes.' It specifies the verb ('Logs') and resource ('agent action or reasoning step'), and distinguishes it from siblings by focusing on audit logging rather than financial operations. However, it doesn't explicitly differentiate from potential logging siblings (none exist in the list), so it's not 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.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for audit logging of agent actions, but provides no explicit guidance on when to use this tool versus alternatives. There are no similar logging tools among siblings, so no comparison is needed, but it lacks context on prerequisites or exclusions (e.g., when not to log). This results in an implied usage score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool submits orders but lacks details on execution behavior (e.g., immediate vs. pending), error handling, rate limits, authentication needs, or whether it's a simulation (paper trading) versus real trading. This is inadequate for a mutation tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded with the core purpose in the first sentence, followed by structured parameter and return explanations. It avoids redundancy, though the return statement could be more specific.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a trading tool with 5 parameters, no annotations, and an output schema (implied by 'Returns'), the description is partially complete. It covers parameter semantics well but lacks behavioral context (e.g., execution details, error cases) and relies on the output schema for return values, leaving gaps in operational guidance.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant meaning beyond the input schema, which has 0% schema description coverage. It explains each parameter's purpose (e.g., 'symbol: Ticker symbol', 'limit_price: Required if order_type is limit'), clarifying semantics that the schema alone does not provide, though it could elaborate on units or constraints for qty and limit_price.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Submits a market or limit order'), the target resource ('to Alpaca paper trading'), and distinguishes it from siblings like cancel_order or get_order_history by focusing on order creation rather than modification or retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for trading orders in a paper trading environment but does not explicitly state when to use this tool versus alternatives like cancel_order or get_order_history, nor does it mention prerequisites such as account setup or market hours.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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. While 'Backtests' implies a computational operation, it doesn't describe what the tool actually returns (though an output schema exists), whether it's resource-intensive, if it requires market data access, or what happens when parameters are invalid. The description lacks behavioral context beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly structured and concise. The first sentence states the purpose clearly, followed by a well-organized parameter list with brief explanations. Every sentence earns its place, and there's no redundant or unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that an output schema exists (so return values are documented elsewhere), the description provides adequate context for a backtesting tool. It covers the strategy type and all parameters with semantic meaning. The main gap is lack of behavioral context about what the backtest actually computes and returns, but the output schema mitigates this concern.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description provides meaningful semantic information for all 6 parameters. It explains what each parameter represents (ticker symbol, moving average periods, date ranges, visualization flag), which compensates for the lack of schema descriptions. The only minor gap is not specifying date formats or valid ranges for the moving average periods.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Backtests') and the specific strategy ('Moving Average Crossover strategy'), which distinguishes it from other financial/trading tools like 'monte_carlo_simulation' or 'walk_forward_analysis'. It provides a verb+resource combination that is precise and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'walk_forward_analysis' for different backtesting approaches or 'compute_indicators' for calculating moving averages without backtesting. There's no context about prerequisites or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the action ('closes all open positions') and return value, but lacks critical behavioral details: whether this requires confirmation, its irreversible nature, potential market impact, rate limits, or error conditions. For a destructive operation with zero annotation coverage, this is a significant gap in disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core action in the first sentence and adds return information in a second sentence. Both sentences earn their place by providing essential operational and output details with zero waste or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's high complexity (destructive financial operation) and lack of annotations, the description is minimally adequate. It covers purpose and output (aided by the output schema), but fails to address critical behavioral aspects like safety warnings or prerequisites. With no annotations and an output schema, it meets baseline but leaves gaps for a high-stakes tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so no parameter documentation is needed. The description does not mention parameters, which is appropriate. Baseline is 4 for zero parameters, as it avoids unnecessary detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Immediately closes all open positions') and distinguishes it from siblings like 'cancel_order' (which cancels specific orders) or 'get_positions' (which retrieves position data). The verb 'closes' and resource 'all open positions' are precise and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context ('Immediately closes all open positions'), suggesting this tool is for risk management or liquidation scenarios. However, it does not explicitly state when to use it versus alternatives like 'cancel_order' for specific orders or provide exclusions (e.g., not for partial closures). The guidance is clear but lacks explicit sibling differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 retrieves data (read-only behavior) and mentions two return formats (JSON string or base64-encoded chart), which adds useful context. However, it does not cover potential rate limits, authentication needs, data freshness, or error handling, leaving gaps in behavioral disclosure for a data retrieval tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by a well-structured 'Args' and 'Returns' section with clear bullet points. Every sentence earns its place by providing essential information without redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (4 parameters, no annotations, but with output schema indicated by 'Returns'), the description is mostly complete. It explains parameters thoroughly and specifies return formats, but lacks details on output schema structure (e.g., JSON fields) or potential errors. With output schema existence, it need not fully explain return values, but could benefit from more behavioral context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description must fully compensate. It provides detailed semantics for all 4 parameters: 'symbol' as ticker symbol with examples, 'interval' and 'period' with valid value lists, and 'visualize' explaining its effect on return type. This adds significant meaning beyond the schema's basic titles and enums.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool 'retrieves historical price data (OHLCV) for a given symbol,' which is a specific verb (retrieves) and resource (historical price data) with the scope (OHLCV format). It distinguishes from siblings like 'get_crypto_price' by specifying historical data rather than current prices, and from 'get_fundamentals' by focusing on price data rather than company metrics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for historical price data retrieval, but does not explicitly state when to use this tool versus alternatives like 'get_crypto_price' for current prices or 'get_technical_summary' for analyzed data. It provides context (historical data) but lacks explicit guidance on exclusions or comparisons with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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