MoneyChoice

by prmail

Overview Schema Related Servers Score Discussions

Python

Remote

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Every tool has a clearly distinct purpose targeting specific economic or financial domains: commodities, currency, equities, GDP, inflation, labor, manufacturing, rates, real estate, retail, and stock analysis. There is no overlap in functionality—each tool forecasts or analyzes a different market segment, making tool selection straightforward for an agent.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with 'forecast_' prefix for 10 tools and 'stock_analysis_' for the 11th. The naming is uniform (snake_case throughout) and predictable, clearly indicating the action (forecast/analysis) and target domain, which enhances readability and usability.
Tool Count5/5
With 11 tools, the server is well-scoped for its purpose of providing economic and financial forecasts and analysis. Each tool covers a distinct aspect of the domain (e.g., commodities, inflation, labor), and the count is neither too sparse nor overwhelming, allowing comprehensive coverage without redundancy.
Completeness4/5
The tool set offers broad coverage of key economic indicators and financial markets, with forecasts for commodities, currencies, equities, GDP, inflation, labor, manufacturing, rates, real estate, and retail, plus stock analysis. A minor gap is the lack of tools for historical data retrieval or updates to forecasts, but agents can still perform core analysis tasks effectively.
Average 3.2/5 across 11 of 11 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnly/idempotent/destructive hints, so the safety profile is covered. The description adds valuable authentication context: that an optional API key can be passed via headers or query parameter and enables Pro tier access. However, it omits rate limits, data freshness, or whether results include historical data, forecasts, or both.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The first sentence is a sentence fragment ('Inflation-related indicators...') lacking a verb, which fails to front-load the tool's purpose. While brief, the structure is weak. The second sentence about API authentication is clear but misplaced as the sole actionable sentence.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
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 correctly omits return value details. However, for a forecasting tool with zero schema coverage, the description is insufficient—it doesn't clarify if it returns historical data, future projections, or both, and leaves one of two parameters effectively undocumented.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description partially compensates by listing indicator examples (CPI, PPI, breakevens) that map to the enum values. However, the timeframe parameter is completely undocumented, and the indicator list ends with 'etc.' leaving some enum values unexplained. The API key mention describes external auth, not schema parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the domain (inflation-related indicators) and lists examples aligning with the enum values, but lacks a specific action verb (retrieve, forecast, get). It does not differentiate from sibling forecast tools (forecast_gdp, forecast_rates, etc.) which share similar naming patterns.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance provided on when to select this tool versus the nine sibling forecast tools (e.g., forecast_rates covers breakevens, forecast_labor covers wage inflation). The API key mention describes authentication mechanics, not usage criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly/idempotent/destructive status. The description adds valuable authentication context regarding API keys (headers vs query param) and Pro tier selection, but omits behavioral details like rate limits, forecast horizon, or data freshness that would be useful given the 'forecast' nature.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two compact sentences with no redundancy. Efficiently front-loads the domain (Housing) and covers authentication in the second sentence. However, extreme brevity contributes to underspecification given the lack of parameter documentation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema (relieving description of return value documentation), the description fails to adequately cover the input parameters. With 2 parameters and 0% schema coverage, the terse description leaves timeframe completely undocumented and indicator only partially explained through implicit mapping.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must compensate for both parameters. It effectively maps prose descriptions to the indicator enum values (listing the four specific data types), but provides zero explanation for the timeframe parameter or what the indicator values represent semantically.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description lists specific real estate indicators (Case-Shiller, supply, permits, mortgage rate) identifying the domain, but lacks an explicit action verb stating what the tool does (retrieve, forecast, fetch). It relies on the tool name/title to imply the forecasting action rather than stating it directly.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance provided on when to use this tool versus siblings like forecast_commodities or forecast_gdp. No mention of prerequisites, data availability, or selection criteria for the specific indicators listed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already establish that the operation is read-only, idempotent, and non-destructive. The description adds specific authentication context—explaining the optional API key mechanism, its location (headers or query parameter), and the Pro tier selection behavior—which is valuable behavioral context not present in 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately brief at only two sentences and avoids verbosity. However, the first sentence is merely a noun phrase without a verb ('Retail sales and...'), which front-loads ambiguity rather than clarity, slightly undermining the structural efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
While the output schema absolves the description from explaining return values, the description inadequately documents the parameters (failing to mention 'timeframe' or explain 'indicator' explicitly) and lacks a clear statement of action. Only the API key information provides complete behavioral context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description partially compensates by mapping the cryptic enum values to human-readable concepts ('Retail sales' maps to 'advance_retail_and_food_services_sales', etc.). However, it fails to document the 'timeframe' parameter entirely or explicitly link the mentioned concepts to the 'indicator' parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description provides only a noun phrase listing data sources ('Retail sales and University of Michigan consumer sentiment') without specifying the action performed (retrieve, get, forecast). It fails to distinguish this tool from its 'forecast_' siblings beyond listing specific subject matter, and omits the verb that would clarify the operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance is provided regarding when to select this tool versus sibling alternatives like forecast_commodities or forecast_gdp. The description mentions specific indicators but does not clarify selection criteria, prerequisites, or explicit when-to-use conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds valuable authentication context not present in annotations: the API key can be passed via headers or query parameter, and valid keys activate 'Pro' tier features. However, it omits other behavioral details like rate limits or data freshness.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief (two sentences) but inefficiently structured. The first sentence is cryptic and noun-heavy without a verb. The second sentence about API authentication is important but disconnected from the first, lacking transition or prioritization of the most critical information upfront.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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 and only two simple parameters, the description is minimally adequate. It identifies the domain-specific indicators available but fails to explain what 'forecast' means in this context (predictions vs. historical data), what the default timeframe behavior is, or how to interpret the results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description partially compensates by listing the three indicator types (durable goods, new orders, industrial production) that map to the enum values. This provides human-readable semantics for the required 'indicator' parameter. However, it completely omits the optional 'timeframe' parameter, leaving that undocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description lists the specific manufacturing indicators covered (durable goods, new orders, industrial production) which distinguishes it from sibling forecast tools by domain. However, it lacks a specific verb describing the action (e.g., 'retrieve', 'get') and instead uses a category label format ('Manufacturing:...'), leaving the exact operation ambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus sibling forecast tools (like forecast_gdp or forecast_commodities) or prerequisites. The sibling differentiation is left to the tool name and the indicator enum values rather than explanatory text in the description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations cover read-only, idempotent, and non-destructive properties; the description adds valuable behavioral context about authentication mechanisms (header vs query param) and tier selection (Pro) that is not present in structured metadata. No destructive behavior is mentioned that would contradict annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely efficient two-sentence structure. First sentence front-loads the domain scope (specific rate types), second covers authentication. Zero redundant words or tautology despite brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for a simple read-only forecasting tool with output schema present (no need to describe return values). However, gaps remain: missing explicit confirmation this returns future forecasts, no timeframe parameter explanation, and no rate-limit or pagination behavior disclosure given the API key mention suggests tiered access.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage, requiring the description to compensate. It partially compensates by enumerating specific rate types that map to the indicator enum values, but completely omits the timeframe parameter (default monthly) and provides no syntax or formatting guidance beyond what the enums implicitly suggest.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
Lists specific financial indicators (Fed funds, Treasuries, SOFR, etc.) which implicitly distinguishes from sibling tools like forecast_gdp or forecast_commodities. However, lacks an explicit action verb (forecast, retrieve, project) to clarify what operation is performed on these rates, forcing reliance on the tool name inference.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides operational guidance about optional API key authentication for Pro tier access, but offers zero guidance on when to select this tool versus siblings (forecast_currency, forecast_inflation, etc.) or prerequisite conditions for use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, covering safety and repeatability. The description adds valuable behavioral context not in annotations: authentication is optional, tiered access (free/Pro), and specific header/query parameter formats for keys. However, it omits rate limits, caching behavior, or error conditions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is optimally structured: the first sentence states purpose, the second covers authentication mechanics. No wasted words; every sentence earns its place with distinct, non-redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists, return values need not be described. However, with 0% schema coverage and only partial parameter documentation (indicator explained, timeframe ignored), the description leaves significant gaps. The presence of rich annotations compensates for safety aspects, but the parameter gap prevents a higher score.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 translates the 'indicator' enum values into human-readable commodities (crude oil, natural gas, etc.), adding semantic meaning. However, it completely omits the 'timeframe' parameter (monthly/quarterly/yearly), leaving half the parameter set undocumented beyond the schema structure.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides 'Macro forecasts' (specific verb) for concrete commodities: crude oil, natural gas, aluminium, and volatility (specific resources). This distinguishes it from sibling tools like forecast_currency or forecast_equities by domain, though it does not explicitly contrast with them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
While the description explains authentication tiers (free vs. Pro) and header formats, it provides no guidance on when to select this tool over sibling forecast tools (e.g., forecast_gdp vs. commodities) or what prerequisites trigger its use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, non-destructive, idempotent behavior. The description adds valuable authentication context (API key triggers 'Pro automatically'), which is behavioral information not present in annotations. However, it omits other behavioral details like rate limits, error handling for invalid indicators, or data freshness/horizon of the forecasts.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two efficient sentences. The first front-loads the core purpose and enumerates the available indicators. The second provides necessary authentication context. Every sentence earns its place with zero redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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 omits return value details. However, with 0% schema coverage and 2 parameters, the description should document both parameters but only documents one. Additionally, with 10 sibling forecast tools, the lack of explicit selection guidance leaves a significant gap in contextual completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must carry full parameter documentation. It effectively explains the 'indicator' parameter by listing all five enum values in the parenthetical. However, it completely omits the 'timeframe' parameter (monthly/quarterly/yearly), leaving that parameter undocumented despite its default value and behavioral significance.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it provides 'USD FX and volatility forecasts' and lists specific currency pairs and indices (USD/EUR, USD/AUD, USD/CNY, dollar index, euro FX vol), which maps directly to the indicator enum values. While the domain (currency) inherently distinguishes it from siblings like forecast_commodities or forecast_equities, it lacks explicit comparative guidance stating when to select this over other forecast tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides authentication mechanics ('Optional API key via headers or ?api_key=') but fails to offer selection guidance on when to use this tool versus sibling forecast tools (forecast_equities, forecast_commodities, etc.). No prerequisites, exclusions, or decision criteria are provided beyond the auth mechanism.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds value by specifying the authentication mechanism (headers or query parameter) and the Pro tier selection behavior. However, it omits rate limits, error handling behavior, data source attribution, or cache behavior that would help an agent understand operational constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two efficient sentences with zero redundancy. The first sentence front-loads the domain and available metrics; the second sentence provides authentication details. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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 omits return value details. However, with 0% schema coverage and two parameters, the description should document both 'indicator' and 'timeframe' explicitly. It covers the former but misses the latter, and provides no differentiation from the numerous sibling forecast tools, leaving actionable gaps in context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description partially compensates by enumerating the five possible indicator values in prose ('unemployment, participation, EPOP...'). However, it completely omits the 'timeframe' parameter and its default value of 'monthly', leaving significant semantic gaps that the schema fails to cover.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the specific domain (US labor market) and lists the exact indicators available (unemployment, participation, EPOP, manufacturing/finance payrolls), which distinguishes it from sibling forecast tools like forecast_gdp or forecast_commodities. While it lacks an explicit verb, the combination of the tool name 'forecast_labor', title, and enumerated indicators makes the purpose clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 nine sibling forecast tools (e.g., forecast_gdp vs forecast_labor). It does not mention prerequisites, data freshness, or selection criteria. The only usage-related information is the optional API key authentication mechanism.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations confirm read-only/idempotent safety, while description adds crucial behavioral context: 'model-based' clarifies these are projections not market data, and the second sentence details the API key authentication mechanism (headers or query param) and Pro tier selection logic that annotations cannot convey.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely compact two-sentence structure with zero redundancy. First sentence covers purpose, scope, and resource types; second covers authentication. Every word earns its place, and critical 'model-based' qualifier is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate but incomplete given the tool's moderate complexity and 0% schema coverage. The description covers the domain and auth mechanism, and presence of output schema excuses return value documentation. However, the missing timeframe parameter semantics and lack of differentiation from 'stock_analysis_all' leave notable gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must fully compensate. It successfully maps the indicator values (sp_500, volatility_vix, volatility_russell_2000) to human-readable names in the first sentence. However, it completely omits the 'timeframe' parameter, leaving its monthly/quarterly/yearly options and default behavior undocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clear verb 'outlook' plus specific resources (S&P 500, VIX, Russell 2000) and scope (US equity). 'Model-based, not live prices' effectively distinguishes from real-time data tools. However, it fails to differentiate from sibling 'stock_analysis_all' which also deals with equities, leaving ambiguity between forecast models vs. analysis tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'model-based, not live prices' provides implicit guidance on when to use (for forecasts, not current trading). However, there is no explicit comparison to siblings like 'stock_analysis_all' or other forecast tools, nor any 'when-not-to-use' guidance or prerequisites beyond the API key mention.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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.
Conciseness3/5
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.
Completeness4/5
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.
Parameters2/5
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.
Purpose5/5
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.
Usage Guidelines3/5
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.
Behavior4/5
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.
Conciseness5/5
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.
Completeness4/5
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.
Parameters3/5
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.
Purpose4/5
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.
Usage Guidelines3/5
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.

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