QuantRisk

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

No annotations are provided, so the description carries full burden. It lists metrics but does not disclose behavioral traits like data source reliance, whether calculations are reversible, or any side effects. Lacks detail on how the tool processes inputs.

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

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

Single, concise sentence that front-loads the core purpose and lists outputs. No redundant information. Earns its place.

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

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

While the description lists output metrics, it lacks details on return format, error handling, or limitations (e.g., free tier constraints mentioned in schema). Given no output schema, it should explain expected results more thoroughly.

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

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

Schema description coverage is 100%, so schema already documents parameters well. The description adds no new parameter details; it only lists output metrics. Baseline of 3 is appropriate as schema does the heavy lifting.

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

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

The description clearly states it calculates core risk metrics and lists specific outputs (VaR, CVaR, volatility, beta, max drawdown). It distinguishes from sibling tools like compare_portfolios or optimize_portfolio by focusing on risk assessment.

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

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

No explicit guidance on when to use this tool vs. alternatives. The description implies it's for risk metrics, but fails to provide context about prerequisites or when not to use it, such as for performance attribution.

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

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

It discloses the models used (Black-Scholes for European, binomial for American) and the paid-tier restriction, but lacks details on assumptions, data source, or performance characteristics.

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

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

Two concise sentences with no wasted words; effectively front-loads the purpose and key details.

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

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

The description is adequate for a calculation tool with a rich input schema, but it could briefly note the output structure since no output schema is provided.

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

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

Schema coverage is 100%, so the description adds minimal value beyond the schema; it names the Greeks but does not elaborate on parameter behavior.

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

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

The description clearly states it calculates option Greeks (delta, gamma, theta, vega, rho) for individual options or portfolios, distinguishing it from sibling tools like analyze_risk or monte_carlo_simulation.

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

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

The description mentions 'Paid tier only' as a usage restriction but does not provide explicit guidance on when to use versus alternatives, nor when not to use.

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

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

With no annotations, the description carries the full burden. It mentions 'Paid tier only' as a constraint but does not disclose any behavioral traits like side effects or whether the tool is read-only. The description is adequate but lacks extra context beyond the basic operation.

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

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

The description is extremely concise—one sentence plus a pricing note—with no filler. It is front-loaded and every part earns its place.

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

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

Although the tool has moderate complexity and no output schema, the description only vaguely mentions 'all key risk and return metrics' without specifying the output format or listing metrics. This leaves the agent with incomplete context about what to expect.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value beyond schema by specifying 'head-to-head across all key risk and return metrics' and noting 'Paid tier only', which provides context not present in the schema.

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

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

The description uses a specific verb ('Compare') and resource ('portfolio allocations'), and clearly distinguishes the tool from siblings by focusing on head-to-head comparison across key metrics, which no other tool explicitly does.

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

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

The description clearly states when to use this tool (comparing two or more portfolios head-to-head), but does not provide explicit guidance on when not to use it or mention alternatives, though siblings are distinct enough that confusion is unlikely.

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

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

No annotations are provided, so the description bears full responsibility for disclosing behavioral traits. However, it fails to mention important details such as data source, handling of missing data, output format, rate limits, or computational constraints. The parameter descriptions include some behavioral hints (e.g., free/paid tier limits), but the primary description lacks transparency.

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

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

The description is extremely concise, consisting of two clear sentences without any redundant or unnecessary words. The main purpose is front-loaded, making it efficient for an agent to quickly understand the tool's function.

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

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

Given the absence of an output schema and annotations, the description is too minimal. It does not explain what the tool returns (e.g., a matrix object, a list of pairs, or a visualization), nor does it provide enough context for an agent to assess suitability beyond the basic purpose. The completeness is insufficient for a tool with three parameters and no structured output documentation.

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

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

The input schema has 100% description coverage, with each parameter (method, tickers, lookback_days) already documented with constraints, defaults, and explanations. The description text adds no further parameter-specific meaning, so a baseline of 3 is appropriate.

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

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

The description uses a specific verb ('Compute') and resource ('pairwise correlation matrix'), clearly stating the tool's primary function and adding value by mentioning identification of highly correlated pairs and diversification opportunities. It distinguishes itself from siblings like 'analyze_risk' and 'price_history'.

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

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

The description provides no explicit guidance on when to use this tool versus alternatives, nor does it mention any exclusions or prerequisites. The mention of 'diversification opportunities' vaguely implies portfolio analysis, but there is no comparison to sibling tools or 'when not to use'.

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

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It only hints at outputs (percentile outcomes, probability of loss) but omits other important traits such as computational intensity, rate limits, data dependencies, or side effects. This is insufficient for a simulation tool.

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

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

The description is a single sentence of 22 words, efficiently conveying the core purpose without extraneous information. Every word earns its place.

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

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

Given the absence of an output schema, the description provides useful context by mentioning percentile outcomes and probability of loss. However, it lacks details on the exact return format or structure of results. For a complex simulation tool, slightly more output context would improve completeness.

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

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

The input schema has 100% description coverage, so the baseline is 3. The description does not add any parameter-specific meaning beyond what is already in the schema; it remains generic. Thus, the score stays at the baseline.

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

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

The description clearly identifies the tool as running a Monte Carlo simulation on a portfolio to model future return distributions, including percentiles and loss probability. It is specific and distinct from sibling tools like analyze_risk or stress_test, which focus on other risk analytics.

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

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

The description does not provide any guidance on when to use this tool versus alternatives such as stress_test or analyze_risk. There are no hints about prerequisites, limitations, or use cases, leaving the agent to infer appropriateness.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals the paid tier restriction but does not mention any side effects, authentication requirements, rate limits, or output format. The behavioral description is minimal.

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

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

The description is concise with only two sentences. The first sentence front-loads the core purpose and method, and the second adds key variations and a condition (paid tier). No redundant or unnecessary text.

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

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

Despite having 6 parameters including nested objects and no output schema, the description does not explain what the tool returns (e.g., optimal weights, expected return). For a complex optimization tool, this is a significant gap in completeness.

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

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

Schema description coverage is 100%, meaning the input schema already documents all parameters with detailed descriptions. The description adds minimal extra value beyond listing the three objectives, so the baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Find the optimal portfolio allocation using mean-variance optimization' and specifies supported objectives (max Sharpe, min variance, target return). It distinguishes from sibling tools like analyze_risk or correlation_matrix by focusing on optimization.

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

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

The description mentions supported objectives and 'Paid tier only' but lacks explicit guidance on when to use this tool versus alternatives (e.g., monte_carlo_simulation). No prerequisites or exclusions are mentioned, leaving usage context unclear.

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

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

No annotations provided, so description bears full burden. It does not disclose important behavioral traits such as data requirements, tier limits (free vs paid), or what the output contains beyond mentioning ratios. The schema indicates tier limits but description omits them.

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

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

Two concise sentences with no unnecessary words. Efficiently captures the core functionality and computed metrics.

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

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

Lists ratios but does not describe output structure or provide examples. Lacks mention of free/paid tier constraint (only in schema) and does not clarify data sourcing. Adequate but with gaps.

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

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

Schema description coverage is 100%, so each parameter already has a clear description. The tool description adds value by explaining the computed ratios, but it does not add extra meaning beyond the schema for the parameters themselves.

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

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

Description clearly states the tool breaks down performance into factor exposures, sector allocation, and position contributions, and computes several risk-adjusted ratios. It distinguishes from sibling tools like analyze_risk or optimize_portfolio by focusing on attribution, but does not explicitly contrast.

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

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

No guidance on when to use this tool versus siblings or when not to use it. No mention of prerequisites or context that would help an agent decide.

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

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

No annotations provided. Description implies read-only behavior but does not explicitly state it. No mention of authorization, rate limits, or other behavioral traits. Adequate for a simple fetch operation.

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

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

Two sentences, no wasted words. Front-loaded with the main action and key constraints.

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

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

No output schema and description does not explain return format (e.g., OHLCV structure). For a raw data tool, more specifics about the output would be helpful for agent to use the result effectively.

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

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

Schema coverage is 100% with descriptions for each parameter. The description adds value by explaining tier-based restrictions on tickers and days, complementing the schema details.

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

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

Description clearly states it fetches historical OHLCV price data for tickers. Differentiates free vs paid tiers. However, it does not explicitly distinguish from sibling tools like analyze_risk, though the function is distinct.

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

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

Describes when to use (fetching historical prices) and tier limitations. Does not provide when-not-to-use or alternatives among sibling tools, but siblings are analytical, not data fetching.

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

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

With no annotations, the description carries the full burden. It discloses that the tool returns concentration metrics and breaks down by sector/market cap/asset class, but does not mention computational cost, required permissions, or whether it is read-only. Adequate but not rich.

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

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

Two sentences, front-loaded with purpose. No wasted words. Efficient and to the point.

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

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

For a single-parameter analysis tool with no output schema, the description is reasonably complete. It explains inputs, outputs (concentration metrics, HHI), and breakdown dimensions. Could benefit from mentioning data source or assumptions, but overall adequate.

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

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

The schema has 100% coverage with a clear description of the 'positions' parameter. The tool description adds minimal value beyond the schema, mentioning the breakdown dimensions. Baseline 3 is appropriate as schema already does the heavy lifting.

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

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

The description clearly states the tool breaks down portfolio exposure by GICS sector, market cap, and asset class, and returns concentration metrics including the Herfindahl-Hirschman Index. This specific verb-resource combination distinguishes it from sibling tools like analyze_risk or performance_attribution.

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

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

The description implies usage for sector exposure and concentration analysis but does not explicitly state when to use this tool over alternatives like analyze_risk or compare_portfolios. No guidance on prerequisites or exclusion criteria.

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

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

No annotations provided, so description carries full burden. It discloses free vs paid tier limits (max positions, custom shocks) but omits whether the operation is read-only, auth requirements, or rate limits.

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

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

Single sentence that front-loads the key purpose and scope (historical crises, paid tier custom shocks), with no wasted words.

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

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

Description covers main functionality and tier constraints, but lacks explanation of output/return values, which is needed since there is no output schema. For a tool with 3 parameters and nested objects, more context on expected results would improve completeness.

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

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

Schema covers 100% of parameters with detailed descriptions. Description adds value by summarizing free/paid tier constraints on positions and custom shocks, complementing the schema's detailed parameter docs.

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

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

Description clearly states the tool stress tests a portfolio and lists specific historical crises and custom shocks, differentiating it from sibling tools like monte_carlo_simulation or analyze_risk.

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

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

Description implies usage for stress testing against crises but does not explicitly guide when to use vs alternatives like analyze_risk or optimize_portfolio; tier constraints are mentioned in schema but not in description.

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