HPSILab Quant Finance

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds important details: partial success when some tools fail, full failure only if all fail, and refresh parameter behavior.

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

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

Two well-structured paragraphs with front-loaded purpose. Every sentence adds value, no waste.

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 moderate complexity and existing output schema, description covers error handling and refresh. Could list aggregated tools, but output schema likely documents return fields.

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

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

With 0% schema description coverage, the description compensates by explaining symbol (example NVDA) and refresh (request fresh IV Radar data). Could be more precise but adds necessary meaning.

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 aggregates all quant tools into one JSON analysis, deriving direction signal, score, and summary. It distinguishes from siblings like get_iv_radar which are individual 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 implies use for comprehensive stock analysis but lacks explicit when-to-use vs alternatives. However, it covers behavior when underlying tools fail, providing context for reliability.

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?

Discloses that force defaults to True to reflect latest data, and that it uses cached PNGs when force=False. Annotations show readOnlyHint=false and destructiveHint=false, consistent with generation. Adds context about reusing email/social generators.

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

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

The description is well-structured with a clear purpose sentence, followed by context and a formatted Args section. No unnecessary words, each 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 the presence of an output schema, the description sufficiently covers return value (URLs). Explains all parameters and distinguishes from main sibling. Could mention relationship to get_* chart tools, but not essential.

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

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

With 0% schema description coverage, the Args section fully explains all three parameters: symbol with example, force with default and rationale, types with allowed values and omission behavior. This compensates completely.

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 generates stock-report PNG images and returns URLs, using a specific verb and resource. It distinguishes itself from analyze_stock by noting the separation for fast JSON analysis.

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

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

Explicitly states separation from analyze_stock to keep analysis fast. Mentions backend reuse. Could be more explicit about when to use individual get_* tools instead, but provides good context.

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

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

Discloses that it runs other tools concurrently, returns both markdown and JSON, and mentions the payment model. Adds context beyond annotations (e.g., non-mutating behavior despite readOnlyHint=false).

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?

Well-structured with purpose, cost, execution details, then arguments. Slightly verbose but all information is relevant and well-organized.

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 all aspects: output format (markdown + JSON), cost model, parameter details, and notes on caching. No missing information 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?

With 0% schema coverage, the description fully explains all parameters: symbol with example, refresh as cache bypass, force_images as fresh render. No ambiguity.

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 generates a full markdown research report with five charts, distinguishes from siblings by describing it as a composite tool that runs analyze_stock and image generation concurrently.

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?

Explains cost and authentication requirements, implying when to use this tool vs. alternatives, but does not explicitly state when not to use or enumerate 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 indicate readOnly and idempotent hints. The description adds behavioral context that the prediction is for the next day and includes components like probability and signal, which is beyond what annotations provide. 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 concise with three focused sentences: purpose, availability, and parameter definition. Front-loaded with the core purpose, no unnecessary text.

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

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

For a simple tool with one parameter and no output schema, the description covers the purpose, output components, availability, and parameter definition. It is complete enough for an agent to understand when and how to invoke the tool.

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

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

The single 'ticker' parameter is only a string in the schema. The description adds full semantics by defining it as 'Stock symbol' and providing an example 'TSLA', which compensates for the 0% schema description coverage.

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

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

The description clearly states the tool provides an AI next-day prediction with specific outputs: probability of closing UP, a buy/watch/sell-lean signal, and consensus strength. This distinguishes it from siblings like analyze_stock which are broader.

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 the tool is available to all authenticated plans and subject to rate limits, providing important context for the agent to decide if it can call the tool. While it does not explicitly compare to siblings, the availability and limits information is sufficient guidance.

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

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

Annotations already declare readOnly, safe, idempotent. Description adds context: plan limits, request limits, behavior of leaving ticker blank vs providing one. 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?

Five lines, front-loaded purpose, clear Args section. 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?

Simple tool with one optional param and no output schema; description covers all needed: purpose, metrics, plan info, parameter 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?

Description fully explains the only parameter (ticker): optional, example 'SPY', default behavior (show all symbols). Compensates for zero schema description coverage.

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

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

Clear verb and resource: 'Backtest performance of the quant strategy across your watchlist' with specific metrics listed (Sharpe ratio, max drawdown, etc.). Distinguishes from siblings like 'analyze_stock' or 'get_mc' by focusing on overall backtest performance.

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?

States availability to authenticated plans and subject to rate limits. Implicitly when to use (for backtest stats) but does not explicitly contrast with alternatives. Still clear enough for 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 safe read/ idempotent behavior. The description adds context about the 'refresh' parameter bypassing cache and requesting latest data. This supplements the annotations with operational detail.

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

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

The description is two clear paragraphs: purpose first, then parameter details. No redundant sentences, though the opening sentence could be slightly tighter. Efficiently 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?

For a simple read tool with two parameters and no output schema, the description adequately covers purpose and parameters. Lacks return format details, but the tool concept is well explained.

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

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

With 0% schema description coverage, the Args section provides meaning: 'ticker' gets an example ('NVDA'), 'refresh' is explained as bypassing the IV cache to get latest option-chain pull, including default. This adds value beyond the bare schema.

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

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

The description clearly states it provides 'Implied-volatility (IV) structure for a stock', specifying the resource and scope. It explicitly lists what it reveals (how expensive options are, volatility squeeze, call/put bias), distinguishing it from sibling tools like analyze_stock or get_option_pressure.

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 indicates availability to all signed-in users and implies usage for IV analysis, but does not explicitly compare to alternatives or state when not to use it. The purpose is clear enough from the description.

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. The description adds context about the simulation method and output, but does not disclose potential limitations or data dependencies.

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

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

The description is concise: a single paragraph front-loading the purpose followed by a minimal parameter definition. Every sentence adds value 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?

For a simple, one-param tool with complete annotations and no output schema, the description adequately explains the simulation's time horizon, method, and expected outputs (price range, odds). Missing details on return format, but still functional.

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

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

With 0% schema description coverage, the description compensates by specifying 'Stock symbol, e.g. 'AAPL',' adding meaningful context that the schema alone ('Ticker') lacks.

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 'Monte Carlo price simulation for the next ~10 trading days' and explains the method and outputs, effectively distinguishing it from siblings like 'analyze_stock' or 'get_ai_prediction'.

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

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

No guidance on when to use this tool versus alternatives, such as when a simple price estimate suffices or when other prediction tools are more suitable.

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 behavioral context beyond annotations, listing specific output metrics (Max Pain, Gamma Wall, weekly high, squeeze target). Annotations already indicate safe, read-only, idempotent behavior, so the description complements them 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?

The description is extremely concise (2 sentences plus parameter docstring), front-loaded with the main purpose, and contains no superfluous 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 (1 parameter, no output schema, rich annotations), the description fully covers what the tool returns and what input it needs, leaving no significant 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?

With 0% schema description coverage, the description provides necessary parameter semantics: 'ticker: Stock symbol, e.g. 'SPY'.' This adds meaning beyond the schema's type-only definition, though it could include additional constraints (e.g., valid exchanges).

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: returning an option-chain pressure map for the nearest weekly/monthly expiry, including specific metrics like Max Pain and Gamma Wall. It differentiates from sibling tools like get_iv_radar by focusing on pressure map 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 implies usage for near-term expiries ('nearest weekly/monthly expiry') but does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or prerequisites.

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, openWorldHint, idempotentHint, and destructiveHint, indicating safe behavior. The description adds value by disclosing pricing ($0.15/call for anonymous, free within plan for signed-in) and the x402 payment mechanism, which are behavioral traits 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 mostly concise, with a clear first sentence stating the core purpose. The payment details add length but are relevant. The structure separates general description from parameter documentation. Minor redundancy could be trimmed, but overall it's 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 tool's low complexity (1 required parameter, no enums, no nested objects) and the presence of an output schema, the description adequately covers input and usage context. It explains the output's nature (JSON) and pricing boundaries, though it could briefly mention what "risk scan" entails if the output schema is unavailable to the agent.

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

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

The schema coverage is 0%, so the description carries the full burden. It defines the parameter "symbol" as a stock symbol with the example "NVDA," adding meaningful semantics beyond the schema's type and title. For a single required parameter, this is clear and helpful.

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

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

The description clearly states the tool provides a "Full pre-trade risk scan JSON for a stock." The verb "get" is implicit, and the resource "pre-trade risk scan" is specific. It effectively distinguishes from siblings like analyze_stock or get_ai_prediction by focusing on risk scan output.

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 explains payment models for different user types (signed-in vs anonymous) and mentions rate limits. However, it does not provide explicit guidance on when to choose this tool over sibling tools, nor does it state 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?

The description adds meaningful behavioral context beyond the annotations: explains that the account is server-side bound and that the Authorization header cannot be changed, describes the email confirmation flow, and notes that the anonymous allowance persists until confirmation. This adds significant insight not present in 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 well-structured: first sentence states the purpose, then usage context, then prerequisites/constraints, then parameter details. Every sentence contributes value without redundancy. It is appropriately sized for a tool with side effects and user interaction requirements.

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 registration tool with one parameter and no output schema, the description covers all necessary aspects: what it does, when to use it, what the user must provide, what happens after (email confirmation), and the immediate result. It is fully self-contained and leaves no critical 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?

The schema provides only the parameter name and format, but the description clarifies it as 'The real user email address that will receive verification.' This adds crucial meaning about the expected value (real, not fabricated) and its role in the verification process.

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

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

The description opens with a clear, specific action: 'Register a free hpsilab account for yourself' and states the immediate outcome ('receive an API key immediately'). It clearly distinguishes this from the sibling stock-analysis tools by focusing on account registration.

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 tells when to use: 'Call this when you are hitting anonymous daily limits and want a higher allowance.' Provides exclusions: 'Do not call this tool with an empty or fabricated email address' and advises asking the user for email first if unavailable. This gives both positive and negative usage guidance.

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