hpsilab-mcp

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

Annotations already mark it as read-only and non-destructive. The description adds behavioral context about aggregation: it reuses other MCP tools as data sources, derives new outputs, and tolerates partial failures. This goes beyond the annotations by explaining the composite nature and fault tolerance.

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 one-sentence purpose followed by brief outlines of outputs and error behavior, then clear parameter documentation. Every sentence adds value without repetition or 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?

Given the tool has an output schema (not shown), the description needn't detail return values. It adequately covers the tool's composition, error tolerance, and key behavioral traits. Some minor gaps like specifying aggregation order or performance implications, but overall complete for its 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 both parameters: symbol (with example 'NVDA') and refresh (clarifying it requests fresh IV Radar data and defaults to False). This provides essential meaning that the schema alone 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 the tool aggregates all quant tools into one JSON stock analysis, specifying it derives a direction signal, score, bullish/bearish factors, and summary. This distinguishes it from sibling tools like get_iv_radar or get_option_pressure, which focus on individual data points.

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 that the tool is a composite of other tools and handles errors gracefully (gated or unavailable tools still allow others to contribute). While it doesn't explicitly state when not to use alternatives, the aggregation purpose and resilience guidance provide clear usage context for an agent deciding between this and individual 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 indicate readOnly and non-destructive. Description adds that backend reuses image generators from email/social publishing. 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 sentences, bullet-style args, no fluff. Efficiently conveys purpose and parameter details.

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

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

Output schema exists, so not describing return format is acceptable. Could mention caching or performance but adequate as is.

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 0%; description explains symbol and force (defaults to True) but does not explain 'types' parameter at all.

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 PNG images and returns URLs. Distinguishes from sibling analyze_stock by explaining separation.

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 why separate from analyze_stock (keeping JSON analysis fast). Lacks explicit when-not-to-use or alternatives for different image types.

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, openWorldHint=true, and destructiveHint=false. The description adds behavioral details such as concurrent execution, dual output (markdown and JSON), and payment requirements for anonymous users, but 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 well-structured with an initial summary, then details on components, usage context, and parameter documentation. Every sentence provides value without unnecessary 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?

Given no output schema, the description explains the return format (markdown for display, JSON mirror). It also covers usage constraints (payment) and input parameters. The tool is complex but the description is sufficiently complete for an agent to use it 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?

The input schema has 0% description coverage, so the description adds necessary meaning: symbol example, refresh explanation (bypass IV cache), and force_images (force fresh render). This compensates for the schema's lack of descriptions, though more detail on 'IV cache' could further improve clarity.

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 generates a full markdown research report with five charts. It specifies that it runs analyze_stock and stock-report image generation concurrently, distinguishing it from sibling tools like analyze_stock and generate_stock_images which perform individual sub-tasks.

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 context on usage for signed-in users (free rate limits) and anonymous agents (x402 payment). However, it does not explicitly state when to prefer this combined tool over calling the sibling tools separately.

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 and non-destructive behavior. The description adds context about return content (probability, signal, consensus) and plan limits, enhancing transparency 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 three sentences plus an Args line, conveying necessary information efficiently. It is well-structured and 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 a simple single-parameter tool with output schema, the description covers purpose, availability, and parameter semantics adequately. No major 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 adds meaning by providing an example ('Ticker: Stock symbol, e.g. TSLA'), which clarifies usage beyond the basic type definition.

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 AI next-day predictions including probability of stock closing up, a signal, and consensus strength. It uses specific verbs and resources, and distinguishes from siblings focused on broader analysis or reports.

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

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

The description mentions availability to all authenticated plans with plan limits, but does not explicitly state when to use this tool versus alternatives like analyze_stock or generate_stock_research_report.

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 context beyond annotations: it clarifies that the tool is available to all plans, subject to requests/day and requests/minute limits. It does not contradict the annotations (readOnlyHint, openWorldHint, destructiveHint). The behavior of providing per-symbol performance metrics is transparent.

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 fairly concise, front-loading the purpose and then providing plan context and parameter details. It could be slightly more concise by removing the plan information, but it's still structured well 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?

The description is complete for a simple tool with one parameter and an output schema. It explains what the tool returns (metrics per symbol) and how to use the parameter. However, it does not mention the time range or data source for the backtest, which could be useful context.

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 description provides excellent parameter guidance for the single 'ticker' parameter. It explains that it's optional, gives an example ('SPY'), and clarifies that leaving it blank returns all symbols. This adds significant value beyond the schema, which only has a default of ''.

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: backtesting quant strategy performance across a watchlist with specific metrics (Sharpe ratio, max drawdown, etc.). It distinguishes itself from sibling tools like analyze_stock or get_ai_prediction by focusing on historical performance metrics.

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

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

The description mentions it's available to all authenticated plans with rate limits, giving context on when the tool can be used. However, it does not explicitly state when to use this tool instead of alternatives, such as when to prefer get_equity_curves over other analytical 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 mark the tool as read-only (readOnlyHint=true) and non-destructive (destructiveHint=false). The description adds value by explaining the 'refresh' parameter behavior (bypasses cache for latest data) and the special role of the tool. It does not contradict annotations, though the side effect on the backend cache is not fully elaborated.

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 plus an Args block, no filler. The main purpose is front-loaded in the first sentence. Every part contributes to understanding, including the availability note and parameter details.

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

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

Given the tool has 2 parameters, an output schema (not shown but exists), and the complexity of IV structure, the description covers purpose, scope, availability, and parameter details. No missing aspects are evident; the output schema handles return values.

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 0%, but the description compensates well with an Args section explaining both parameters. 'ticker' is exemplified with 'e.g. "NVDA"', and 'refresh' is explained as bypassing cache with a default of False. This adds meaning beyond the schema types and 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 it provides 'Implied-volatility (IV) structure for a stock' with specific outputs like option expensiveness, volatility squeeze, and call/put bias. It distinguishes from sibling tools (e.g., get_option_pressure) by focusing on IV structure. The verb 'get' and resource 'IV radar' are explicit.

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

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

The description mentions 'Available to all signed-in users,' which sets usage context. It implies when to use (to understand option pricing dynamics) but does not explicitly state when not to use or provide alternatives. Sibling tools are not referenced in the description, so guidance on selecting between tools is absent.

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 non-destructive. The description adds that the simulation is stochastic, generating thousands of random paths, which gives important behavioral context. However, it could mention that results vary per call due to randomness.

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 explains the purpose, second describes the args. No extraneous information, front-loaded, efficiently conveys necessary details.

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

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

Given the complexity of Monte Carlo simulation, the description covers the core functionality. The output schema exists (not shown) so return values are not required. Minor omissions: no discussion of assumptions or limitations (e.g., market regimes).

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 provides the only explanation for the 'ticker' parameter: 'Stock symbol, e.g. "AAPL"'. This is sufficient but minimal; could be improved by noting valid symbols or case-insensitivity.

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 Monte Carlo price simulation for ~10 trading days, estimating a price range and probability of finishing higher. This distinguishes it from sibling tools like get_ai_prediction or analyze_stock.

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 what the tool does but does not provide explicit guidance on when to use it versus alternatives like get_ai_prediction or analyze_stock. Usage context is implied but not directly stated.

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, openWorldHint=true, and destructiveHint=false, establishing a safe, non-mutating behavior. The description adds value by specifying the output content (pressure map, specific metrics) and the temporal scope (nearest weekly/monthly expiry), which goes beyond the annotations without contradicting them.

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

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

The description is extremely concise: two sentences define the purpose and key metrics, followed by a clean argument definition. Every word is informative, with no filler or redundancy, and the most critical 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?

Given the tool has an output schema (not shown but indicated), the description does not need to detail return structure. It lists the key metrics (Max Pain, dealer Gamma Wall, etc.) which suffices for an agent to understand what the tool provides. The parameters are fully explained, and the expiry scope is clarified. This is complete for the tool's complexity level.

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 0% description coverage, so the description must compensate. It does so by explaining the 'ticker' parameter as 'Stock symbol, e.g. SPY', adding clear meaning and an example. This is sufficient for a single-parameter tool; a higher score would require additional constraints or format details.

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

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

The description clearly states the tool provides an 'option-chain pressure map' for the nearest weekly/monthly expiry, listing specific metrics like Max Pain, dealer Gamma Wall, likely weekly high, and extreme squeeze target. This distinguishes it from sibling tools such as analyze_stock (general analysis) or get_iv_radar (IV focus), making the purpose unmistakable.

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 specific expiry windows ('nearest weekly/monthly expiry') and lists options-related metrics, which suggests use cases around options pressure analysis. However, it lacks explicit when-to-use or when-not-to-use guidance and does not reference alternative tools like get_iv_radar for volatility or analyze_stock for broader insights.

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 and non-destructive behavior. The description adds valuable behavioral context: rate limits for signed-in users, x402 payment for anonymous agents, and that the return is JSON. 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 (4 sentences) and front-loaded with the main purpose. Every sentence provides distinct value: purpose, user types, payment method, and parameter details. No unnecessary 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 simplicity (one required parameter, output schema exists), the description covers all necessary aspects: what it returns, who can use it, how payment works, and the parameter. No gaps remain.

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 only one parameter and 0% schema coverage, the description adds meaning by specifying the parameter name ('symbol'), its type ('Stock symbol'), and an example ('NVDA'). This compensates for the schema's lack of 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 'Full pre-trade risk scan JSON for a stock,' specifying the verb ('get'), resource ('pre-trade risk scan'), and output format ('JSON'). This distinguishes it from sibling tools like 'get_option_pressure' or 'analyze_stock'.

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 context on rate limits for signed-in users and payment for anonymous users, but does not explicitly state when to use this tool versus alternatives or when not to use it. Usage is implied but lacks comparative guidance.

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