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

With no annotations provided, the description fully bears the burden of behavioral transparency. It thoroughly discloses the analytical approach (impact scores, direction, classification), and includes important caveats about correlation, market-wide events, low-volume stocks, and news pre-pricing. This ensures the agent understands the tool's behavior and limitations.

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

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

The description is lengthy but well-structured with sections (Key Metrics, Impact Classification, Use Cases, etc.). It is front-loaded with the core purpose. Every section adds value, though some redundancy exists (e.g., 'Why It Matters' partly repeats 'Analysis Insights'). Overall, it earns its length for a complex tool.

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

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

Given the tool's complexity and lack of an output schema, the description thoroughly covers what the tool returns: individual news impacts, aggregate statistics, top impacts, correlation strength, and event analysis. It also addresses use cases and limitations, making it contextually complete for agent invocation.

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

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

Schema description coverage is 100%, with clear descriptions for all three parameters. The description adds value beyond the schema by explaining trade-offs for days_back and news_limit (e.g., 'Longer periods provide more data but may include less relevant events'). This enhances semantic understanding.

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

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

The description clearly states the tool analyzes how news events affect stock prices over time, correlating news with price movements. It distinguishes itself from sibling tools like get_market_news and compare_peer_companies by focusing on impact analysis rather than just retrieving news or comparing peers.

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

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

The description provides explicit use cases with example queries, such as 'How does news affect Tesla stock price?' and 'Which news had the biggest impact on AAPL?'. It includes 'Important Notes' that caution about correlation vs. causation and other limitations, but lacks direct comparison to alternatives like get_market_news for when to use each.

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 full burden. It states outputs (max profit, loss, break-even, Greeks, chart) but does not disclose data source (live vs. cached), freshness, or any side effects. It implies calculation and visualization but lacks detail on behavior like whether it uses market data or limitations.

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

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

Three clear sentences: first defines function and outputs, second lists supported strategy complexity, third states use case. No extraneous words, efficient and well-structured.

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

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

For an options analysis tool with 100% schema coverage and no output schema, the description adequately covers purpose, outputs, and supported strategies. Lacks mention of chart format or data source, but overall sufficient for an agent to understand when to use it.

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

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

Schema coverage is 100% with parameter descriptions. The tool description adds context about supporting multi-leg strategies and that premium can be optional (already in schema). The description provides marginal additional clarity but does not significantly extend beyond schema definitions.

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

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

The description clearly states the tool analyzes options strategies to calculate specific metrics (max profit, loss, break-even, Greeks) and generate a chart. It lists supported multi-leg strategies (spreads, straddles, iron condors, butterflies), distinguishing it from siblings like 'calculate_greeks' (only Greeks) or 'get_options_chain' (raw data).

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 helps visualize risk/reward before entering a trade, which implies appropriate usage context. However, it does not explicitly state when not to use it or reference alternative tools (e.g., calculate_greeks for just Greeks, get_options_chain for raw data). No exclusions or prerequisites are provided.

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 full burden. It details what the tool returns (intrinsic value, recommendation, projections, sensitivity analysis) and explains that inputs are customizable with defaults from historical data. It does not mention rate limits, destructive effects, or error states, but for a computational model, the transparency is adequate.

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 sections, bullet points, and a table, making it readable. However, it is verbose: the 'What DCF Analysis Provides' section partially duplicates 'Returns'. The first sentence gets to the point, but the overall length could be reduced without losing clarity.

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

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

Given the absence of an output schema, the description adequately covers return values (intrinsic value, recommendation, projections, sensitivity analysis) and input options. It explains the investment recommendation thresholds and sensitivity analysis scope. However, it could be more explicit about the output format (e.g., JSON structure) and edge cases.

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

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

Schema coverage is 100%, with each parameter having a clear description. The description adds a high-level overview of key inputs (e.g., 'Revenue growth rates', 'WACC') and groups them, but it largely paraphrases what is already in the schema. There is no extra semantic insight beyond the schema's own descriptions.

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

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

The description clearly states it calculates intrinsic value using Discounted Cash Flow analysis, with specific focus on stock valuation. While the purpose is well-defined, it does not explicitly differentiate from sibling tools that might also compute valuations (e.g., 'get_analyst_ratings' or 'compare_peer_companies'). The title 'DCF Valuation | Intrinsic Value | Investment Analysis' reinforces the purpose.

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 lists concrete use cases (e.g., 'Calculate intrinsic value for Apple') and suggests when to enable sensitivity analysis. However, it provides no guidance on when NOT to use this tool or alternatives (e.g., for simple price checks, use get_quote). The usage context is implied but lacks exclusions.

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, the description carries full burden. It discloses the model (Black-Scholes) and defines the output Greeks meaningfully. However, it omits limitations (e.g., European options only) and does not mention that the tool can fetch underlying price if not provided, nor any authentication or rate limit concerns.

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 front-loaded with the main action and includes structured bullet points for Greek definitions. It is reasonably concise, though the definitions could be considered verbose; every sentence serves a purpose without 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 no output schema, the description compensates by explaining the returned Greeks. Parameter descriptions in schema are complete. The tool's context is adequately covered for a financial calculation tool, though it assumes familiarity with Black-Scholes and option types.

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

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

Schema description coverage is 100%, so the schema already explains each parameter. The description adds definitions for Greeks (outputs) but does not enhance parameter understanding beyond what the schema provides. Baseline score of 3 is appropriate.

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

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

The description clearly states the verb 'Calculate' and the resource 'option Greeks', specifying the Black-Scholes model. It lists the specific Greeks (Delta, Gamma, Theta, Vega, Rho), which uniquely identifies this tool among siblings like 'calculate_historical_volatility' or 'calculate_indicator'.

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 used for computing option Greeks with given parameters, but it does not provide explicit guidance on when to use this tool versus alternatives (e.g., 'analyze_options_strategy' might also involve Greeks). No exclusions or prerequisites are mentioned.

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 full burden. It states the output is annualized volatility percentages but omits details on calculation methodology, data source, or any side effects. Adequate but not rich.

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

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

Three concise sentences with no fluff: first states purpose, second adds context, third states output format. Every sentence earns its place.

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

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

For a tool with two parameters and no output schema, the description covers the output (annualized volatility percentages) and key usage. It could specify that results are returned per period, but overall it's sufficient given the tool's simplicity.

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

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

Schema description coverage is 100%, and the input schema already documents both parameters well. The description adds minimal extra meaning beyond 'multiple periods' and does not detail the period array format or 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 defines the tool's function: calculating historical volatility for multiple periods. It explicitly mentions the use case of comparing with implied volatility, which distinguishes it from siblings like get_implied_volatility and get_historical_data.

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 when to use the tool (comparing with implied volatility for opportunities) but does not state explicit alternatives or conditions where it should not be used.

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 must carry the full burden. It mentions the tool returns 'calculated values with timestamps and trading signals where applicable,' which gives some behavioral context. However, it does not disclose data freshness, required authentication, rate limits, or any side effects beyond being a read-only calculation.

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

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

The description is two concise sentences, front-loaded with the core purpose and resource. Every sentence adds value: first defines action and assets, second describes output. No fluff or repetition.

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

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

The tool has 5 parameters and no output schema. The description provides only a high-level overview of return values ('calculated values with timestamps and trading signals'), which is insufficient for an agent to understand the exact output structure for each indicator (e.g., fields for MACD vs RSI). Error handling and data availability are also omitted.

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

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

Schema description coverage is 100% with detailed descriptions for each parameter (e.g., defaults for period, enum values for indicator). The description adds minimal extra meaning beyond listing the supported indicators, which is already in the schema. Baseline score of 3 is appropriate.

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

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

The description clearly states the tool calculates technical indicators on price data for stocks or cryptocurrencies, and explicitly lists supported indicators (RSI, MACD, SMA, EMA, Bollinger Bands, Stochastic). This specific verb-resource pairing distinguishes it from siblings like calculate_dcf_valuation or get_historical_data.

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 no guidance on when to use this tool versus alternatives (e.g., get_historical_data for raw data, calculate_historical_volatility for volatility, etc.). There is no explicit when-to-use, when-not-to-use, or comparison to sibling tools.

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

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

No annotations are provided, and the description lacks behavioral details such as data sources, calculation methods, prerequisites (e.g., need options chain data), or side effects. The description is purely definitional.

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

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

The description is concise at three sentences, with the first sentence stating the purpose, the second defining the term, and the third providing context. No wasted words, but it could be more structured with a bulleted list.

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

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

Without an output schema, the description fails to explain what the tool returns (e.g., a single number, an object with strike price and total OI). This is a significant gap for a calculation tool.

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

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

The input schema covers both parameters with descriptions (schema coverage 100%). The description does not add any new information about parameter semantics beyond what the schema already provides.

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

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

The description states the tool calculates the max pain price for an options expiration and defines what max pain is. It clearly distinguishes from sibling tools like calculate_greeks or get_options_chain by its specific focus.

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

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

The description mentions that many traders believe prices gravitate toward max pain as expiration approaches, implying when to use it (near expiration). However, it does not explicitly say when to use this tool versus alternatives like calculate_greeks or get_options_chain, and provides no exclusions.

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 the return type (side-by-side comparison of key metrics) and limits (up to 10 companies). However, it does not mention any behavioral details like rate limits, authorization, or response structure, which is acceptable for a read-only comparison tool.

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 bullet points and use cases, front-loading the purpose. While slightly long, every section adds value for an agent.

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 only 2 parameters, no output schema, and no annotations, the description is quite complete. It covers what can be compared, use cases, and return type. It lacks output schema details, but that is acceptable.

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

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

Schema coverage is 100% with both parameters described. The description adds value by listing example metrics and categories, but the schema already explains the parameters. Baseline 3 is appropriate.

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

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

The description clearly states the tool compares key financial metrics across multiple companies, with specific metrics and use cases listed. It distinguishes from siblings like get_quote (single company) and search_ticker (ticker search).

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

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

The description provides clear use cases (e.g., 'Compare Apple, Microsoft, and Google financials') and specifies that it returns side-by-side comparison. It doesn't explicitly state when not to use it, but the context of sibling tools gives implicit 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?

No annotations provided, so description carries full burden. Clearly describes what is returned: reporting person, ownership percentage, purpose, filing dates, SEC links. Explains form types and amendments. Lacks details on rate limits or authentication, but sufficient for behavioral understanding.

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 headings and bullet points. Front-loaded with summary. Every sentence adds value, but slightly longer than necessary; could be more concise without losing clarity.

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

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

Despite no output schema, description adequately covers return values and filing types. For a tool with 4 parameters and moderate complexity, the description is complete and leaves 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?

Schema coverage is 100%, and description adds significant meaning: explains formType enum values ('13D' vs '13G' vs 'both'), activistOnly flag, and default values. Provides context beyond schema, e.g., 'activistOnly: returns only activist investors with multiple 13D filings'.

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

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

Description explicitly states it tracks major ownership changes (5%+ stakes) from SEC Schedule 13D and 13G filings. Distinguishes between 13D (activist intent) and 13G (passive ownership), and provides clear use cases. Differentiates from sibling tools like get_13f_institutional_holdings or get_insider_trades.

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 concrete use cases (e.g., 'Show me recent 13D filings', 'Who filed 13D for Tesla?'). Explains when to use each form type. Does not explicitly state when not to use, but context makes it clear.

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

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

Discloses return data (institution details, filing dates, holdings summaries) and explains compareQuarters behavior; no destructive actions indicated.

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 bullet points and sections, though somewhat lengthy; front-loaded with key 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 no output schema, description adequately covers what the tool returns, use cases, and parameter context for a moderately complex tool.

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

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

Schema coverage is 100%; description adds meaning with CIK examples and explanation of compareQuarters, enhancing 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?

Clearly states it tracks institutional investor holdings from SEC Form 13F filings, with specific examples like Berkshire Hathaway and use cases that precisely define the tool's scope.

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

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

Provides explicit use cases with CIK examples, but does not directly distinguish from sibling tools like get_13dg_ownership_changes.

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 must cover behavioral traits. It mentions filing timeliness (4 business days) and return content, but does not disclose potential rate limits, authentication needs, or any destructive actions. The return format is vague.

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 long but well-organized with sections and bullet points. It starts with a clear summary and uses whitespace effectively. Every sentence adds information, though it could be slightly tightened.

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 provides a reasonable overview of return contents. With 4 optional parameters fully documented in schema, the description covers usage scenarios and parameter values. It could mention pagination or default behaviors, but overall it is adequate.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds significant value by explaining event categories, item numbers, and their meanings, which goes beyond the schema's short descriptions. This helps the agent understand parameter semantics deeply.

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 SEC Form 8-K current reports, listing event categories and item numbers. It distinguishes itself from sibling tools like get_earnings_calendar and get_market_news by focusing exclusively on 8-K filings.

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?

Use cases are provided (e.g., 'Show me recent 8-K filings for Apple'), and the description implies when to use this tool vs. others (e.g., for 8-Ks vs. other forms). However, it lacks explicit 'when not to use' or direct alternative tool references.

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, the description carries the full burden. It details the return metrics (consensus rating, target prices, etc.) and includes notes that analysts' opinions are not guarantees. However, it does not disclose potential rate limits, data freshness, or whether the tool is read-only.

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 sections but is quite verbose. Sections like 'Why It Matters' and 'Important Notes' are somewhat extraneous for tool selection. Could be more concise while retaining key 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 absence of an output schema, the description adequately explains the returned data. Parameter count is low and well-covered. No critical information is missing for an agent to select 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 100%, so baseline is 3. The description adds value by explaining the 'symbol' parameter with examples ('AAPL', 'MSFT') and notes about availability for widely-covered US stocks. The 'symbols' parameter is also briefly described.

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: track analyst recommendations, price targets, and consensus changes. It lists key metrics and use cases, distinguishing it from sibling tools like 'get_quote' or 'get_earnings_calendar' by focusing on analyst sentiment.

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 example use cases and 'Why It Matters,' but lacks explicit guidance on when to use this tool versus alternatives. No mention of prerequisites or when not to use it, though the context is somewhat implied by the examples.

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, the description carries full burden. It lists returned fields but discloses no behavioral traits (e.g., read-only nature, data freshness, rate limits, or side effects). Basic info only.

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

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

Two sentences, front-loaded with purpose, no fluff. Every sentence adds value.

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

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

For a simple one-parameter tool with no output schema, the description adequately explains what is returned and hints at a use case. Could mention data source or update frequency, but it's near complete.

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

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

Schema coverage is 100% and the schema already describes 'symbol' as a ticker. The description does not add extra meaning beyond listing return fields, so it doesn't exceed the baseline for a well-covered 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 action 'Get' and the resource 'dividend information for a stock', listing specific fields (dividend rate, yield, ex-dividend date, payout ratio). This distinguishes it from sibling tools, none of which focus on dividends.

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

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

It provides a use case ('affects options pricing, especially for calls around ex-dividend dates') but does not explicitly state when to use or avoid this tool compared to alternatives. The guidance is implied rather than explicit.

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 full burden. It states what is returned (dates, estimates, historical results) but lacks details on pagination, error handling, or data scope (e.g., market-wide when symbol omitted). Basic transparency is present but incomplete.

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

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

Three sentences with no waste. First sentence front-loads the core purpose, second adds relevant context, third lists returns. Efficient structure.

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 2 optional parameters and no output schema, the description adequately covers the main function. It could mention that omitting symbol gives market-wide calendar, but the schema already hints at that. Overall, reasonably complete.

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

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

Input schema has 100% coverage with descriptions for both parameters. The description adds context about options volatility but does not enhance parameter meaning beyond what the schema already provides. Baseline score is appropriate.

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

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

The description clearly states it gets upcoming earnings dates and historical earnings data for a stock, using a specific verb and resource. It differentiates from sibling tools like get_financial_statements or get_historical_data by focusing specifically on earnings calendar data.

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 that earnings impact options pricing, implying use cases for options analysis, but does not explicitly state when to use this tool vs alternatives or when not to use it. No direct comparison with siblings is given.

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 the description carries full burden. It discloses the data provided, trading sessions, and important notes about liquidity and volatility. It does not mention authentication or update frequency, but covers core behavioral aspects.

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 sections but is somewhat verbose, including educational content ('Why It Matters'). It could be more concise while retaining clarity.

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 return data (price, change, volume, session indicator). Parameter coverage is complete, and the description adds context about sessions and use cases. Almost complete for agent usage.

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

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

Schema coverage is 100%, but the description adds context beyond schema: symbol description notes US stocks only, symbols parameter precedence. This adds meaningful guidance for the agent.

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 real-time pre-market and after-hours trading data for stocks. It distinguishes from siblings by focusing specifically on extended hours, and includes explicit examples and data scope.

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

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

The description includes use cases and important notes about when to use the tool (e.g., for early/late trading reactions) and limitations (lower liquidity, volatility). It does not mention alternatives among sibling tools, but the context makes the purpose clear.

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

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

Since no annotations are provided, the description carries full burden. It discloses the data source (SEC filings), return components (statements, ratios, period info, links), and includes a parameter description for limit. It does not mention destructive actions or auth, but as a read-only data retrieval tool, this is adequate. Slightly more detail on response structure could improve transparency.

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 headings and bullet points, making key information easy to scan. It is somewhat verbose but every section adds value. Front-loads the main purpose effectively.

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 comprehensive, covering all financial statements and ratios, use cases, and return components. Without an output schema, it sufficiently explains what the tool returns. Given the complexity of financial data, it is complete.

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

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

Schema coverage is 100% with each parameter described. The description adds value by explaining defaults (e.g., limit default depends on periodType) and providing context for includeRatios and periodType use cases, going beyond the schema alone.

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

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

The description clearly states it accesses financial statements from SEC filings, listing specific statements and ratios. It distinguishes from siblings like get_historical_data (price history) or get_options_chain (options data) by focusing on fundamental financial data.

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

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

The description provides clear use case examples (e.g., 'Get Apple's annual financial statements') and mentions available data types. However, it does not explicitly state when to avoid using this tool in favor of alternatives, though the sibling list and context make it implicit.

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?

Mentions support for various time intervals but does not disclose behavioral traits like rate limits, error handling for invalid symbols, or data source specifics. With no annotations, the description could provide more context on execution 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 sentences, front-loaded with core function, no unnecessary words. Every sentence adds value.

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

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

For a tool with no output schema and moderate complexity (4 parameters), the description adequately covers the purpose and usage scope. Could mention return format or data adjustments for completeness, but not lacking.

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

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

Schema coverage is 100%, so description adds no new meaning beyond what is already provided in the parameter descriptions. The interval summary is useful but not additive beyond the schema.

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

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

Clearly states it retrieves historical OHLCV price data for stocks or cryptocurrencies, specifying the resource and action. Distinguishes from siblings like get_quote (current price) and get_extended_hours_data by focusing on historical data.

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?

Indicates utility for charting and technical analysis, providing a clear use case. However, it does not explicitly state when not to use this tool (e.g., for current prices) or mention sibling 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 provided, so description carries full burden. It discloses that the tool returns current IV, IV by expiration, and comparison with historical volatility, which informs the agent of output content. However, it does not mention any side effects or permission requirements.

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

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

Three sentences, front-loaded with purpose, no wasted words. Efficient and clear.

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

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

For a simple tool with one parameter and no output schema, the description adequately explains the concept of IV, its use, and what data is returned. It covers the essential information, though it could be slightly more detailed about the output structure.

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?

Only one parameter (symbol) with 100% schema coverage. The schema already describes it as a stock ticker symbol, and the description adds no additional meaning or constraints beyond that.

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 the tool gets implied volatility data for a stock, specifying the verb (get) and resource (IV data). Distinguishes from sibling tools like calculate_historical_volatility.

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?

Mentions comparing IV to historical volatility to identify high/low volatility environments, but does not explicitly state when to use this tool versus siblings like calculate_historical_volatility or calculate_greeks, nor does it provide when-not 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?

No annotations are provided, so the description carries full behavioral burden. It accurately describes real-time access to EDGAR, the two modes, and returned fields. It does not disclose limitations like rate limits or data freshness, but overall transparency is good.

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 somewhat lengthy but well-structured with headings, bullet points, and examples. It front-loads the primary purpose and uses emojis for emphasis. Every section serves a purpose, but slight trimming could improve conciseness.

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

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

Given no output schema, the description adequately explains return values (insider names, positions, etc.). It covers both modes, all 6 parameters with enums, and provides sufficient context for an AI agent to use the tool effectively.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds significant value by explaining parameter usage with examples (e.g., relative dates for startDate, mode switching via symbol presence), justifying a higher score.

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

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

The description explicitly states the tool is for SEC Form 4 insider trading filings, distinguishes two operating modes (company-specific vs market-wide), and lists emitted data types. The verb 'retrieve' with resource 'SEC Form 4 data' is specific and differentiates from sibling tools like get_sec_form4_filings.

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

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

The description provides clear trigger keywords and example queries, making it obvious when to invoke this tool. However, it does not explicitly mention when not to use it or suggest alternative tools for related tasks, which would elevate the score to 5.

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

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

No annotations are provided, so the description carries the full burden. It discloses the extensive data returned (categorized news, fundamentals, ratings, events, ownership, insider summary, short interest, options activity, sector context) and notes a limitation (use get_insider_trades for detailed Form 4).

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 opening and bullet points for the comprehensive view. It is slightly lengthy but every sentence adds value. The front-loading of the main purpose is effective.

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 and no annotations, the description is remarkably thorough. It covers what the tool does, when to use each mode, parameter details, and even references a sibling tool. For a tool with 3 parameters and moderate complexity, it is fully complete.

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

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

Schema coverage is 100%, but the description adds meaning beyond schema: it explains default behavior (comprehensive defaults true if symbol provided, limit defaults differ), and clarifies the purpose of each parameter beyond their schema descriptions.

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

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

The description clearly states it retrieves market intelligence for a ticker symbol or general market news, distinguishing between comprehensive and basic modes. It lists specific content categories and differentiates from sibling tool get_insider_trades.

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 when to use comprehensive (with symbol) vs basic (comprehensive=false) and directs users to get_insider_trades for detailed SEC filings. However, it does not explicitly contrast with other news-related siblings like analyze_news_impact or get_analyst_ratings.

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. Lists output fields but fails to disclose behavioral traits like rate limits, real-time vs delayed data, potential size of response, or any side effects. Lacks depth for a read operation.

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

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

Two sentences, front-loaded with core purpose and data fields. Second sentence adds context without redundancy. No wasted words; highly efficient.

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

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

Given no output schema, description adequately lists returned fields (strikes, premiums, etc.). Could mention potential size or pagination, but covers essential elements for agent to select tool. Sibling tools are analytical, so this tool's role is clear.

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

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

Schema coverage is 100% with both parameters described. Description does not add extra meaning beyond schema: it omits details on expirationDate format or default behavior. Baseline 3 as schema already covers parameters.

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

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

Description uses specific verb 'Get' and resource 'options chain data', listing exact fields (calls/puts, strikes, premiums, volume, open interest, implied volatility). Clearly distinguishes from sibling tools like get_implied_volatility or calculate_greeks which focus on derived 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?

States 'Useful for analyzing available options contracts and their prices', implying use for raw data retrieval. However, lacks explicit guidance on when to use versus siblings (e.g., for further calculations) and no when-not-to-use conditions 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?

With no annotations, the description carries full burden. It discloses real-time nature and return fields (price, change, volume, market cap), but leaves 'other market data' unspecified and omits limitations or prerequisites.

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 a single focused sentence listing purpose, return data, and examples. Every word adds value, no redundancy.

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

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

Given no output schema, the description only vaguely lists return fields. It doesn't specify structure or units. Sibling differentiation is weak. Adequate but not complete.

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

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

Input schema covers both parameters with descriptions. The description adds examples and hints but doesn't go beyond schema. Baseline 3 is appropriate.

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

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

The description clearly states it gets real-time price quotes for stocks or crypto, with examples. However, it does not explicitly distinguish from sibling tools like get_quote_batch, though the singular nature and examples imply single-tick usage.

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 such as get_quote_batch or get_historical_data. No exclusions or context for use.

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

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

No annotations are provided, so the description carries the burden. It mentions error handling for invalid symbols but does not disclose rate limits, data freshness, or authentication requirements, though it is a read-only operation.

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

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

Two concise sentences: first states purpose and efficiency, second mentions return behavior and error handling. 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 one parameter, no output schema, and no annotations, the description sufficiently covers the tool's purpose, batch scope, and error handling. Could optionally mention output format, but not necessary.

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

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

Schema description coverage is 100%, with the parameter description already indicating an array of ticker symbols with a maximum of 50. The tool description adds no significant semantics beyond 'efficiently in a single request', so baseline 3 is appropriate.

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

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

The description clearly states the verb 'get' and the resource 'real-time quotes for multiple stock or cryptocurrency tickers', and distinguishes it from sibling tools like 'get_quote' (single) and 'search_ticker' by emphasizing batch efficiency.

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 using this tool for batch requests without specifying explicit when-not-to-use or alternatives, but the sibling list provides context for single-ticker queries.

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 full burden. It discloses the tool retrieves real data from SEC.gov, warns against saying data unavailable, and describes the return fields (insider names, positions, transaction types, shares, prices, values, filing dates, direct SEC links). No destructive behavior is mentioned, which is appropriate for a read-only tool.

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 lengthy and contains redundant elements like emojis, all-caps warnings, and extensive examples. While structured with sections, it could be more concise by trimming the 'PRIMARY TOOL' list and example queries without losing clarity.

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

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

Despite no output schema, the description explicitly lists the return fields (insider names, positions, transaction types, shares, prices, values, filing dates, direct SEC links). It covers both operating modes and all parameters thoroughly, making it well-rounded for a read-only tool.

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

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

Schema coverage is 100%, but the description adds significant contextual meaning beyond the schema. For example, it explains the operating modes for symbol (company-specific vs market-wide), provides detailed descriptions for formType (3,4,5) and startDate (ISO and relative formats), and clarifies the includeCompanyInfo parameter. This is above the baseline of 3.

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 SEC Form 4 insider trading filings from EDGAR, specifies two operating modes (company-specific vs market-wide), and uses specific verbs like 'get' and 'returns'. It distinguishes itself as the primary tool for Form 4 queries, providing clarity on its scope.

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

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

The description provides explicit usage guidance with example queries and lists what queries it is the primary tool for. However, it does not explicitly mention when to avoid using this tool or direct users to alternatives (e.g., get_insider_trades sibling) for potentially overlapping functionality.

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 full burden. It discloses the key metrics returned (short ratio, % of float, shares short, change, squeeze risk score) and explains the risk score thresholds. It does not mention destructive aspects (none exist) but could note data availability constraints (e.g., US stocks only, mentioned in schema but not description).

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 excessively long with educational content (e.g., famous short squeezes, why it matters) that is not needed for tool invocation. It could be trimmed to 2-3 sentences plus a list of return fields. The first sentence is effective, but the rest is verbose.

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

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

Given the tool has no output schema, the description adequately explains the return fields and provides context for the squeeze risk score. It covers what an agent needs to understand the output, though it could mention the data source or update frequency.

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

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

Schema coverage is 100% (both parameters have descriptions). The description does not add much beyond the schema; it explains the meaning of output metrics but not parameter-specific details. Baseline of 3 is appropriate given high 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 it tracks short interest metrics like short ratio, days to cover, and squeeze potential. The first line effectively conveys the tool's purpose. However, it does not explicitly differentiate from siblings, but the function is distinct enough.

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 example use cases (e.g., 'What's the short interest for Tesla?') that imply when to use it. However, it does not offer exclusions or comparisons to sibling tools, which would help an agent decide between this and tools like get_quote or get_historical_data.

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 burden. It clearly indicates a read/search operation with no side effects, stating 'Returns matching tickers...' which implies idempotency. 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 two sentences with no wasted words. It front-loads the primary action and immediately conveys the tool's value. Every sentence earns its place.

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

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

Given the simplicity of the tool (no output schema, no nested objects), the description fully explains what it does, how to use it, and what to expect in the response. It is complete for an agent to select and invoke correctly.

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

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

The schema covers 100% of parameters, and the description adds value by explaining the query parameter accepts 'company name, keyword, or partial symbol' and describes the output beyond the schema. This enriches the semantic 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 the tool's purpose: 'Search for stock or cryptocurrency ticker symbols' and specifies input types (company name, keyword, partial symbol) and output details. It effectively distinguishes from sibling tools like get_quote which operate on known tickers.

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 the tool is 'useful for finding the correct ticker symbol when you know the company name,' providing clear context for when to use it. While it doesn't explicitly exclude other scenarios, the sibling tools naturally cover alternatives.

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