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 does not disclose return format, side effects, data sensitivity, or operational behavior (e.g., read-only nature, output structure). For a tool that performs analysis, this is a significant gap.

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, concise sentence with no redundant information. It is front-loaded with the verb and resource, making it easy to parse.

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

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

Given the tool's simplicity (one parameter), the description is minimally adequate. However, with no output schema and no behavioral notes, the agent cannot know what the analysis returns or how to interpret results, leaving a notable gap in completeness.

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 fully documents the 'holdings' parameter, so schema coverage is 100%. The description adds no additional meaning beyond the schema; it simply restates the overall purpose. 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 a specific verb ('Analyze') and resource ('sector and asset class diversification of a given stock portfolio'). This distinguishes it from siblings like get_risk_assessment (risk) or generate_investment_report (broader report).

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, nor any exclusions or prerequisites. The agent must infer usage solely from the name and description, which is ambiguous relative to related 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?

Annotations are absent, so the description carries the full burden for disclosing behavior. The description only states 'basic arithmetic calculations' and gives no information about side effects, safety, exact operation handling (e.g., division by zero), return format, or any other behavioral trait. This minimal disclosure is inadequate for a tool with no structured annotation support.

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 sentence that directly states the tool's purpose without any wasted words. It is highly concise and front-loaded, though it borders on being under-specified. One sentence earns its place, but it could be expanded without becoming 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 low complexity (3 simple parameters) and full schema coverage, the description might seem adequate at first glance. However, with no annotations and no output schema, the description fails to explain return values or provide usage context. It also doesn't distinguish this generic 'calculate' from the more specialized financial calculation siblings, making the overall description incomplete for an agent to correctly select and invoke 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?

The input schema provides descriptions for all three parameters (a, b, operation) with a full enum for operation, so schema coverage is 100%. The tool description adds no extra meaning beyond what the schema already states. Per the calibration baseline, a score of 3 is appropriate when the schema does the heavy lifting.

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 uses a clear verb ('Perform') and resource ('basic arithmetic calculations'), so the primary action is understandable. However, it doesn't differentiate from sibling tools like 'calculate_financial_ratios' or 'convert_temperature', both of which are also calculation-type tools. Thus, it meets the 'clear but no sibling differentiation' criterion.

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. There is no mention of suitable scenarios, exclusions, or relationship to sibling tools. Since no context is given beyond the one-sentence purpose, the agent is left without direction on selection.

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

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

With no annotations provided, the description carries the full burden for behavioral disclosure. It does not mention data sources, time periods, or any limitations. While 'calculate and retrieve' implies a read-only operation, it adds no context beyond the basic action.

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, front-loaded sentence with no redundant information. It efficiently conveys the core function and gives concrete examples, making it highly concise 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?

The tool is simple with one parameter and no output schema. The description covers the essential output (ratios) and gives examples, which is sufficient for basic understanding. However, it lacks details on return format or use context, slightly reducing completeness.

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 fully describes the only parameter (`ticker` as stock ticker symbol), achieving 100% schema coverage. The description adds no additional meaning or format details, so the baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Calculate and retrieve key financial ratios'. It provides specific examples (P/E, Debt-to-Equity, ROE) that distinguish it from sibling tools like get_financial_statements or run_dcf_valuation. The verb+resource combination is specific and unambiguous.

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 offers no guidance on when to use this tool vs. alternatives. There is no mention of prerequisites, scenarios, or exclusion of other tools. A user would have to infer usage from the name and examples alone.

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 behavioral disclosure burden. It only says 'based on file content or direct input' but does not explain how the file is processed, whether the file fields are mandatory, what the output format is, or any side effects. This leaves significant behavioral unknowns.

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, efficient sentence followed by a short clarification. Every word contributes to the core purpose, and it is front-loaded with the action. It is appropriately sized for a simple conversion 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?

The tool has six parameters and two execution modes, yet the description gives no information about return values, input mode prerequisites, or edge cases. The absence of an output schema further increases the need for descriptive context, and this description falls short.

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 description coverage is 100%, so the baseline is 3. The description adds the concept of two input modes (file and direct) but does not clarify how the parameters interrelate (e.g., which fields to provide for direct input, or that file_content is injected). It provides marginal added value over 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?

The description states a specific action ('Convert temperature units') and names the supported units (Celsius/Fahrenheit). This clearly distinguishes it from sibling tools like calculate or get_stock_price. The mention of 'file content or direct input' adds scope but doesn't detract from clarity.

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 choose this tool over alternatives like calculate, nor does it explain when to use direct input versus file content. It simply describes the conversion function without any contextual cues or 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 provided, the description carries the full burden of behavioral disclosure. It merely states that an email is sent, without detailing any side effects, permissions required, delivery guarantees, or error behavior. For a side-effecting action like sending email, this is a significant gap.

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, concise sentence with no redundancy, front-loading the verb and core purpose. Every word earns its place, making it 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?

For a simple tool with fully documented parameters, the description is minimally viable. However, it omits any context about prerequisites (e.g., whether a report must already exist), connection to sibling tools, or expected outcomes, which would be helpful given the absence of annotations and output 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?

The input schema already describes all three parameters (ticker, emailAddress, reportContent) with 100% coverage. The description adds no additional parameter-level meaning, so it meets the baseline but does not enhance understanding 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?

The description clearly states the action ('Emails') and the object ('an investment report'), with a recipient address specified. This unambiguously distinguishes it from sibling tools like generate_investment_report, which creates reports rather than sends them.

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 offers no guidance on when to use this tool versus alternatives. It does not mention that a report should be generated first (e.g., via generate_investment_report) or any scenario where this tool is the appropriate choice, leaving the agent to infer usage entirely.

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 must disclose behavioral traits. It states the tool aggregates findings but doesn't specify whether it is a pure function, persistence side effects, authentication requirements, or output format. This lack of context is a significant gap for a generation 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 a single, focused sentence without extraneous words. It's appropriately concise, though it could include more detail without becoming bloated.

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 simple parameters and full schema coverage, but no output schema and no annotations. The description leaves ambiguity about what the report contains or how it's delivered. For a low-complexity tool, it's minimally 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?

Schema description coverage is 100%, so the baseline is 3. The description doesn't add any parameter-specific meaning beyond what the schema already provides, but it doesn't need to given full 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 uses a specific verb 'aggregates' and resource 'AI investment report,' clearly indicating the tool creates a report. However, it doesn't differentiate from siblings like email_investment_report or orchestrate_financial_analysis, so it lacks sibling distinction.

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 is provided about when to use this tool versus the sibling tools. The description doesn't mention prerequisites, alternative tools, or 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 of behavioral disclosure. The word 'Retrieve' implies a read-only operation, but the description does not disclose return format, authentication needs, rate limits, or any other behavioral traits beyond the basic action.

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?

A single, front-loaded sentence with no filler or repetition. Every word contributes to conveying the tool's core function.

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 adequate for a simple retrieval tool, and the schema fully documents parameters. However, without annotations or an output schema, and with no usage guidance or behavioral context, the description is not fully complete for an agent to confidently invoke it in all situations.

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 100% description coverage for all parameters, including enum values for statementType and period. The description adds no additional parameter meaning beyond what the schema already provides, so the baseline score of 3 applies.

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 uses the specific verb 'Retrieve' and names the exact resources (income statement, balance sheet, cash flow statement) for a company, clearly distinguishing it from sibling tools like get_stock_price or calculate_financial_ratios.

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. It doesn't mention exclusions or contrast with siblings such as calculate_financial_ratios or get_market_news, 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states that news articles are retrieved; it does not mention whether results include titles, full text, publication dates, rate limits, or whether 'real' implies live data vs. simulated. This is a minimal but not fully transparent 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 a single concise sentence that fully communicates the core action and target resource without unnecessary words or redundancy. It is front-loaded and easy to parse.

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 nested objects or output schema, the description is mostly complete. It clearly indicates the result (news articles) and the input (ticker), but it could benefit from a note on the output format or time sensitivity. However, the low complexity makes this an acceptable level of completeness.

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 100% description coverage, with the ticker parameter clearly described as 'The stock ticker symbol to filter news by.' The description's mention of 'specific stock' aligns with the schema but adds no additional meaning beyond what the schema already provides. 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 retrieves market news articles filtered by a specific stock ticker. The verb 'Retrieve' with resource 'real market news articles' and scope 'for a specific stock' makes the purpose unambiguous and distinguishes it from sibling tools like get_stock_price or get_financial_statements.

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

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

The description implies usage when needing news for a particular stock (via ticker), but provides no explicit guidance on when to prefer this tool over alternatives or when not to use it. There are no exclusions or comparison to sibling news-related tools (if any), so usage context is only implied.

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 responsibility for disclosing behavior. It only mentions 'live' and 'beta,' but fails to state whether the operation is read-only, what data sources are used, or how the risk profile is computed. This leaves significant unknowns for an agent.

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, front-loaded sentence with no filler. Every word contributes to conveying the core action and scope, making it 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 the absence of both annotations and an output schema, the description alone should explain what the risk profile contains. It only mentions beta, leaving other potential metrics unspecified. The tool appears simple, but the description is too sparse for an agent to fully understand what will be returned.

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 parameter 'ticker' is already described as 'The stock ticker symbol.' The description adds no extra semantic meaning beyond calling the target a 'company,' which does not materially enhance understanding of the parameter.

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 uses a specific verb ('Assess') and resource ('live risk profile of a company') and further specifies a key output ('including beta'). This clearly distinguishes it from sibling tools like get_stock_price or calculate_financial_ratios, which focus on different 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?

No guidance is provided on when to use this tool versus alternatives. The word 'live' implies real-time context, but there is no explicit statement about suitability, prerequisites, or exclusion of other tools such as calculate_financial_ratios or run_dcf_valuation.

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 convey behavioral traits. The verb 'retrieve' implies a read-only operation, but it doesn't disclose data freshness, return format, or any rate limits. It adds some context about real-time and historical data but remains minimal.

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, clear sentence that immediately states the action and scope. No filler or redundant information.

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

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

Given the simple tool with two parameters and no output schema, the description covers the core purpose but omits mention of the period parameter and the exact return data. This leaves some ambiguity about what 'historical data' includes, making it slightly incomplete.

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 already documents both parameters (ticker and period) with descriptions and an enum for period, achieving 100% coverage. The description adds no additional meaning beyond mentioning the ticker symbol, so it relies fully on 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?

The description uses a specific verb 'retrieve' and names the resource 'current real-time stock price and historical data' along with the input 'ticker symbol', clearly distinguishing it from sibling tools like get_market_news or get_risk_assessment.

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 explicit when-to-use or alternatives are given; usage is implied by the description. It doesn't contrast with get_financial_statements or run_dcf_valuation, leaving the agent to infer when to select this tool.

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 fully disclose behavioral traits. It only mentions coordination of multiple agents, but does not state whether the operation is read-only, describe potential side effects, error handling, or output format. For an orchestration tool, this is insufficient transparency without annotation safety hints.

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 sentence, front-loaded with the key verb 'Coordinates,' and contains no redundant or extraneous information. It is highly concise 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?

Given the tool's orchestration complexity, the description is under-specified: it lacks details on expected output (though 'unified analysis' hints at it), potential latency, failure modes, or whether it is appropriate for all analysis requests. The absence of an output schema further increases the need for descriptive completeness, which is not met.

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 both query and ticker fully described. The tool description adds no additional parameter-specific meaning beyond contextualizing that these inputs feed a multi-agent analysis, so the baseline 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 specifies the tool's function: coordinating multiple analytical agents (Market Data, Financials, Valuation) to produce a unified stock analysis. This distinguishes it from sibling tools that perform individual lookups or report generation, establishing a unique 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 implies usage for comprehensive analysis requests ('coordinates multiple agents... produce a unified analysis'), but it does not explicitly state when to use this tool versus alternatives. No exclusions or when-not-to-use guidance is provided, leaving the conditions for selection somewhat 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?

With no annotations provided, the description carries the full burden of disclosing behavioral traits. It mentions 'live free cash flows' which suggests dynamic data retrieval, but it doesn't disclose potential side effects, data source dependencies, error handling, or whether it's a pure read operation. The agent is left with significant uncertainty.

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 sentence that front-loads the action and stays focused on the core purpose. Every word is meaningful, and there is no redundant or extraneous text.

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

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

The tool has no output schema and no annotations, leaving the description as the only source of context. It never states what the tool returns, what happens if optional parameters are omitted, or what assumptions are baked into the model. This is insufficient for a financial modeling tool with three parameters.

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%: each parameter has a description (e.g., growthRate: 'Projected annual growth rate (percentage)'). The tool description adds no additional meaning beyond the schema, so the baseline of 3 applies. It does not clarify format nuances like whether percentages are expressed as decimals or whole numbers.

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 identifies the tool's function: running a Discounted Cash Flow model to estimate intrinsic value using live free cash flows. This specific verb-resource combination distinguishes it from sibling analytical tools like get_stock_price or calculate_financial_ratios.

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 gives no explicit guidance on when to use this tool versus alternatives. It does not mention when not to use it, prerequisites, or how it fits into an investment analysis workflow. While the purpose is clear, the lack of comparative context leaves the agent to infer usage.

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