QuantOracle

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds 'Deterministic' (consistent with idempotency) and enumerates return fields (total return, Sharpe, etc.). Also mentions 'PAID ONLY' as a behavioral constraint. No contradictions; adds useful context.

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

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

The description is concise at 4 sentences, front-loaded with purpose, then strategies, usage, and returns. Every sentence adds value with no redundancy. It efficiently packs all essential information.

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

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

Given the complexity (6 params, 1 nested, no output schema), the description fully covers the tool: explains strategies, parameter shapes, and return fields including equity curve and vs buy-and-hold. Also notes it's paid. No gaps for agent to select and invoke correctly.

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% (all 6 parameters described). The description reinforces the params object structure per strategy, but the schema already includes example structures. Since schema covers the parameter meanings, the description adds minimal new semantics beyond usage context.

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

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

The description clearly states the tool performs deterministic backtests of four specific strategies (SMA crossover, RSI mean reversion, momentum, Bollinger breakout). It distinguishes itself by noting it replaces 10+ individual calls, making the purpose precise even among many financial siblings.

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 says 'Use when backtesting a trading strategy on price history' and lists the supported strategies, providing clear context. It lacks explicit exclusions or alternatives, but the paid restriction is noted. Given rich sibling list, some guidance on when not to use would improve, but it's still clear.

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?

Annotations provide readOnlyHint, idempotentHint, destructiveHint. The description adds behavioral details: max 100 per batch, pricing as sum of individual endpoint prices, throughput improvement. No contradiction with annotations.

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

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

The description is extremely concise, front-loaded with purpose, and every sentence adds value. It efficiently conveys usage, constraints, and benefits without redundancy.

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

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

Given the tool's complexity (batching multiple computations), the description covers purpose, usage, constraints (max 100), pricing, and ideal scenarios. No output schema, but the description adequately explains return value implicitly.

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% (single parameter 'requests'). The description adds semantic value by explaining the structure ('array of {endpoint, params} objects') and endpoint format ('e.g., 'options/price''), which goes beyond the schema's description.

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 'Execute multiple computations in a single request,' specifying the verb (execute) and resource (multiple computations). It distinguishes from siblings which are individual endpoint tools like 'options_price' or 'risk_portfolio'.

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

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

The description explicitly says 'Use when you need to execute multiple computations efficiently' and provides context like 'bundle up to 100 calls for ~6x throughput improvement' and ideal use cases such as 'backtests, parameter sweeps, and portfolio-wide calculations.' It lacks explicit when-not-to-use but is still clear.

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?

Annotations indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds that the tool returns equivalent APY, APR, daily/weekly/monthly rates, and effective annual rate, which is valuable context beyond the annotations. No contradictions with annotations.

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

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

The description is two sentences: the first states the purpose, the second provides usage guidance and output summary. Every sentence contributes meaning without redundancy, achieving maximum conciseness.

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

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

Given the absence of an output schema, the description lists the returned metrics (equivalent APY, APR, daily/weekly/monthly rates, effective annual rate). All three parameters are fully documented in the schema, making the description complete for agent understanding.

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 input schema already describes all three parameters, including defaults and allowed values. The description does not add significant new information about parameters, hence a 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 'Convert between APY and APR with configurable compounding frequency,' specifying the verb (convert) and resource (APY/APR). This differentiates it from sibling tools which cover other financial calculations.

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

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

The description explicitly says 'Use when converting between APY and APR with different compounding frequencies,' providing clear context. It does not mention when not to use or alternatives, but for a straightforward conversion tool this is adequate.

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

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

The description discloses that the tool returns effective price, price impact percentage, and output amount after slippage. Annotations already indicate read-only and idempotent behavior, and the description adds no contradictions. It could mention that the estimate is based on the constant-product formula and does not account for real-time market effects.

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

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

The description is three sentences: the first defines the tool, the second gives usage guidance, and the third lists outputs. It is concise, front-loaded, and contains no extraneous information.

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

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

Given the tool's simplicity (5 parameters, no output schema), the description adequately covers inputs and outputs. It could be improved by clarifying that results are estimates based on the constant-product formula, but it is still fairly complete.

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

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

The input schema covers 100% of parameters with descriptions. The description adds context like 'trade size and pool reserves' and the return values, which slightly supplements the schema. However, the benefit is minimal given the schema already describes each parameter.

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

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

The description clearly states it is a DEX slippage estimator for constant-product AMM (x*y=k), with a specific verb and resource. It is distinct from sibling tools like crypto_impermanent-loss or risk_transaction-cost.

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 advises using it when estimating slippage on a DEX trade with constant-product AMM math, which provides clear context. It does not explicitly exclude non-constant-product AMMs or mention alternatives, but the usage is well-defined.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds return value expectations (annualized cost, projected payments, carry trade estimate) beyond annotations, but could mention that no trades are executed or data limitations.

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 three sentences: purpose, usage scenario, and outputs. It is front-loaded and contains no unnecessary words, 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.

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

The description conveys main outputs but lacks detail on 'regime detection' and does not explain how results are presented. With no output schema, more specifics on structure or edge cases 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 coverage is 100%, so baseline is 3. The description adds context by mentioning 'holding period' (not a direct parameter but implied) and links inputs to outputs, slightly enhancing understanding beyond the schema.

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

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

The description clearly states it performs 'funding rate analysis with annualization and regime detection,' a specific verb+resource. It distinguishes from siblings by specifying 'perpetual futures funding rates' and listing outputs not found in other crypto tools.

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

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

The description explicitly says 'Use when analyzing perpetual futures funding rates,' providing clear usage context. However, it does not mention when not to use it or name alternative tools for related tasks like swap comparisons.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. Description adds output details (percentage, hold vs LP value, breakeven ratios) that go beyond annotations, improving transparency. No contradictions.

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

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

Two highly focused sentences with no wasted words. Front-loaded with core purpose, then usage and output summary. Every sentence 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?

Description lists three return values, which is good for a no-output-schema tool. However, it oversimplifies v3 usage by not mentioning lower/upper tick parameters explicitly. Could be more complete on parameter-specific constraints.

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. Description adds context by specifying 'provide initial prices and current prices' and the v2/v3 distinction, which maps to parameters. Adds moderate value beyond schema.

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

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

Description clearly states it is an impermanent loss calculator for Uniswap v2/v3, distinguishing it from siblings like backtest_strategy or crypto_dex-slippage. Verb+resource is specific.

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

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

Explicitly says 'Use when calculating impermanent loss for a liquidity provider position', providing clear context. Does not list alternatives or when-not-to-use, but the scope is well-defined and distinct from other tools.

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

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

Annotations already provide readOnlyHint and idempotentHint. The description adds context by listing the return values (liquidation price, distance to liquidation, margin call price). It does not contradict annotations.

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

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

The description is concise with three sentences, front-loading the purpose. It could be more structured (e.g., bullet points for returns) but is still efficient and clear.

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

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

Given the tool has no output schema, the description compensates by specifying return values. It covers the main inputs and outputs, but could mention data sources or assumptions for 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 coverage is 100%, so baseline is 3. The description only mentions a subset of parameters (entry price, leverage, position side, maintenance margin) and does not add significant meaning beyond the schema descriptions.

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

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

The description clearly states it is a 'liquidation price calculator for leveraged positions' and specifies the inputs and outputs. It is specific and distinct from sibling tools, as no other liquidation price calculator exists.

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

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

The description explicitly says 'Use when computing the liquidation price for a leveraged position,' providing clear context. It does not mention when not to use or alternatives, but given the tool's unique purpose, this is sufficient.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds behavioral context by specifying the return values (rebalancing needed, drift per asset, trade list), which exceeds annotation coverage and helps the agent understand output.

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 two sentences, front-loaded with purpose, and contains no redundant information. Every sentence earns its place, making it concise and well-structured.

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

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

Given the tool's simplicity (3 parameters, no output schema), the description covers purpose, usage, parameter semantics, and return values adequately. It is complete for an agent to invoke correctly.

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

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

With 100% schema coverage, baseline is 3. Description adds value by explaining the output structure and connecting parameters (target weights, current weights) to the function, enabling the agent to anticipate results beyond the schema.

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

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

The description clearly states the tool performs 'drift detection and trade computation' for portfolio rebalancing, distinguishing it from siblings like portfolio_rebalance-plan which likely generates a full rebalance plan. The verb+noun combination is specific and informative.

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

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

The description explicitly says 'Use when checking if a crypto portfolio needs rebalancing,' providing clear context. However, it does not mention when not to use or explicitly compare to alternatives, but the usage scenario is well-defined.

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?

Annotations indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false, and the description adds that it returns a period-by-period unlock schedule with cumulative totals, which is consistent and adds value beyond the annotations.

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

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

Two concise sentences with front-loaded purpose and usage guidance. No redundant information, every sentence serves a purpose.

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

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

Given the tool has no output schema, the description adequately states it returns a period-by-period unlock schedule with cumulative totals. Combined with schema descriptions for parameters, the tool is fully specified for its simple computational role.

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 parameter descriptions. The overall description summarizes key inputs (total tokens, cliff period, vesting duration, TGE unlock percentage) but does not add significant detail beyond the schema. Baseline 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 it computes a token vesting schedule with cliff, linear/graded unlock, and TGE, distinguishing it from all sibling tools which cover other financial calculations.

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

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

The description explicitly says 'Use when computing a token vesting schedule with cliff and linear vesting', providing clear context for when to invoke. It does not mention exclusions, but the specific domain is sufficiently narrow.

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?

Annotations already indicate readOnlyHint=true and destructiveHint=false. Description adds that it returns 'option price via geometric closed-form or Turnbull-Wakeman approximation', which is valuable behavioral detail beyond annotations.

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

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

Two sentences plus a list of required inputs. No wasted words; front-loaded with purpose and method. Highly efficient.

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

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

The description covers the tool's purpose, methods, required inputs, and return value, which is sufficient for a pricing tool with well-documented schema. No output schema exists, but the return is clearly implied.

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 parameters are fully described. The description lists key parameters (spot, strike, time, rate, volatility, averaging type) but does not add new semantics beyond what the schema already provides.

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

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

Description clearly states 'Asian option pricing' with two specific methods (geometric closed-form or Turnbull-Wakeman approximation), distinguishing it from siblings like barrier options or standard options pricing.

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

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

Explicitly says 'Use when pricing Asian (average-price) options', providing clear context for when to invoke this tool, though it does not explicitly mention when not to use it or name alternatives.

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

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

Annotations already indicate readOnlyHint=true and idempotentHint=true. The description adds that it uses analytical formulas and returns price plus adjustment factors. This adds some behavioral context but does not delve into assumptions, limitations, or error conditions. Given annotations cover safety, a score of 3 is appropriate.

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 three sentences with no wasted words. It front-loads purpose, then usage guidance, then return values. Every sentence 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?

For a computational tool without output schema, the description lists return values (price, vanilla equivalent, adjustment factors). It covers inputs and outputs adequately for an agent to understand usage. Could be improved by noting the analytical formulas' assumptions, but not necessary for minimal 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%, so baseline is 3. The description mentions 'spot, strike, barrier level, barrier type, and standard option parameters,' which maps to parameters but adds no new detail beyond the schema.

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

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

The tool explicitly states it is for 'pricing barrier options using analytical formulas,' specifying the verb and resource clearly. It distinguishes itself from siblings like 'derivatives_asian-option' or 'derivatives_lookback-option' by focusing on barrier options.

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 advises 'Use when pricing knock-in or knock-out barrier options,' providing clear context. It does not explicitly list when not to use or suggest alternatives, but the specificity of barrier options compared to sibling tools is sufficient.

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?

Annotations already indicate readOnlyHint=true and idempotentHint=true. The description adds behavioral context by stating the outputs ('Returns: option price, early exercise boundary, and tree node values'), which is not in the annotations. No contradictions with annotations.

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

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

Two sentences with no filler. First sentence states purpose, second sentence provides usage and outputs. Front-loaded and every sentence 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?

Despite having no output schema, the description mentions the return values (price, early exercise boundary, tree node values). For a tool with 9 parameters and no nested objects, this is sufficient to use the tool without additional context. However, it could mention edge cases or limitations.

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 baseline is 3. The description merely lists parameter names ('Provide spot, strike, time, rate, volatility, steps, and exercise style'), which adds no new meaning beyond the schema's existing descriptions.

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

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

The description explicitly states the tool does 'CRR binomial tree pricing for American and European options.' It uses a specific verb ('pricing') and resource ('American and European options'), and distinguishes itself from sibling tools like 'derivatives_asian-option' by naming the model (CRR) and option types.

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

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

The description provides clear when-to-use guidance: 'Use when pricing American or European options via the CRR binomial lattice.' It lists required inputs. However, it does not explicitly state when not to use this tool or suggest alternatives, which would have made it a 5.

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

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

Annotations already provide readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds the pricing formula name but does not elaborate on other behavioral aspects like no side effects, auth needs, or rate limits. With good annotation coverage, a 3 is appropriate.

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?

Three sentences with no wasted words. The first sentence acts as a clear title, the second provides usage guidance, and the third summarizes parameters and output. Efficient and front-loaded.

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

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

Lacks detail on output format (e.g., single number vs object) and does not explain floating vs fixed strike usage nuances. With 10 parameters and no output schema, more context would be helpful for correct usage.

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

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

Schema description coverage is 100%, so the schema fully documents parameters. The description lists some parameter categories but does not add meaning beyond what is already in the schema descriptions. Baseline 3 per rules.

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 prices lookback options (floating/fixed strike) using Goldman-Sosin-Gatto formulas. The name and title reinforce this, and it distinguishes from sibling tools like derivatives_asian-option and derivatives_barrier-option.

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

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

Explicitly says 'Use when pricing lookback options', which is clear. However, it does not mention when not to use it or provide alternatives among the many derivative pricing siblings, missing an opportunity to guide selection.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds context on input requirements (arrays of strikes, calls, puts, open interest) and outputs, complementing annotations without contradiction.

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?

Three concise, front-loaded sentences cover purpose, usage, and data expectations without redundancy. Every sentence adds value.

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

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

Given no output schema, the description lists all return values. It explains input structure adequately. Minor gaps: output format (e.g., skew metrics structure) not specified, but overall sufficient for agent invocation.

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

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

Schema coverage is 100%, so parameters are well-documented. The description summarizes inputs as 'strikes, calls, puts, and open interest' but adds no new semantic depth beyond the schema.

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

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

The description clearly states the tool analyzes option chains for skew, max pain, and put-call ratios, listing specific outputs. It distinguishes from sibling tools like 'options_implied-vol' by offering a comprehensive analytics suite.

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?

Includes explicit direction: 'Use when analyzing an options chain for skew, max pain, and put-call ratios.' It provides clear context but does not explicitly exclude other uses or name alternatives, leaving room for improvement.

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?

Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds value by explaining the output: 'Returns: parity check, theoretical values, and any arbitrage amount.' This behavioral context goes beyond annotations.

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

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

The description is two sentences: the first states the tool's purpose, and the second provides usage instructions and expected outputs. Every sentence is necessary, with no fluff or redundancy.

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

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

Given 7 parameters (5 required) and no output schema, the description adequately explains what the tool does and returns. It could mention default values for optional parameters, but the schema already handles that. Overall, it is sufficiently complete for a mathematical tool.

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

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

Schema description coverage is 100%, so the baseline is 3. The description lists key inputs ('call price, put price, spot, strike, rate, and time') but omits the optional 'q' (dividend yield). It doesn't add significant meaning beyond the schema, earning a middle score.

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

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

The description clearly states the tool's purpose: 'Put-call parity check and arbitrage detection.' This is a specific verb-resource combination that distinguishes it from sibling derivatives tools like options_price or options_implied-vol, which serve different purposes.

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

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

The description explicitly says 'Use when checking put-call parity or detecting arbitrage opportunities,' providing clear guidance on when to use it. However, it does not discuss alternatives or when not to use it, which would be helpful but is not critical given the tool's niche.

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?

Annotations already indicate readOnly and idempotent. Description adds that it returns interpolated surface, skew, term structure, smile parameters, which gives the agent insight into the output. No description of side effects or prerequisites beyond the 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?

Extremely concise: two sentences plus a bullet-like list of returns. Every sentence adds value. No redundancy.

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

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

For a tool without output schema, the description adequately lists the main outputs. It covers inputs and purpose. However, it lacks edge cases or guidance on data quality. Given the tool's moderate complexity, it is mostly complete.

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

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

Schema coverage is 100%, so parameters are documented. Description summarizes that inputs are arrays of strikes, expiries, and IV values, which aligns with the schema. It adds context by linking parameters to the overall goal, but does not add new details beyond the schema.

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

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

Clearly states it builds an implied volatility surface from market data. Uses specific verb 'Build' and identifies the resource. Distinguishes from siblings like options_implied-vol by mentioning surface, skew, term structure, smile.

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

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

Provides explicit usage context: 'Use when constructing an implied volatility surface from market data.' However, no mention of alternatives or when not to use this tool compared to siblings like options_implied-vol or derivatives_option-chain-analysis.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. Description adds return values (Z-spread, OAS, default probability, LGD) but no additional behavioral traits beyond annotations. No contradiction.

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: first defines purpose, second provides usage guidance and outputs. No wasted words, important info front-loaded.

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

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

Covers inputs and outputs sufficiently given no output schema. Mentions all key return values. Missing a note about prerequisites (e.g., need market data) but schema covers input format.

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

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

Schema description coverage is 100%, so each parameter already has a clear description. The tool description merely lists required params without adding new meaning beyond the schema.

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

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

Explicitly states it computes credit spread and Z-spread from bond price vs risk-free curve, and specifies the use case for corporate bond default probability. Distinguishes from sibling fixed-income tools like 'fixed-income_bond' and 'fi_yield-curve-interpolate'.

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

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

Provides clear context: 'Use when computing Z-spread and implied default probability... Provide bond price, coupon, maturity, and risk-free curve.' Lacks explicit exclusions or alternatives, but context is sufficient.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds that it returns specific outputs like monthly payment and schedule, which is consistent and non-contradictory. No additional behavioral traits needed.

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 two short paragraphs, front-loaded with the title-like phrase. Every sentence adds value: purpose, usage, input, output. No fluff.

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

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

With 4 parameters, no output schema, and a simple calculation tool, the description sufficiently covers what it does (schedule with extra payments), inputs (principal, rate, term, extra payment), and outputs (payment, interest, schedule). No 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 coverage is 100% with good parameter descriptions. The description adds meaning by mentioning 'extra payment savings analysis' and listing return fields, which helps the agent understand the tool's purpose beyond the schema.

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

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

The description clearly states it produces a 'full amortization schedule with extra payment savings analysis' and explicitly says to use it when generating a loan amortization schedule. It differentiates from siblings by specifying amortization, a distinct niche among financial tools.

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

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

The description provides explicit usage guidance: 'Use when generating a loan amortization schedule.' It does not explicitly mention alternatives or when not to use, but the context is clear for this specialized tool.

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

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

The description adds value beyond annotations by listing specific return values (price, duration, convexity, accrued interest). Annotations already declare readOnlyHint and idempotentHint, and the description aligns with no side effects claimed.

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 two sentences plus a list of outputs—no wasted words. Key information is front-loaded: purpose, usage, and output summary.

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 outputs and hints at input flexibility ('yield or price'), it misaligns with the schema (which only has ytm). This inconsistency reduces completeness. No output schema exists, so description should be precise; it is not fully accurate.

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 the schema already documents parameters. The description only slightly adds context by mentioning 'yield or price', but this is inconsistent with the schema (which has no price parameter). No extra semantic depth beyond schema.

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

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

The description clearly states the tool computes bond price, duration, convexity, and DV01, with explicit outputs listed. It distinguishes itself from siblings like fixed-income_amortization and fi_yield-curve-interpolate.

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

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

The description provides clear usage context: 'Use when pricing a bond or computing yield, duration, and convexity.' It lists required inputs but does not explicitly exclude alternatives or mention 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, covering safety and idempotency. The description adds only that it returns interpolated yields, which is expected. No contradictions.

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

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

Three sentences, no wasted words. Purpose is front-loaded. Every sentence adds value.

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

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

The description covers input requirements and output expectations. It lacks mention of ordering constraints for tenors, but given the tool's simplicity, it is nearly complete.

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

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

Schema description coverage is 100%, so the schema already explains each parameter. The description reinforces 'observed maturities and yields, plus query maturities' but adds no new meaning beyond what the schema provides.

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

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

The description states 'Yield curve interpolation' with specific methods, clearly distinguishing the tool's verb and resource. No sibling tool has the same purpose, making it unique.

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

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

The description explicitly says 'Use when interpolating a yield curve at arbitrary maturities,' providing clear usage context. It does not include when-not or alternatives, but the specificity is sufficient.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds value by detailing the outputs (carry return, spot return, total return, annualized P&L breakdown) and confirming it is a calculation tool. No contradictions with annotations.

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

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

The description is two sentences long, front-loaded with the purpose, followed by usage guidance and input/output summary. Every sentence is informative and non-redundant, making it efficient for an agent to parse.

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

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

Given the tool's moderate complexity (7 parameters, no output schema), the description provides sufficient context: purpose, inputs, and outputs. It does not specify formulas or edge cases, but the schema covers parameter units, and the tool is a standard financial calculation, so the description is adequately complete.

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

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

Schema description coverage is 100%, providing basic descriptions for all 7 parameters. The description adds context by mapping parameters to carry trade concepts ('high-yield and low-yield rates, entry spot rate, exit spot rate'), enhancing understanding beyond the schema alone.

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 performs 'Currency carry trade P&L decomposition', a specific verb-resource combination. It distinguishes itself from sibling tools (all other FX tools have different purposes) and explicitly lists inputs and outputs, leaving no ambiguity about its function.

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

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

The description includes 'Use when analyzing carry trade P&L decomposition', providing explicit guidance on when to invoke this tool. Although it does not mention alternatives or when not to use, the sibling tools are all distinct, so no exclusion is necessary.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by stating the output (implied forward rates) and the process (bootstrapping), which goes beyond the annotations. No contradictions.

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

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

The description is extremely concise with three short sentences, each serving a distinct purpose: stating the function, providing usage guidance, and describing the output. No unnecessary words, and the most important information is front-loaded.

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

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

The description covers the basic purpose and output but lacks details about the bootstrapping method (e.g., interpolation), constraints (e.g., forward_start < forward_end), or edge cases. Given the tool's moderate complexity (4 parameters, no output schema), more information 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 description coverage is 100%, so the baseline is 3. The description does not add new parameter-level information beyond what the schema already provides; it only summarizes the overall goal. Thus, it meets but does not exceed 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 states the tool bootstraps forward rates from a spot yield curve, specifying the verb (bootstrap), resource (forward rates), and input (spot yield curve). It also mentions the output (implied forward rates between tenor pairs), making the purpose unambiguous.

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

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

The description explicitly states 'Use when bootstrapping forward rates from a yield curve,' providing clear context for when to use this tool. However, it does not mention when not to use it or provide alternatives, which is a minor gap given the many sibling tools.

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

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

Annotations already indicate readOnlyHint and idempotentHint, and the description adds return value details (forward rate, parity-implied rate, arbitrage) without contradicting annotations.

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

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

Two sentences, front-loaded with purpose and immediate value, no redundant information.

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 full schema coverage and annotations, the description provides complete context: what, when, inputs, and outputs.

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

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

Schema has 100% coverage with parameter descriptions; the description lists required parameters but adds no extra meaning beyond the schema.

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

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

The description clearly states it is an interest rate parity calculator with arbitrage detection for FX pairs, distinguishing it from sibling tools like fx_forward-rate or fx_carry-trade.

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

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

It specifies when to use (computing covered/uncovered interest rate parity) and lists required inputs, but does not explicitly mention when not to use or compare to alternatives.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true, indicating safe read operation. The description adds that the tool returns fair value and over/undervaluation percentage. No contradictions. Could mention assumptions (e.g., relative PPP), but adequate given annotations.

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

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

Two sentences with no fluff. First sentence provides a clear title-like purpose, followed by usage instructions and output. Perfectly front-loaded and concise.

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

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

Given annotations and schema, the description covers what the tool does, required inputs, and output. No output schema, but return values are described. Could be more detailed about assumptions (e.g., using relative PPP), but sufficient for an agent to use correctly.

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 all four parameters. The description paraphrases some inputs but adds no significant new meaning beyond what the schema provides. Baseline 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 estimates fair value using PPP, with a specific verb ('estimation') and resource ('FX rate using PPP'). It distinguishes from sibling tools like fx_forward-rate or fx_carry-trade by highlighting the PPP methodology, though no explicit comparison is made.

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

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

Explicitly states when to use ('when estimating fair value of an FX rate using PPP') and what inputs to provide (domestic/foreign price indices, base-period exchange rate). Does not mention when not to use or alternatives, but the context is clear.

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?

Annotations already set readOnlyHint=true and idempotentHint=true. Description adds critical behavioral info 'PAID ONLY — no free tier' and mentions return structure, without contradicting annotations.

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

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

Two paragraphs with front-loaded purpose and succinct usage instructions. No filler or redundancy.

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

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

Given rich schema, clear annotations, and no output schema, the description adequately explains purpose, usage, required inputs, and paid status, making it complete for an agent.

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

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

Schema coverage is 100%. Description restates parameter names and types but does not add new semantics beyond what the schema's descriptions already provide.

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 starts with 'Rank cheapest effective hedges for a given position' and lists specific hedge types, clearly distinguishing this tool from sibling tools like options_strategy or risk_position-size.

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

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

Explicitly states 'Use when agents need to hedge an existing position' and lists required parameters, providing clear context for when to invoke the tool.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds value by specifying the return outputs: current ATR, normalized ATR as % of price, history, and volatility regime. No contradictions.

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

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

Description is only 3 sentences covering purpose, usage, and outputs. No filler, front-loaded with key information, and every sentence adds value.

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

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

Given no output schema, description lists the return values (ATR, normalized ATR, history, regime). It is complete enough for a volatility indicator tool. Minor gap: no mention of input length requirements or edge cases, but sufficient.

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

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

Schema description coverage is 100%, so the input schema already documents all parameters (high, low, close, period). The description only says 'Provide an array of prices,' which adds no new meaning. Baseline 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?

Description clearly states it computes Average True Range (ATR) with normalized ATR and volatility regime classification. Specific verb+resource, and no sibling tool duplicates this functionality, so it is well-differentiated.

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

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

Explicitly says 'Use when measuring volatility via Average True Range,' providing a clear context for use. However, no mention of when not to use or alternatives (e.g., other volatility indicators), so not a full 5.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. Description adds return details (bands, %B, bandwidth, squeeze flag, position) that enrich behavioral understanding without contradiction.

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: first defines what it is, second gives usage and outputs. No redundancy, highly efficient.

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

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

Given the straightforward computation tool, the description covers purpose, usage, and outputs. No output schema exists, but return fields are listed, making it complete.

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

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

Schema covers 100% of parameters with complete descriptions. Description only mentions 'period/multiplier' corresponding to window and num_std, adding no new meaning beyond the schema defaults.

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

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

The description clearly states the tool computes Bollinger Bands with %B, bandwidth, and squeeze detection. It specifies the verb 'compute' in the usage guideline and includes a list of return values, distinguishing it from sibling tools like indicators_atr.

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

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

Explicitly says 'Use when computing Bollinger Bands with squeeze detection.' Provides clear context but does not mention when not to use or alternatives.

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

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

Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds value by detailing the return objects: current MA values, crossover signals, history, and signal strength.

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 two concise sentences. Each sentence serves a purpose: stating the tool's function and providing usage and return information. No wasted words.

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

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

Given the tool's complexity (3 parameters, no output schema), the description sufficiently covers purpose, usage, inputs, and outputs. Annotations and schema are comprehensive.

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

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

Schema coverage is 100% with clear descriptions for all parameters. The description says 'Provide prices and two MA periods' but does not add new details beyond the schema.

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

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

The description states 'Golden/death cross detection with signal history', which is a specific verb+resource. It distinguishes from sibling indicator tools by focusing on moving average crossovers.

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

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

The description explicitly says 'Use when detecting moving average crossovers', providing clear context. However, it does not mention when not to use or alternative tools.

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

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

Annotations already declare readOnlyHint=true, idlempotentHint=true, destructiveHint=false. The description adds value by listing the exact retracement and extension levels returned, which is behavioral detail beyond the annotations. No contradictions.

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

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

The description is extremely concise: two sentences plus a list of levels. It is front-loaded with the purpose and has no fluff. Every sentence 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?

Despite no output schema, the description enumerates the exact retracement and extension levels returned. For a simple calculation tool with well-known inputs, this provides sufficient context for an agent to use the tool correctly.

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%: all three parameters (swing_high, swing_low, direction) are described in the schema. The description's mention of 'Provide a high price and low price' adds no new semantics beyond the schema, 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?

Description clearly states it computes Fibonacci retracement and extension levels, specifies exact levels returned, and identifies required inputs (high and low prices). It distinguishes itself from sibling tools like indicators_bollinger-bands or indicators_atr by being specific to Fibonacci calculations.

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

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

Explicitly says 'Use when computing Fibonacci retracement and extension levels.' It does not provide exclusions or alternatives, but the context is clear enough for an agent to decide when to invoke this tool versus other technical indicators.

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?

Annotations already indicate readOnly, idempotent, and non-destructive. The description adds value by detailing the return categories (trend regime, volatility regime, change signals), enhancing transparency beyond annotations.

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

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

The description is two sentences with no wasted words. It front-loads the purpose and usage, then lists outputs. Every sentence adds value.

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

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

For a classification tool with 3 well-documented parameters, the description covers purpose, usage, and outputs. It lacks details on algorithm assumptions but is adequate for agent selection.

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%, baseline 3. The description mentions 'Provide a price series' linking to the 'prices' parameter, but does not explain 'sma_period' or 'vol_window' beyond what the schema already provides.

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

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

The description explicitly states the tool classifies market regime (trend, volatility, risk) and lists specific outputs. It uses a specific verb ('classify') and resource ('market regime'), distinguishing it from sibling tools like 'indicators_crossover' or 'indicators_technical'.

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 says 'Use when classifying market regime (trending vs ranging, high vs low volatility)', providing clear context. However, it does not mention when not to use or explicitly name alternatives, though sibling tools imply other uses.

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?

Annotations already indicate readOnlyHint=true and idempotentHint=true, so the tool is safe and idempotent. The description adds behavioral context: it combines multiple indicators, requires at least 30 closing prices, returns specific fields (trend direction, volatility regime, RSI, SMA, strategy suggestion), and is paid-only. This goes beyond the annotations.

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

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

The description is concise: two short paragraphs. The first line acts as a title summarizing the tool's output. Every sentence adds value—purpose, usage context, requirements, returns, and access restriction. No superfluous content.

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

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

Given the tool has 7 parameters and no output schema, the description adequately covers the goal, inputs (min 30 closes), and outputs (trend, vol, RSI, SMA, strategy suggestion). It does not detail return formats or optional parameter usage, but the schema covers parameter descriptions, so completeness is high.

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% (all 7 parameters have descriptions). The description only mentions 'closing prices (min 30)' which is already in the schema. No additional parameter semantics are added beyond the schema, so it meets 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 states the tool's purpose: 'Combined regime classification: trend, vol, RSI, direction, strategy suggestion.' It explicitly distinguishes itself from sibling tools by saying it 'Replaces technical + regime + realized-vol,' indicating it consolidates multiple separate calls into one.

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

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

The description specifies when to use this tool: 'Use when you need a complete market regime assessment... Instead of calling technical + regime + realized-vol separately.' It also provides a prerequisite ('Provide closing prices (min 30)') and notes that it's 'PAID ONLY — no free tier,' guiding the agent on appropriate 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, signaling safe, side-effect-free behavior. The description adds that it returns composite signals and trend classification but does not disclose any additional behavioral traits beyond what annotations provide.

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-load the purpose and usage. Every word earns its place—no redundancy or filler. Efficient and clear.

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

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

Without an output schema, the description compensates by listing the computed indicators and signals. Parameters are fully covered. It lacks edge-case info (e.g., NaN handling, minimum price length beyond minItems=5) but is largely complete for a tool of this complexity.

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

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

Schema coverage is 100%, so the schema already documents all three parameters well. The description adds 'Provide an array of prices and optional volumes' but this largely echoes the schema. It does not clarify the period's effect or data constraints beyond schema.

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

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

The description clearly states the tool computes '13 technical indicators + composite signals' from price series and lists the specific indicators. It distinguishes itself from siblings like indicators_crossover and indicators_regime by offering a broad set of indicators in one call.

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

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

Explicitly says 'Use when you need multiple technical indicators computed from a price series.' This provides clear context for when to use, but does not include exclusions or comparisons to siblings, which would elevate it further.

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?

While annotations already indicate safe read-only operation, description adds that it uses Fisher equation and outputs real return, purchasing power change, and cumulative real growth, which is helpful beyond annotations.

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

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

Three concise sentences: action, usage with required inputs, and outputs. No redundant or vague language.

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 outputs and formula but lacks explicit return structure. However, given no output schema, it is adequate and useful.

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

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

Schema coverage is 100% with good descriptions; description adds context that 'periods' and 'initial_value' are for cumulative calculation, enhancing understanding.

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 converts nominal returns to real returns using Fisher equation, differentiating from siblings like macro_real-yield by specifying the formula and outputs.

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

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

Explicitly says 'Use when converting nominal returns to real returns using the Fisher equation' but does not provide when-not-to-use or alternative tools.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds output details (returns real yield, breakeven inflation, risk premium) and parameter trade-offs, providing useful behavioral context beyond annotations.

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

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

Three concise sentences: purpose, usage, outputs. No fluff, all sentences are essential. Front-loaded with the core purpose.

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

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

Given the tool's simplicity (4 params, no output schema, strong annotations), the description adequately covers purpose, inputs, and outputs. It could optionally explain the Fisher equation relationship but is complete enough for agent use.

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

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

Schema coverage is 100% with parameter descriptions. The description adds value by clarifying the alternative relationship between inflation_expectation and tips_yield, and confirms the default tenor_years. This goes beyond the schema.

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

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

The description explicitly states 'Real yield and breakeven inflation from nominal yields' and further clarifies it computes real yield, breakeven inflation, and inflation risk premium. It clearly identifies the tool's function but does not differentiate from sibling tools like macro_inflation-adjusted.

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

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

The description provides clear usage context: 'Use when computing real yield and breakeven inflation' and specifies input alternatives (nominal yield + inflation expectation or TIPS yield). It lacks explicit when-not-to-use or alternative tool mentions.

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?

Annotations already indicate read-only, non-destructive, idempotent behavior. The description adds that the tool returns two outputs (implied rate and deviation), which is useful. No behavioral traits beyond annotations are disclosed, but the description aligns well.

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 at three sentences, front-loading the purpose and usage, then stating return values. No superfluous text; every sentence 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 tool is a simple calculation with no output schema, the description covers purpose, when-to-use, and return values adequately. It could mention the formula or assumptions, but the schema fills in parameter details, making it reasonably complete.

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

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

Schema coverage is 100% with descriptions for all 7 parameters. The description reinforces that certain parameters (inflation, target inflation, output gap, neutral rate) are key, but does not add significant new meaning beyond the schema. Baseline 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 computes the Taylor Rule interest rate prescription, specifying the verb 'estimating' and the resource 'appropriate policy interest rate'. It effectively distinguishes itself from sibling tools by its unique focus on a specific monetary policy rule.

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

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

Explicitly states 'Use when estimating the appropriate policy interest rate via the Taylor Rule', providing clear guidance on when to invoke the tool. Though alternatives are not mentioned, the specificity makes misuse 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?

The description provides behavioral details beyond annotations: it specifies the Newton-Raphson iteration method, convergence in 5-8 iterations, and the return values (implied volatility, convergence info, Greeks). Annotations only indicate read-only and idempotent hints; the description adds significant operational context.

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

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

The description is extremely concise: three sentences with no wasted words. It front-loads the tool's identity ('Newton-Raphson implied volatility solver') and then efficiently covers when to use, inputs, and outputs.

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 adequately covers return values (implied volatility, convergence info, Greeks). It could be improved by mentioning error conditions or precision, but it is sufficiently complete for a finance solver tool.

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

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

The input schema has 100% description coverage, so the baseline is 3. The description lists the key parameters (spot, strike, time, etc.) but does not add new semantic meaning beyond the schema. It summarizes rather than enhances parameter understanding.

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

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

The description clearly states it is a 'Newton-Raphson implied volatility solver' that backs out implied volatility from market price. This purpose is distinct from sibling tools like options_price, which likely compute forward prices. The verb 'solver' and resource 'implied volatility' are specific and unambiguous.

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

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

The description explicitly states when to use the tool: 'Use when you know the market price of an option and need to back out the implied volatility.' This provides clear context, though it does not explicitly mention alternatives or when not to use it.

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

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the tool is safe. The description adds that it returns an array of price/payoff pairs and key metrics, providing useful behavioral context.

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

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

The description is concise with two sentences, front-loading the primary purpose and usage. No unnecessary words or repetition.

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

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

Given the complexity of multi-leg options and no output schema, the description only briefly mentions the return format (array of pairs) and metrics (breakevens, max profit/loss). It could benefit from specifying the exact structure of the return array.

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 limited value beyond summarizing that legs require strike, premium, quantity, and type. It does not provide additional constraints or examples.

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

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

The description clearly states that the tool generates data points for plotting options payoff diagrams. The verb "generate" and resource "payoff diagram data" are specific, and it distinguishes itself from sibling tools like options_price or options_strategy.

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

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

The description provides a clear usage scenario: "Use when you need payoff/P&L data points for plotting an options strategy." While it does not explicitly mention when not to use or list alternatives, the context of siblings makes the purpose clear.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds that it returns price, 10 Greeks, and breakdown, which is useful but not essential beyond annotations.

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

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

The description is two sentences: a headline summarizing the tool, then a clear usage and returns statement. Every word is meaningful and front-loaded.

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

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

No output schema exists, so the description compensates by listing return values (price, 10 Greeks, intrinsic/time value breakdown). This is sufficient for typical use, though edge cases are not addressed.

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 all parameters have descriptions. The description mentions some parameters (spot, strike, time, rate, volatility) but adds no new meaning beyond what the schema provides. Baseline 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 it performs Black-Scholes pricing with 10 Greeks, listing specific return values. This directly differentiates it from sibling tools like options_implied-vol (implied volatility) and options_strategy (multi-leg strategies).

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

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

Explicitly says 'Use when you need to price a European option or compute Greeks' and lists required inputs. It does not explicitly state when not to use or name alternatives, but the context of sibling tools provides implicit differentiation.

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?

Annotations provide readOnlyHint, idempotentHint, destructiveHint. Description adds that it returns ranked candidates with risk/reward ratios, breakevens, and full Greeks, and notes it's paid only. No contradictions.

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

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

Three sentences, front-loaded with main purpose, then usage guidelines, then return details and cost note. 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?

Despite no output schema, description explains return content (ranked candidates with risk/reward, breakevens, Greeks). Covers required parameters and usage context. Sufficient for agent.

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

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

Schema description coverage is 88%. Description mentions spot, vol, DTE, and strategy, which matches schema. It does not add significant new semantics beyond what's in the schema.

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

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

The description clearly states it scans and ranks vertical spreads by risk/reward, and it replaces multiple individual options/price calls. This distinguishes it from siblings like options_price, options_strategy, etc.

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

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

Explicitly says when to use (evaluating multiple vertical spread candidates) and notes it replaces 8-16 calls. Also mentions paid tier constraint.

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?

Annotations already indicate read-only, idempotent, non-destructive. Description adds the return information (net premium, max profit/loss, P&L data), which is critical since there is no output schema. No contradictions with annotations.

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

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

Four sentences, front-loaded with purpose, then usage, then input format, then output. No redundant words; every sentence contributes value.

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

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

For a moderately complex tool (multi-leg options), the description covers purpose, usage, input, and output. Lacks specifics about error handling or edge cases, but annotations suffice. No output schema, but description lists return items adequately.

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 little beyond specifying the input structure ('array of legs with strike, premium, quantity, and type'). This mirrors the schema without new insights.

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 starts with a clear, specific verb+resource: 'Multi-leg options strategy P&L, breakevens, max profit/loss, risk/reward.' It contrasts with sibling tools like options_price (single option) and options_implied-vol (volatility), distinguishing its multi-leg focus.

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

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

Explicitly states 'Use when analyzing a multi-leg options strategy' and provides examples (spreads, straddles, etc.). While it doesn't list when not to use, the clarity implies alternatives exist for single-leg strategies.

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?

Annotations already indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds beyond these by stating that the tool returns a ranked list with detailed outputs (legs, profit/loss, breakevens, etc.) and that it is paid-only. No contradictions with annotations.

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

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

The description is very concise, with only a few sentences. It front-loads the purpose and return format, and includes a usage note. Every sentence is necessary and there is no wasted 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?

Given the tool has 8 parameters, no output schema, and annotations, the description covers the main aspects: purpose, required inputs, return contents, and a usage note. It does not explain the scoring criteria or how the ranking works, but overall it is fairly complete.

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

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

Schema description coverage is 100%, so the schema already documents all parameters well. The description mentions providing spot price, outlook, vol_view, T, sigma, r, but this adds minimal value beyond the schema. Baseline 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 that the tool ranks top options strategies given market outlook and volatility view, and lists what it returns (P&L, breakevens, etc.). It is specific about the verb 'rank' and resource 'options strategies', but does not explicitly differentiate from sibling tools like 'options_strategy' or 'options_spread-scan'.

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

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

The description includes 'Use when agents need to pick the best options strategy given a market outlook', which provides clear context. It also notes 'PAID ONLY — no free tier', giving a usage restriction. However, it does not provide alternatives 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?

Description adds critical context: 'PAID ONLY — no free tier' beyond annotations (readOnlyHint, idempotentHint, destructiveHint). It also details the return values, which helps the agent understand behavior.

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