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

Without annotations, the description carries full burden. It details output content but does not disclose behavioral traits like error handling, rate limits, or authentication requirements. The examples are helpful but transparency is moderate.

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 brief intro then bullet points, plus Args/Returns sections. It is concise yet informative, though a bit verbose in listing data points.

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 (not shown), the description adequately covers return details. However, it lacks context on when results might be empty or errors (e.g., invalid symbol). The description is sufficient but not exhaustive.

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

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

Schema coverage is 0%, but the description adds meaning: it explains the parameter 'symbol' as a stock ticker with examples (e.g., 'AAPL'). This goes 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 comprehensive stock analysis including specific data points (price, technicals, fundamentals, options). It differentiates from sibling tools like get_insider_buys or screen_options_wheel by its broad scope, but does not explicitly call out the differentiation.

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 vs. alternatives. It lists what it provides but does not specify scenarios (e.g., 'For a quick overview, use this; for insider trading, use get_insider_buys').

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

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

No annotations provided, so description carries full burden. Discloses data source (House Clerk PTR filings), factual background, and return format. Does not mention rate limits or auth, but read-only nature is implied.

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 leading purpose sentence, informative context, and clear parameter list. Not overly verbose, but context paragraph could be slightly trimmed.

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 description needn't detail return fields. Provides legal context and scoring hint. Lacks explanation of score interpretation or pagination, but adequate given schema.

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 description adds an 'Args' section that explains each parameter (min_score, ticker_filter, limit) with purpose and defaults, fully compensating.

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 'Get recent congressional stock trades' with specific verb and resource. Distinguishes from siblings like get_insider_buys by specifying 'congressional' and 'House financial disclosures'.

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

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

Provides clear context on when to use, citing the STOCK Act and predictive performance. Notes filtering options but lacks explicit when-not-to-use or alternative tools.

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

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

No annotations present, so description carries full burden. It explains the tool's output and purpose but lacks disclosure of side effects, access restrictions, or rate limits.

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

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

Concise three-paragraph structure with front-loaded purpose, clear method explanation, and parameter description. No wasted words.

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

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

With an output schema present, the description adequately describes return data (ranked plays with IV and crush scores). Single parameter fully explained. No gaps.

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

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

Schema description coverage is 0%, so description compensates fully by explaining 'days_ahead' meaning, default, and max, beyond schema's type and default.

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 action (get upcoming earnings) and resource (stocks with high IV), and distinguishes it from siblings like analyze_stock or screen_options_wheel by focusing on IV crush strategies.

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

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

Provides context for when to use (earnings with high IV for short-volatility strategies), but does not explicitly mention when not to use or compare with alternatives.

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

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

No annotations are provided, so the description carries the full burden. It discloses that the tool scans recent filings and returns scored signals based on purchase size, insider role, and cluster buying. It is clearly read-only, though not explicitly stated. No contradictions.

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

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

The description is well-structured: a purpose sentence, followed by bullet points detailing behavior, parameters, and return type. Every sentence adds value; the academic research line provides context without fluff. Efficient and front-loaded.

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

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

Given the tool's simplicity (2 parameters, output schema exists), the description is complete. It covers what the tool does, how it works, what parameters do, and what it returns (JSON array scored and ranked). No gaps for effective use.

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

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

Schema description coverage is 0%, so the description must compensate. The Args section explains both parameters: 'days' (look back period with max 30) and 'min_value' (minimum purchase value in USD). Schema only provides defaults and types; description adds meaning and constraints beyond schema.

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

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

The description clearly states the tool retrieves insider buying signals from SEC Form 4 filings. It specifies the resource (SEC EDGAR filings), verb (get scored signals), and scope (open-market purchases by insiders). It distinguishes from siblings like get_congressional_trades by focusing on corporate insiders.

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 for usage, mentioning academic research that insider buying predicts returns. This implies use for investment signals. However, it does not explicitly state when not to use this tool or mention alternatives among sibling tools, leaving the agent to infer.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It outlines the return format (JSON) and data categories but does not address data freshness, caching, authentication requirements, or any potential delays. This is adequate for a simple read-only tool but lacks depth.

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 and well-structured, using bullet points to list data categories. Every sentence serves a purpose, with no redundant or vague phrasing.

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 (no parameters, no annotations, but has an output schema), the description is complete. It explains the tool's purpose, the data returned, and the return format, which is sufficient 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 zero parameters, so the description does not need to add parameter details. Baseline for zero parameters is 4, and the description appropriately omits parameter information.

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 market overview with specific major indices, VIX, sector ETFs, and top movers. It uses a specific verb-resource combination and distinguishes itself from sibling tools like analyze_stock and get_earnings_iv_plays by focusing on a broad market snapshot.

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 the tool is for a quick snapshot of market conditions but provides no explicit guidance on when to use it versus alternatives. There is no mention of prerequisites or exclusion criteria, leaving the agent to infer usage 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?

No annotations are provided, so the description bears full responsibility. It states the tool returns ranked candidates including premium yields and risk metrics, but does not disclose data freshness, rate limits, or whether it requires authentication. The screening criteria are explained, but behavioral traits beyond that are absent.

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 and well-structured: purpose first, then method, then parameter details. Every sentence adds value, and the argument list is clearly formatted. No wasted words.

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

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

With only 3 parameters, all explained, and an output schema present (so return format is covered), the description is complete. It explains the screening logic and metrics returned, making it sufficient for an agent to understand and invoke the tool correctly.

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

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

Schema coverage is 0%, but the description compensates by explaining each parameter's purpose, default values, and constraints (e.g., min_price avoids penny stocks). This adds significant meaning beyond the bare schema, though the parameter names are already somewhat self-explanatory.

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 screens for cash-secured put candidates for the wheel strategy, using a specific verb ('screen') and resource ('cash-secured put opportunities'). It distinguishes itself from sibling tools (e.g., analyze_stock, get_market_pulse) by focusing solely on wheel strategy candidates.

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 the wheel strategy and when to use the tool (for identifying candidates with high put premiums). However, it lacks explicit guidance on when not to use it or clear alternatives among siblings. The context is helpful but not exhaustive.

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