MoneyChoice

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

Annotations already declare readOnly, idempotent, non-destructive, so the description adds meaningful context by explaining optional authentication and free vs Pro tier access. No contradiction, though rate limits and error behavior are not mentioned, which is minor given annotations.

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

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

Two short sentences: the first states the tool's purpose, the second gives authentication details. No wasted words, well front-loaded, and easy to scan.

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 2 enum parameters and an output schema, the description covers purpose and auth but misses parameter semantics, especially timeframe and the gold indicator (which appears only in the enum, not the prose). The gold omission and 'volatlity_gold' typo are unaddressed, leaving ambiguity about full coverage.

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

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

Schema description coverage is 0%, so the description carries the burden, but it only lists commodities in prose and never explains the indicator enum values or the timeframe parameter. It fails to distinguish the two crude oil options, the gold typo, or how timeframe affects outputs.

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?

States it provides macro forecasts for specific commodities (crude oil, natural gas, aluminium, and volatility), which clearly maps to the indicator enum and distinguishes it from sibling forecast_* tools. The verb and resource list are explicit and specific.

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?

Description implies usage when commodity forecasts are needed, but provides no explicit when-to-use or when-not-to-use guidance, and no alternatives are mentioned. The authentication details are operational rather than selection-oriented.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is known. The description adds useful behavioral context by documenting the optional API key via headers or query parameter and its effect of selecting Pro automatically when valid, which goes beyond the structured annotations.

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

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

The description is two compact sentences with no filler. The first sentence front-loads the exact scope, and the second sentence adds the API-key/Pro behavior. Every word 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 simple two-parameter enum-driven tool with an output schema and safety annotations, the description plus schema covers what an agent needs to select and invoke it. The only minor gap is that it doesn't clarify what the returned forecast structure contains, but the presence of an output schema reduces the need for that.

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

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

Schema description coverage is 0%, so the description must compensate. It does help by translating enum-like indicator values into readable series (e.g., USD/EUR, dollar index, euro FX vol), but it doesn't explicitly document the timeframe parameter or the exact mapping between enum values and series beyond the names. The enum values themselves are partially self-explanatory, keeping this minimally adequate.

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 resource as 'USD FX and volatility forecasts' and enumerates the specific series covered: USD/EUR, USD/AUD, USD/CNY, dollar index, and euro FX vol. This is specific enough to distinguish it from sibling forecast_commodities, forecast_equities, and similar tools.

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

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

The description conveys that this tool is for currency and FX-volatility forecasts by naming actual currency pairs and the dollar index. It doesn't explicitly state 'use this instead of forecast_commodities', but the asset-class focus gives clear context, and no exclusions are needed given the sibling names.

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

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

Annotations already mark this as read-only, idempotent, and non-destructive. The description adds important behavior beyond that: forecasts are model-based rather than live, and an optional API key can select Pro mode. It could disclose more about update frequency or data sources, but it adds meaningful behavioral context.

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 compact sentences, front-loaded with the core purpose and followed by the authentication note. Every sentence adds value and there is no filler.

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 only two parameters, enumerated values, a read-only/idempotent annotation profile, and an output schema, this description is complete enough for an agent to select and invoke it correctly. It covers the asset scope, the non-live nature of the data, and Pro-mode access behavior.

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

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

Schema description coverage is 0%, so the description must compensate. It names S&P 500, VIX, and Russell 2000, which maps well to the indicator enum, and mentions the optional API key behavior. However, it does not explicitly explain the timeframe parameter or its default, leaving some burden on the schema's self-describing enums.

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 specifies a clear resource (US equity outlook) and names the exact indicators: S&P 500, VIX, and Russell 2000 volatility. It also distinguishes itself from sibling tools by emphasizing model-based forecasts rather than live prices.

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 clear context for use: US equity forecasting, not live market price queries. It does not explicitly name sibling alternatives or say when not to use it, so it stops short of full routing guidance.

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

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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds valuable context: data sources (Federal Reserve banks), output format (annualized %), and authentication requirements. No contradictions with annotations.

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

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

Two well-structured sentences. The first sentence front-loads the core purpose and data sources; the second provides authentication details. No wasted words 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?

Appropriately complete for a simple 2-parameter tool with rich annotations and an output schema. Documents the critical 'indicator' parameter and auth mechanism. The missing 'timeframe' documentation is a minor gap given the parameter has an intuitive name and default value, though it should ideally be mentioned given 0% schema coverage.

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

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

With 0% schema description coverage, the description must compensate. It effectively documents the 'indicator' parameter by mapping the cryptic enum values to human-readable 'Atlanta Fed nowcast' and 'St. Louis Fed real GDP nowcast'. However, it completely omits the 'timeframe' parameter (monthly/quarterly/yearly) and its default value.

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 identifies the resource (US GDP growth) and specific data sources (Atlanta Fed nowcast, St. Louis Fed real GDP nowcast). However, it does not explicitly distinguish when to use this tool versus sibling forecast tools like forecast_inflation or forecast_labor.

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 authentication guidance (optional API key via headers or query parameter for Pro access), but lacks explicit guidance on when to select this tool over other forecast siblings and does not explain when to use each indicator option (Atlanta Fed vs St. Louis Fed).

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

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

Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds a concrete behavioral detail beyond annotations: the API key can be supplied via headers or a query parameter and selects Pro when valid, which affects authentication and result quality.

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 short sentences with no filler; the domain is stated first and the API key note is kept separate. It is efficient, though slightly fragmentary and could have used the space to add usage guidance.

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 an output schema and safety annotations, the description covers read-only behavior and an auth detail, but it omits when to use the tool, what the timeframe values mean, and how indicators map to specific enum choices. An agent would still need to inspect enums and infer behavior.

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

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

Schema description coverage is 0%, so the description must compensate, but it only loosely maps to the indicator enum (CPI, PPI, expectations, breakevens) and says nothing about the timeframe parameter. It helps categorize indicators but does not define individual enum values or timeframe semantics.

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 identifies the tool as covering inflation-related indicators such as CPI, PPI, expectations, and breakevens, which clearly distinguishes it from sibling forecast tools. It lacks an explicit verb like 'returns' or 'forecasts', but the tool name and title supply that context.

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 given about when to use this tool over forecast_rates, forecast_gdp, or other siblings. The only operational note is about the optional API key, which is not a usage-selection guideline.

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?

Read-only, idempotent, non-destructive traits are already in annotations. The description usefully adds that an optional API key in headers or query string promotes to Pro when valid, which is an auth-behavior detail not visible in the schema.

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 short sentences; the first is front-loaded with the domain and coverage, the second adds the only non-obvious operational detail. 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 a rich input schema (enums and defaults), output schema present, and annotations covering safety, the description supplies the remaining essential context: geographic scope and auth upgrade path. The absence of sibling-selection guidance is a minor gap given the overlapping forecast_manufacturing 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 schema has 0% coverage but the description paraphrases all five indicator enum values (unemployment, participation, EPOP, manufacturing/finance payrolls). It does not explicitly mention the timeframe parameter or its default/monthly options, though those are self-explanatory enums.

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 identifies the resource as US labor-market data and enumerates the covered series (unemployment, participation, EPOP, payrolls), which distinguishes it from sibling forecast tools by domain. It lacks an explicit verb like 'retrieves' or 'forecasts', but the noun-phrase style is 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?

There is no statement about when to choose this tool over siblings; e.g., employees_in_manufacturing could overlap with forecast_manufacturing. It neither gives decision criteria nor 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?

Annotations already establish read-only, idempotent, and non-destructive behavior, so the description is not the sole carrier. It adds a concrete access detail—optional API key via headers or query parameter selects Pro when valid—which is useful behavioral context beyond the schema. It does not over-explain return semantics, which are covered by the output schema.

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 short sentences with no filler: the first identifies domain and valid indicators, the second gives the API-key behavior. Every sentence earns its place and is front-loaded.

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

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

For a simple two-parameter tool with a rich output schema and safety annotations, the description plus schema is mostly sufficient. The main residual gaps are the lack of explicit usage guidance and unmentioned timeframe semantics, but the schema's enums and default mitigate those.

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

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

Schema description coverage is 0%, so the description must compensate. It makes the three indicator enum values more readable, but it says nothing about 'timeframe' or how the default works. The second sentence is about API auth, not parameter meaning, leaving one of two parameters effectively undocumented in prose.

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

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

The description opens with the domain 'Manufacturing:' and enumerates the exact indicator set (durable goods, new orders, industrial production), making the resource clear. It lacks an explicit verb like 'fetches' or 'forecasts', but the tool name and title supply that, and the list differentiates it from the other forecast_* siblings.

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 given on when to choose this tool over forecast_commodities, forecast_gdp, or other siblings; the sibling names are the only signal. There are also no exclusions or criteria such as 'use for US manufacturing data only'.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=false, covering safety and side-effect profile. The description adds the valuable behavioral detail about the optional API key and Pro access authentication, which goes beyond the annotations. No contradiction with annotations.

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

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

Two sentences packed with information: the first lists the covered rates, the second covers authentication. No filler or redundancy. The content is front-loaded with the most important info (what it covers) before the accessory API key detail. 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 read-only forecast tool with enumerated params and an output schema present, the description covers the scope of data (rates) and the special API key behavior. It does not explicitly mention that the tool returns forecasts over time, but the title and name imply that, and the output schema likely describes the return structure. It is adequate for a tool of this simplicity, though a note about the meaning of the timeframe parameter would improve 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?

Schema description coverage is 0%, so the description must compensate. The description summarizes the indicator enum by listing categories (e.g., 'Fed funds, Treasuries, SOFR'), which partially explains the indicator parameter. However, it provides no explanation for the 'timeframe' parameter (monthly/quarterly/yearly) beyond what the schema enum shows. Since half the parameters are left without additional meaning, and even the indicator is only summarized not mapped to exact enum values, the compensation is insufficient.

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 names specific rate categories (Fed funds, Treasuries, SOFR, CP, prime, yield spreads) and clearly differentiates from sibling forecast tools like forecast_commodities or forecast_equities. It does not explicitly say 'forecast' but the tool name and title convey that. It is specific to interest rates, so an agent can tell it apart from the other forecast_* siblings.

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 interest rate related queries by listing the covered instruments, but it does not explicitly state when to use this tool versus other forecast tools, nor does it mention any exclusions (e.g., 'for commodities use forecast_commodities'). The context is clear from the list, but there is no direct guidance 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?

Annotations already disclose readOnly, idempotent, and non-destructive behavior. The description adds useful authentication context: an optional API key selects Pro when valid. It does not describe return format or any other behavioral traits, but given the annotation coverage, the addition is enough to justify a mid score.

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 compact: a front-loaded domain summary plus a necessary note about the Pro API key. There is no filler or redundancy. It loses a point because the first sentence is a fragment rather than a complete, self-explanatory statement.

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 two-parameter tool with an output schema and safety annotations, the description covers domain and authentication but misses key context: what the output actually represents (forecast vs. historical data), how timeframe changes results, and when to prefer sibling tools. An agent could call the tool from schema alone, but the description leaves too much to assumption.

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

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

Schema description coverage is 0%, so the description must compensate. It partially does by listing human-readable indicator names, which map to the indicator enum. However, it never mentions the timeframe parameter or explains how it affects results. The enum values are self-explanatory, but the description fails to provide explicit parameter guidance.

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 domain (real estate) and lists the specific data series: Case-Shiller, supply, permits, and 30-year mortgage rate. This distinguishes it from sibling forecast_* tools. However, it lacks an explicit verb such as 'retrieves' or 'forecasts,' so the action is only implied by the tool name and title.

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 housing-related indicators by listing those categories, but it never explicitly states when to use this tool versus alternatives like forecast_commodities or forecast_rates. There is no 'when not to use' or mention of sibling tools, so an agent must infer the selection from the domain list.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful auth behavior ('Optional API key via headers or `?api_key=` selects Pro when valid'), but it does not describe other behavioral details like data freshness, coverage limits, or response behavior. This is acceptable given the strong annotations.

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

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

The description is very brief and front-loads the core data content, followed by a concise auth note. It wastes no words, though the first sentence is a noun fragment rather than a complete statement of action.

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

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

With an output schema present, return-value documentation is not necessary. The description covers data domains and auth, and the simple enum-based schema makes the call constructible. However, the missing explicit purpose and absence of sibling differentiation leave some contextual gaps for an agent deciding whether to invoke this 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 description coverage is 0%, so the description must compensate, but it only loosely maps the indicator enum to 'Retail sales' and 'University of Michigan consumer sentiment.' It adds no meaning for the 'timeframe' parameter and does not explain the values beyond what the enum already shows.

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 names the data domains ('Retail sales and University of Michigan consumer sentiment') but lacks a verb or explicit statement that this tool returns forecasts. It is not a tautology, but it is more of a content list than a clear purpose, and it relies on the tool name/title to imply 'forecast'.

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 the sibling forecast tools. It never mentions alternatives or exclusion conditions. The only usage-related note is about the optional API key, which concerns authentication, not tool 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?

Adds significant value beyond annotations by disclosing rate limiting (abuse controls), Pro-only access restrictions, authentication requirements (internal_key, a2a_internal_api_key), and detailed response structure (interpretation paragraph format with specific metrics like vol_pri_angle_degree).

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?

Front-loaded with clear purpose statement. However, second paragraph is dense with implementation-specific references (analyze_std_dev, vol_pri_angle_degree) that reduce readability. Abuse control details, while useful, could be more concise.

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

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

Given the presence of an output schema, the description appropriately focuses on operational constraints (auth, rate limits) and response formatting rather than return value details. Covers the dual-mode response behavior adequately.

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

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

With 0% schema description coverage, the description must document all 3 parameters but only thoroughly explains `include_interpretation`. The `limit_per_table` parameter is completely omitted, and `symbol` is only implied by 'one ticker' without explicit parameter documentation.

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?

Opens with specific verb 'Return' and resource 'MoneyChoice aggregate stock analysis', clearly scoped to 'one ticker'. Distinct from sibling 'forecast_*' tools by focusing on aggregate analysis rather than future predictions.

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

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

Provides clear guidance on when to set `include_interpretation` true (default) versus false for JSON-only output. However, lacks explicit comparison to sibling forecast tools regarding when to choose analysis over forecasting.

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