QuantOracle
Server Quality Checklist
- Disambiguation4/5
Tools target distinct quantitative domains (options pricing, risk metrics, technical analysis, simulation) with clear functional boundaries. Minor overlap exists between risk_portfolio and stats_sharpe-ratio since both calculate Sharpe ratios, though one offers comprehensive portfolio analysis while the other is a standalone utility for simple calculations.
Naming Consistency3/5Employs domain prefixes (options_, risk_, stats_) but mixes abbreviations (tvm, stats) with full words and includes a verb prefix (simulate_). Hyphenation is inconsistent (implied-vol vs technical), word order varies (indicators_technical places the descriptor last), and pluralization differs across the set.
Tool Count5/5Eleven tools represent an ideal scope for a specialized quantitative finance server, providing comprehensive coverage of options analytics, risk management, and statistical utilities without excessive fragmentation. Each tool addresses a specific computational domain without redundant alternatives.
Completeness4/5Strong coverage of options analytics (pricing, implied volatility, multi-leg strategies) and risk metrics (Kelly criterion, portfolio VaR/CVaR), but lacks broader time-value-of-money functions beyond CAGR and omits portfolio optimization or correlation analysis tools. The surface supports core quantitative workflows but has minor gaps in fixed income and comprehensive portfolio construction.
Average 3.3/5 across 11 of 11 tools scored. Lowest: 2.5/5.
See the tool scores section below for per-tool breakdowns.
This repository includes a README.md file.
This repository includes a LICENSE file.
Latest release: v2.1.0
Tools from this server were used 2 times in the last 30 days.
This repository includes a glama.json configuration file.
- This server provides 63 tools. View schema
No known security issues or vulnerabilities reported.
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Tool Scores
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
While annotations correctly declare readOnlyHint=true and idempotentHint=true, the description adds zero behavioral context. It fails to specify what the 13 indicators are, what format the output takes, or any constraints beyond the schema minimums.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely brief (6 words), avoiding bloat, but it fails the 'every sentence earns its place' test by offering only a domain label that largely repeats the annotation title without actionable guidance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (13 distinct indicators) and the absence of an output schema, the description should explain the return format and list the indicators calculated. The current description leaves critical output semantics undocumented.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, establishing a baseline of 3. The description ('13 technical indicators...') adds no additional meaning regarding parameter semantics, usage examples, or valid ranges beyond what the schema already documents.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the domain (technical indicators) and mentions 'composite signals,' but lacks a specific verb (e.g., 'calculates,' 'computes') to clarify the action performed. It does not differentiate from statistical siblings like stats_realized-volatility or stats_sharpe-ratio.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance provided on when to use this tool versus the statistical or options analysis siblings. No mention of prerequisites (e.g., minimum data quality) or when volume data is required versus optional.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds value by specifying exactly which 22 metrics are computed, but fails to describe the output structure (absent output_schema), computational limits beyond schema's maxItems, or interpretation guidance for the metrics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise single sentence with no redundant words. However, the brevity approaches under-specification given the tool's complexity (22 distinct calculations). The metric list is efficiently presented.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex 22-metric calculation tool with no output schema, the description provides the bare minimum by naming the metrics. It omits return value structure, grouping of metrics (e.g., risk-adjusted vs. tail-risk), or sample output format that would aid agent invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear definitions for 'returns', 'benchmark_returns', and 'risk_free_rate'. The description adds no parameter-specific guidance, meeting the baseline score for high-coverage schemas.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5Does the description clearly state what the tool does and how it differs from similar tools?
The description lists the 22 risk metrics calculated (Sharpe, VaR, etc.) implying a calculation function, but lacks an explicit verb (e.g., 'calculate', 'compute'). It distinguishes from sibling 'stats_sharpe-ratio' (single metric) by scope, but doesn't clarify when to prefer this over 'risk_kelly' or 'simulate_montecarlo'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance provided on when to use this comprehensive tool versus specific alternatives like 'stats_sharpe-ratio' (for single metric) or 'risk_kelly'. No mention of prerequisites such as minimum data requirements (though minItems: 5 exists in schema).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations comprehensively cover safety profiles (readOnlyHint, destructiveHint, idempotentHint), so the description does not need to address those. However, the description fails to disclose return value formats, computational behavior, or the mutually exclusive relationship between the x and p parameters despite the absence of an output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of a single 7-word fragment that immediately states the supported operations without redundancy. While appropriately brief for a mathematical utility, the extreme brevity sacrifices opportunity to provide usage guidance or behavioral context that would assist agent invocation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema and the tool's multiple operational modes (CDF, PDF, quantile, confidence intervals), the description inadequately explains what values are returned or how the parameters interact. The description should specify that x and p are mutually exclusive inputs for different calculation directions and describe the return structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the structured schema already documents each parameter's purpose (e.g., 'Value to compute CDF/PDF for', 'Probability for inverse CDF'). The description lists the operations available but adds no semantic meaning regarding parameter relationships or constraints beyond what the schema provides, warranting the baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies specific statistical operations (CDF, PDF, quantile, confidence intervals) performed on the normal distribution resource, using precise terminology. While it clearly indicates the tool's functionality, it does not explicitly differentiate from sibling statistical tools like stats_sharpe-ratio or stats_realized-volatility regarding when to prefer this specific distribution analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, nor does it clarify which parameters to use for which calculation type (e.g., using x for CDF/PDF versus p for quantile). There is no mention of prerequisites such as requiring exactly one of x or p to be provided, or that confidence_level operates independently.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, covering the safety and determinism profile. The description adds the term 'Standalone,' which minimally suggests it returns a single value rather than a complex object, but provides no details on calculation methodology, error handling (e.g., zero variance), or return format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of a single, efficient sentence with no redundant words or filler. It is appropriately front-loaded for a simple calculation tool, though the brevity sacrifices some explanatory detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (single mathematical calculation) and rich schema (100% coverage), the description is minimally adequate. However, it lacks mention of what the tool returns (a numeric ratio), the concept of risk-adjusted returns, or how to interpret the result, which would be helpful given the absence of an output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the baseline is 3. The description does not mention parameters at all, offering no additional context beyond the schema (e.g., explaining that returns should be in decimal form, or that risk_free_rate should match the annualization_factor period).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the resource (Sharpe ratio) and input (returns series) but lacks an explicit action verb (e.g., 'Calculate'). It reads as a noun phrase ('Standalone Sharpe ratio...') rather than stating what the tool does, leaving the action implied rather than explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance provided on when to use this tool versus siblings like 'stats_realized-volatility', 'risk_portfolio', or 'risk_kelly'. The description does not indicate prerequisites (e.g., minimum data requirements, though minItems: 5 is in schema) or when this metric is preferred over other risk-adjusted measures.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true and idempotentHint=true, covering safety and determinism. The description adds value by specifying the analytical outputs (P&L curve, breakeven points) returned by the tool, though it omits details about calculation methodology or performance characteristics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely terse at nine words, but efficiently structured with the key domain concept first and specific outputs enumerated. No redundant or filler text, though the extreme brevity leaves room for expansion with usage context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the 100% schema coverage and comprehensive annotations, the description provides sufficient context for a calculation tool by enumerating the returned metrics. However, it lacks guidance on strategy construction patterns or interpretation of results that would help an agent use this effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the schema adequately documents all parameters (legs, S_range, points) including their semantics (positive=long, negative=short for quantity). The description adds no parameter-specific guidance, meeting the baseline expectation for high-coverage schemas.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the domain (multi-leg options strategies) and specific outputs calculated (P&L, breakevens, max profit/loss, risk/reward). It effectively distinguishes from sibling tools like 'options_price' or 'options_implied-vol' by specifying 'Multi-leg' and strategy-level metrics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance provided on when to use this tool versus alternatives like 'options_price' for single legs, or prerequisites for constructing valid strategies. No mention of input requirements beyond the schema.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds useful context mapping 'discrete' to win/loss scenarios and 'continuous' to return series analysis, but doesn't explain what the tool returns (optimal fraction to bet) or validation logic implied by the parameter descriptions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise at 10 words. The single sentence is front-loaded with the core concept (Kelly Criterion) and mode distinctions. While efficient, it borders on too terse for users unfamiliar with which mode applies to their use case.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the rich schema (100% coverage, clear parameter descriptions), the description doesn't need to detail parameters. However, for a financial calculation tool, it omits what the output represents (optimal betting fraction) and doesn't clarify the mutual exclusivity of parameter groups (discrete vs continuous inputs) beyond the mode parameter description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the baseline is 3. The description adds minimal semantic grouping by mentioning the two modes parenthetically, but doesn't need to compensate for schema gaps since all parameters are well-documented in the schema itself.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool calculates the Kelly Criterion and specifies the two supported calculation modes (discrete vs continuous). However, it doesn't explicitly state that this is for optimal position/bet sizing or distinguish it from the sibling 'risk_portfolio' tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use discrete mode (win/loss probabilities) versus continuous mode (return series), nor when to prefer this over the sibling risk_portfolio tool. The description only labels the modes without explaining selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, establishing this is a safe, deterministic calculation. The description adds value by disclosing the 'forward projections' behavioral trait, indicating the tool can extrapolate future values, not just calculate historical CAGR. However, it fails to describe the projection scope (how many years forward) or return format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise single sentence. The phrase 'Compound Annual Growth Rate' is slightly redundant with the tool name and annotation title, but 'with optional forward projections' provides essential distinctiveness. No wasted words, though additional sentences covering usage guidelines would improve utility.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple four-parameter calculation tool with complete schema coverage and comprehensive annotations, the description is minimally adequate. However, given the absence of an output schema, it should ideally describe what the tool returns (e.g., the growth rate percentage and projection values) and explain the projection behavior more explicitly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 100% description coverage, clearly documenting all four parameters including the optional boolean flag. The description mentions 'optional forward projections' which aligns with the include_projections parameter, but adds no semantic detail beyond what the schema already provides (e.g., no guidance on projection horizon or value interpretation).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the specific calculation performed (Compound Annual Growth Rate) and distinguishes it from sibling tools (technical indicators, options pricing, risk calculations) by specifying it is a TVM calculation. It also highlights the optional forward projections feature. Lacks an explicit verb ('Calculate'), relying on implied action.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides no guidance on when to use this tool versus alternatives, nor when to enable the optional projections parameter. No mention of input constraints (e.g., start_value must be less than end_value for positive growth) or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, non-destructive, idempotent characteristics. The description adds valuable context about the model (GBM) and operational constraints (5000 path maximum). However, it fails to disclose what the tool returns (raw paths vs. percentiles vs. summary statistics), random seed behavior, or computational complexity, which is critical information given the lack of output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two highly efficient sentences with zero filler. It front-loads the essential model identifier (GBM) and ends with the critical constraint (5000 paths). Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 7 parameters with full schema coverage and helpful annotations, the description meets minimum viability. However, for a simulation tool lacking an output schema, it should describe the return value structure (e.g., 'returns array of simulated portfolio paths' or 'returns percentile analysis') to be complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the baseline score is 3. The description mentions 'contributions/withdrawals' which aligns with the parameters, but adds no additional semantic context about parameter interactions (e.g., that contributions and withdrawal_rate apply simultaneously) or format details beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies the mathematical model (GBM - Geometric Brownian Motion), the method (Monte Carlo), and distinct features (contributions/withdrawals, 5000 path limit). It effectively distinguishes this from sibling analytical tools like stats_sharpe-ratio or tvm_cagr by indicating it's a stochastic simulation. It loses one point for not explicitly stating the domain (portfolio/financial asset simulation) though this is inferable from parameters.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus deterministic alternatives like risk_portfolio or tvm_cagr. It does not mention prerequisites, appropriate use cases (e.g., retirement planning vs. short-term forecasting), or when the stochastic approach is necessary versus deterministic calculations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the operation is read-only and idempotent. The description adds valuable context about the computational output—the '10 Greeks (delta through color)'—which annotations do not cover. It does not describe error conditions (e.g., handling of extreme volatility), but covers the key behavioral trait of what gets calculated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, dense sentence with zero waste. It front-loads the model name and key differentiator (10 Greeks), making it immediately scannable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite lacking an output schema, the description partially compensates by specifying that 10 Greeks are returned (hinting at the output structure). For a standard financial model with well-understood inputs, this is sufficient, though explicit return type documentation would improve it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the structured fields already document all parameters (S, K, T, r, sigma, q, type) completely. The description adds no parameter-specific guidance, but given the high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description provides a specific verb ('pricing'), identifies the exact model ('Black-Scholes'), and specifies the secondary outputs ('10 Greeks'). This clearly distinguishes it from siblings like options_implied-vol (which calculates volatility from price) and options_strategy (which analyzes multi-leg positions).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states what the tool does but provides no guidance on when to use it versus alternatives. It does not clarify, for example, that options_implied-vol is the inverse operation (calculating sigma from market price) or when to prefer this over options_strategy for risk analysis.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations establish read-only/idempotent safety; the description adds valuable behavioral context by specifying convergence characteristics (5-8 iterations), which informs latency expectations. However, it omits failure modes or output format details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with zero waste: first establishes identity and algorithm, second provides performance characteristics. Information is front-loaded and dense.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
While adequate for a calculation tool with complete input schema, the absence of an output schema creates a gap—the description should specify what value/object is returned (e.g., implied volatility as decimal, convergence metadata).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the schema fully documents all 7 parameters. The description does not add parameter-specific semantics, meeting the baseline expectation when the schema carries the descriptive burden.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies the exact algorithm (Newton-Raphson) and domain task (implied volatility solver), clearly distinguishing it from sibling tools like options_price (forward pricing) and stats_realized-volatility (historical calculation).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
While domain experts can infer this is for backing out volatility from observed market prices (inverse of pricing), the description lacks explicit when-to-use guidance versus options_price or warnings about convergence requirements.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations cover safety properties (readOnlyHint, idempotentHint), so the description isn't burdened with those. It adds value by specifying the mathematical estimators used, but omits details about return format (whether it returns all four volatility measures or selects one), array length requirements beyond the schema minimum, or handling of missing optional data.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely compact single sentence front-loaded with the core concept. Every term serves a purpose: 'Realized volatility' identifies the domain, the colon-delimited list specifies variants, and 'from OHLC' maps to input requirements. Zero redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a pure calculation utility with no output schema, the description adequately covers inputs and processing logic. It could benefit from mentioning whether the tool returns multiple values (one per method) or requires method selection, but the essential information for invocation is present.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema coverage, the baseline is 3. The description adds value by mapping the parameters to the finance domain concept 'OHLC' (Open/High/Low/Close) and linking specific price arrays to their required estimators (e.g., noting GK/YZ need open prices), providing semantic context beyond the schema's mechanical descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly identifies the resource (realized volatility) and specific calculation methods (close-to-close, Parkinson, Garman-Klass, Yang-Zhang), distinguishing it from the sibling 'options_implied-vol' tool which calculates market-implied rather than historical volatility.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description lists the available volatility estimators, implicitly suggesting which to use based on available data (e.g., Parkinson requires high/low), but lacks explicit guidance on when to prefer this over 'stats_sharpe-ratio' or other risk metrics, and doesn't clarify whether all methods are calculated simultaneously or selected via parameters.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.
To manually sync the server, click the "Sync Server" button in the MCP server admin interface.
How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
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