macroooracle

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 transparency. It states that the data comes directly from BLS and lists topics, but it does not disclose the output format, whether it returns a time series or point-in-time values, or any operational behaviors like update frequency or access restrictions. This is minimal and leaves room for surprises beyond the obvious read-only implication.

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 key resource ('US employment data direct from Bureau of Labor Statistics') and follows with a compact bullet-like list of the exact series included. There is no filler or redundancy; every word adds informational value.

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

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

The tool is simple (no parameters, no output schema), but the description does not clarify the return structure—whether it provides a monthly snapshot, historical series, or individual values, nor the units or seasonal adjustments. With no output schema to fill this gap, the description is incomplete for a caller to predict the response format confidently.

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

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

The input schema has zero parameters, so there is nothing to explain. The description appropriately omits parameter details, and the 100% schema coverage means no information gap exists. According to the rubric, a zero-parameter tool gets a baseline of 4.

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 output as US employment data sourced from BLS and enumerates five specific indicators (unemployment rate, nonfarm payrolls, labor participation, hourly earnings, labor force). This specificity distinguishes it from sibling tools like bls_inflation, which cover a different domain, and bls_series, which is generic.

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 implied usage is when a caller needs US employment metrics, but there is no explicit guidance on when to choose this tool over alternatives such as labor_market or bls_series. It does not mention exclusions or provide comparison with siblings, scoring at the implied level rather than a fully guided one.

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 fully disclose behavior. It indicates the two data series provided, but does not mention whether the data is historical, current, or a time series, nor any limitations or update cadence. The description is not misleading but is 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, front-loaded sentence that directly states the tool's purpose and scope. Every word contributes meaning, with no filler or redundancy.

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

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

Given the tool has no parameters and no output schema, the description provides a basic understanding of the returned data. However, it does not specify whether values are index levels, annual changes, or monthly observations, nor the time period covered. This is a minimal but not fully complete description for a data retrieval 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 tool has zero parameters, so the baseline is 4. The description does not need to explain parameter semantics, and it appropriately focuses on the output data. No parameter information is missing.

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 that the tool provides US CPI inflation data from BLS, specifically the headline CPI-U index and core CPI. It distinguishes from siblings like bls_employment and ecb_inflation by specifying the US BLS source and the two series, though it lacks an explicit action verb like 'retrieves' or 'returns'.

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 such as inflation_v2 or ecb_inflation. The description implies it is for US CPI data, but does not explicitly state conditions or exclusions, leaving 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.

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

With no annotations, the description carries the full burden. It does not disclose what the tool returns, how errors are handled for invalid series, or any rate limits. The available list hints that unsupported names could be rejected, but that is left unstated.

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 with a clean list. It is front-loaded with the action and immediately provides usable details with 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?

For a simple one-parameter fetch tool, the description provides the essential valid values but omits return format, potential errors, and any date/unit context. Given no output schema or annotations, this leaves gaps for the agent.

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's description ('Series name or BLS series ID') is generic, but the tool description adds concrete valid series names, which is the only place enumerating options. This significantly aids correct parameter usage.

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 fetches any BLS data series and enumerates specific available series. It is distinct from siblings like bls_employment or bls_inflation by being generic, though it does not explicitly differentiate itself.

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 list of available series implies when to use the tool, but there is no explicit guidance on choosing this over specialized siblings or what situations are inappropriate. No exclusions or alternatives are mentioned.

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

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

With no annotations, the description carries the full burden and does well: it discloses the return fields (reason_codes, signal_strength, risk_band), explains the baseline references (100 and long-run avg ~85), and explicitly warns about the absence of PESSIMISTIC/OPTIMISTIC labels to prevent misinterpretation.

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 and front-loaded with the '[EVIDENCE v1.1]' tag. Each sentence contributes meaningful information: the index name/role, the output fields, and the critical caveat about labels. No filler or redundancy.

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

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

For a simple zero-parameter tool, the description covers purpose, output semantics, baseline values, and a key behavioral caveat. It could add interpretation thresholds for signal_strength/risk_band, but is sufficiently complete for an evidence-only utility.

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 tool has zero parameters, so the schema provides complete coverage. The description appropriately focuses on outputs rather than inputs, meeting the baseline for parameterless tools.

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 (Michigan Consumer Sentiment Index) and its role (evidence only), and explicitly distinguishes it from the retired macroracle version by stating 'NO PESSIMISTIC/OPTIMISTIC labels.' This makes it distinct among the sibling macro 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?

Usage is implied through 'as evidence only' and the caution about label absence, but there is no explicit statement of when to prefer this tool over alternatives or when not to use it. The context is clear enough for an agent to infer, but explicit guidance is missing.

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 full responsibility for disclosing behavioral traits. It mentions data categories but does not describe performance implications (e.g., slower than individual endpoints), data freshness, error handling for partial failures, or the exact structure of the output. This is a significant gap for a aggregator 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?

A single, front-loaded sentence that efficiently communicates the tool's purpose and scope. Every word adds value, and there is no redundant or extraneous content.

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

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

Given the tool's complexity (aggregating five data domains) and the absence of an output schema or annotations, the description provides only a high-level enumeration. It tells the user what areas are covered but not the specific metrics, return format, or usage caveats. This is minimally adequate but leaves gaps for a complex dashboard.

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 tool has zero parameters, so the description need not explain parameter-specific behavior. Baseline for 0-param tools is 4, and the description's enumeration of data areas gives some idea of what the dashboard covers, though it doesn't need to add more.

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 function as a comprehensive ECB dashboard covering rates, FX, inflation, economy, and yields in a single call. It distinguishes itself from sibling tools that focus on individual areas (e.g., ecb_rates, ecb_fx) by explicitly being the 'full' aggregate version.

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 phrase 'in one call' implies this is the go-to tool when a user wants comprehensive ECB data without making multiple separate calls. It provides clear context on its broad scope, though it doesn't explicitly mention alternatives or exclusion scenarios.

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 fails to mention whether the tool returns historical series, current snapshots, or units of measurement. It also doesn't clarify if this is a read-only operation or if there are any access concerns. The description only lists content, not behavior.

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

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

The description is a single, short sentence (or fragment) that front-loads the main topic and enumerates the three key indicators. Every word earns its place with no redundancy or filler. It is highly concise and well-structured for a simple data-retrieval tool.

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

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

Given the lack of an output schema, the description should clarify what the tool returns. It does list the three indicators, but omits details such as time period, geographical coverage (beyond Euro Area), unit formats, or whether the output is a single aggregation or a time series. The low complexity makes it minimally viable, but there is room for more context.

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 tool has zero parameters, and the input schema is empty. The baseline for 0 params is 4, as there is no parameter meaning to explain. The description adds no parameter information, but none is needed. It does list the data fields returned, which indirectly implies the scope of any hypothetical parameters.

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

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

The description clearly identifies the tool as providing Euro Area economic indicators, listing GDP growth YoY, unemployment rate, and M3 money supply. It lacks an explicit verb but the noun-phrase format is unambiguous enough to convey the resource. It partially distinguishes from siblings by naming specific metrics, though it doesn't explicitly contrast with 'ecb_dashboard' or 'ecb_series'.

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 use this tool versus alternatives. The description merely lists indicators, providing no exclusions, prerequisites, or comparisons to sibling tools like 'ecb_inflation' or 'ecb_rates'. The intended context is only implied by the topic, not stated.

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 the data source and frequency, but leaves unresolved whether this returns a time series, a single latest snapshot, the exact date range, or any rate limits/pagination. This is minimal and lacks detail about output structure or side effects.

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 of ten words, immediately stating the currencies and frequency. There is no filler, and it is appropriately sized for a tool with no parameters or complex behavior.

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 lack of an output schema and annotations, the description should clarify what is returned, but it only mentions the currencies and daily frequency. It does not specify whether data is a full history or latest only, nor the structure of the response. It is adequate for a simple tool but leaves room for ambiguity.

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

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

The input schema has zero parameters, so the description does not need to add parameter semantics. Per the rubric, zero parameters default to a baseline of 4. The description adds nothing extra, but none is needed.

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 'EUR exchange rates vs USD, GBP, JPY, CHF, CNY' clearly identifies the resource (exchange rates) and distinguishes it from sibling tools like ecb_rates or ecb_inflation. However, it lacks an explicit verb such as 'get' or 'retrieve', making it a noun phrase rather than a full action statement.

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 mention of 'Daily from ECB' implies usage for accessing daily ECB EUR exchange rates for the listed currencies. Yet it does not explicitly state when to use this tool over alternatives among the many related ECB tools, nor does it provide any exclusions or alternative tool 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It offers no information about what the tool returns, whether it is a read-only query, any limitations, data frequency, or other behavioral traits. The single line simply states the data topic without any operational 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?

The description is extremely concise—a single line that directly states the tool's subject matter. There is no unnecessary wording or repetition. It effectively conveys the core information in a minimal format.

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 nature (no parameters, no output schema), the description provides the essential topic but lacks supplementary context such as the data source, units, frequency, or how to interpret the values. The presence of many inflation-related siblings (inflation, inflation_v2, inflation_v3, bls_inflation) without any usage guidance makes it somewhat incomplete for selection decisions.

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 tool has zero parameters, so the baseline is 4. The description adds context about the data breakdown (headline, core, Germany) which, while not parameter-related, helps the user understand what is being returned. Since there are no parameters to explain, the description need not add parameter 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 clearly identifies the tool's resource (Euro Area HICP inflation) and scope (headline, core, Germany), which distinguishes it from sibling tools like bls_inflation or ecb_rates. However, it lacks an explicit verb (e.g., 'retrieves' or 'provides'), making it a noun phrase rather than a complete action statement.

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 use cases, prerequisites, or explicit comparisons to sibling tools such as ecb_dashboard or inflation_v2. The usage context is entirely implied by the title and data content.

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. However, it only states the data domain ('Eligible asset rates and yields') and does not disclose whether this is a read-only lookup, how data is returned, update frequency, or any access constraints. This is insufficient for a tool with no structured annotation safety info.

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 exceptionally concise with two short sentences. The first sentence states the regulatory context, and the second clarifies the data content. Every word earns its place, and the structure is front-loaded with the most distinctive 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 tool's low complexity (no parameters, no output schema), the description provides enough to know what domain it covers, but 'Eligible asset rates and yields' is somewhat vague. It does not specify whether these are current values, historical series, or specific rate types (e.g., €STR, EONIA), which would be helpful for an agent to fully understand the return value. It is adequate but not thorough.

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 tool has zero parameters, so the baseline is 4. The description adds meaningful domain context beyond the empty schema by specifying that this tool provides eligible asset rates and yields for MiCA compliance, which helps the agent understand what the tool conceptually represents even without parameters.

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

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

The description clearly identifies the tool as ECB data for MiCA stablecoin reserve compliance, specifying the relevant articles (24/25/53). It distinguishes from sibling tools like ecb_yields and ecb_fx by focusing on the MiCA regulatory context, though it lacks an explicit verb like 'retrieve' or 'list'.

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 phrase 'relevant for MiCA stablecoin reserve compliance' implies the intended use case, but there is no explicit when-to-use guidance, nor any mention of alternative tools or when not to use this tool. It leaves the agent to infer when this should be selected over the many sibling ECB 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It does not state whether the tool returns current values, historical series, or a snapshot, nor does it mention update frequency, data source, or any limitations. The agent is left to infer the tool's behavior from the rate names alone.

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, well-structured sentence with a colon introducing a comma-separated list of rates. It is concise and free of filler, though the lack of a verb makes it feel more like a label than an action 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 no-parameter tool, the description conveys the core content (which rates are covered) but omits important context such as whether values are current or historical, units (percent vs basis points), or how this relates to other ECB tools. It is minimally viable but leaves clear gaps.

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

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

The tool has zero parameters and the input schema is an empty object with 100% coverage. The baseline for zero-parameter tools is 4, and the description adds no parameter-related ambiguity. No additional param explanation is needed.

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 as providing ECB interest rates and enumerates specific rate types (Main Refinancing, Deposit Facility, Marginal Lending, EURIBOR, Euro Short-Term Rate). While it lacks an explicit verb like 'returns' or 'lists', the resource is highly specific and distinguishes it from sibling tools such as ecb_yields or ecb_fx.

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 guidance on when to use this tool versus alternatives like fed_rates, ecb_yields, or ecb_dashboard. The description simply lists the available rates without any context or decision criteria.

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

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

No annotations are provided, so the description carries full burden. It only states the fetch action, without disclosing return format, history depth, or behavior for invalid IDs. The claim of '20 series available' contradicts the schema's enum of only 6 IDs, creating confusion.

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, but the phrase '20 series available: rates, FX, inflation, economy, yields' is vague and potentially misleading given the schema's limited enum. It could be more precise by listing the exact series IDs or pointing to the schema.

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 no output schema, the description should explain what the returned data looks like and enumerate series IDs, but it does neither. Given the complexity of navigating 30+ sibling tools, this description is insufficient for an agent to confidently select and invoke the tool.

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

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

The input schema covers the parameter fully with an enum and description. The description adds a high-level categorization of series (rates, FX, etc.) but does not list the actual IDs, and its '20 series' count conflicts with the schema's 6, adding marginal ambiguity rather than clarity.

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 fetches a specific ECB data series by ID, using the verb 'Fetch' and identifying the resource. However, it lacks specificity about which series are available (claims 20 but only lists categories), so it doesn't fully distinguish from category-specific siblings like ecb_rates.

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 use when you know a series ID, but does not explicitly state when to prefer this over the category-specific sibling tools. It provides no exclusions or alternatives, leaving usage decisions to inference.

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?

There are no annotations, and the description does not disclose behavioral details: no mention of return format, update frequency, data source, or read-only nature. It is a noun phrase that does not even explicitly state that invoking the tool returns a value.

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 single-sentence description is extremely concise and front-loaded, with no filler or redundant phrases. Every word contributes to identifying the data series, which is appropriate for a tool with no inputs.

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 names the exact data point but lacks context about the returned value (e.g., current value vs. time series, units, timestamp, or data source). With no output schema, this leaves the agent to infer the output structure, though the tool's low complexity makes it minimally adequate.

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

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

This tool has zero parameters, so the schema provides no parameter information and the baseline is 4. The description adds semantic meaning by specifying the exact series (Euro Area 10Y AAA government bond yield), which is sufficient for a parameterless call.

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 precisely: 'Euro Area 10Y AAA government bond yield benchmark,' with region, maturity, and rating qualifiers. This differentiates it from broader siblings like ecb_rates or yield_curve, though it lacks an explicit action verb like 'retrieves' or 'returns.'

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 use this tool instead of related tools such as ecb_rates, ecb_dashboard, or yield_curve_v1-3. The description states only what the benchmark is, not when it should be selected.

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 and does so admirably. It discloses the payment requirement ($0.05 via x402), the 'evidence only' nature, the output structure (reason_codes + signal_strength + risk_band), and the limitation of no regime labels. It also notes the legacy macroracle lineage and free trial units, providing a thorough behavioral profile 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 somewhat dense with emojis, payment details, call URL, and free-unit note, but every sentence contributes essential information—payment requirement, purpose, output, limitations, and access method. It is front-loaded with the premium alert and remains readable. Slightly overlong for a no-param tool, but not wasteful.

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 fully self-contained: it explains what the tool returns (reason_codes, signal_strength, risk_band), what data it covers, how to access it (URL and header), and its limitations. With no output schema provided, this description sufficiently covers the return values and usage context, making it complete for an agent to decide on and invoke the 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 tool has zero parameters and the input schema is empty, so there is no parameter semantics to clarify. The description implicitly confirms that no inputs are needed by outlining the tool's function. Baseline score for zero parameters is 4, and the description adds no conflicting or confusing information.

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

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

The description clearly states the tool provides a 'US economic health composite (GDP, unemployment, inflation, consumer confidence)' and explicitly notes it returns reason_codes + signal_strength + risk_band. This specific blend of indicators distinguishes it from sibling tools like gdp_growth or inflation, and the mention of 'NO regime labels' further differentiates it from related 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 provides clear context: it is a paid premium tool requiring x402 payment, and it explicitly states that it is a composite and does not provide regime labels. However, it does not explicitly name alternative tools for when regime labels are needed, so it lacks a direct 'use instead' pointer. Still, the context is evident and clear.

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

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

With no annotations, the description carries the full burden of behavioral disclosure, but it only lists data topics. It does not state whether the tool is read-only, what data source or freshness applies, how results are returned, or any limitations. The phrase 'policy outlook' is ambiguous and lacks behavioral detail.

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

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

The description is concise and front-loaded, containing zero filler words. However, it is a noun phrase rather than a complete sentence, and the lack of a verb reduces structural clarity. It is appropriately short but could be more informative.

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

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

Given the tool's simplicity (no parameters, no output schema), the description provides a minimal list of contents but is vague about 'policy outlook' and does not clarify version differences from fed_rates_v2/v3. It is adequate for a basic data feed but leaves room for more specificity about return values and 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?

The tool has zero parameters, and the schema is empty. Per the rubric, the baseline is 4 for 0-parameter tools. The description adds contextual meaning by specifying the data domain (rates, calendar, outlook), which is useful for a parameterless tool.

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 (Federal Reserve interest rates, FOMC meeting calendar, policy outlook) but lacks a specific verb or action. It is clear in topic but does not state what the tool actually does (e.g., returns, provides, monitors), and it does not distinguish from sibling tools like fed_rates_v2 or fed_rates_v3.

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 explicit guidance on when to use this tool versus alternatives. The description implies it is relevant to Fed rates and policy information, but it does not mention exclusions, prerequisites, or how it compares to the many 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?

No annotations are provided, so the description carries the full burden. It discloses the output structure (signal, regime, percentiles, etc.) and that it provides an interpretation layer, but it does not disclose data freshness, computation method, or any limitations.

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

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

The description is two sentences, front-loaded with the core resource and features, followed by the target audience. Every sentence adds value; 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?

For a zero-parameter tool with no output schema, the description provides adequate information about what the tool returns. However, it does not explicitly differentiate from fed_rates_v3, which is a sibling; this is a minor 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 tool accepts zero parameters, so the schema already fully covers parameter semantics. The description adds no parameter-specific information but none is needed. Baseline 4 for zero parameters.

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

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

The description clearly identifies the tool as returning Fed Funds Rate data with an interpretation layer (signal, regime, percentiles, momentum, volatility, confidence, actionable_prompt). It distinguishes from sibling fed_rates by explicitly stating the interpretation layer, though it lacks an explicit verb like 'get' or 'retrieve'.

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 phrase 'Built for autonomous agents that need pre-reasoned data' implies when to use this tool; however, it does not explicitly state alternatives or when not to use it, leaving room for ambiguity compared to raw data 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?

With no annotations provided, the description carries the full burden for behavioral disclosure. It states what is returned and what is deliberately omitted, which is useful. Yet it offers no details on data recency, source, error behavior, or other operational traits, leaving some behavioral aspects undisclosed.

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 extremely concise, using two short sentences (plus a positioning tag) that pack all key information. It is front-loaded with the core purpose and every clause adds value, 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 parameterless tool with no output schema, the description adequately covers the main purpose, return fields, and explicit non-goals. It leaves some ambiguity about field formats or interpretation, but overall it is sufficiently complete for an agent to understand and invoke the tool correctly.

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

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

The tool has zero parameters, so the baseline is 4 per the rubric. The description doesn't need to explain parameter semantics; instead it focuses on output semantics, listing the returned fields. This is appropriate and consistent.

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 returns Fed Funds Rate evidence with specific fields (signal_strength, risk_band, reason_codes, action categories). The phrase 'as evidence only' and 'Product positioning: evidence_infrastructure' distinguish it from advisory or recommendation tools, and the 'Returns' verbs signal its data-fetching role.

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

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

The description provides clear context that this is an evidence-only tool and explicitly lists what it does NOT do (no recommendations, no sizing, no venue picks), giving the agent usage boundaries. However, it does not name alternative tools for those other purposes, so it lacks explicit 'use this instead' 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?

With no annotations, the description carries the burden, but it only lists data categories rather than stating explicit behavior (e.g., 'returns data', output type, or whether it is a read-only query). It is not misleading and gives some sense of what to expect, but it leaves mechanics unspecified.

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 packs all relevant content; every word contributes and nothing is redundant or padded.

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 no-parameter tool, the description lists its main content areas, but it lacks usage guidance and does not differentiate from overlapping sibling tools (recession_risk_v1, wb_gdp). It is adequate but leaves some ambiguity for an agent deciding between this and alternatives.

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 is empty and there are zero parameters, so there are no parameter semantics to document; per rubric, a baseline of 4 is warranted.

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 phrase 'US GDP growth' clearly identifies the resource and domain, and the list of quarterly/yearly, real GDP, consumer spending, and recession risk conveys scope. However, it lacks an explicit verb and does not distinguish itself from siblings like wb_gdp or recession_risk_v1, so it misses a 5.

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 instead of sibling tools such as wb_gdp or recession_risk_v1. The description merely enumerates indicators without giving selection criteria 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, the description carries the full burden of behavioral disclosure. It gives no details about what the tool returns, whether it is a simple ping, or any side effects. 'Server status, API connectivity' is an under-specified subject matter rather than a disclosure of behavior.

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

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

The description is a fragment without a subject-verb structure. It is not concise in a purposeful way; it is under-specified. It lacks a clear sentence and does not provide structured information about the 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?

Even for a simple tool with no parameters or output schema, the description is incomplete. It leaves out what result to expect and how to interpret the status. The bare phrase 'Server status, API connectivity' does not sufficiently prepare an agent to invoke the tool correctly.

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

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

The tool has zero parameters, so the schema coverage is 100% and the description does not need to explain parameter semantics. Baseline 4 applies because there is nothing to compensate for.

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 'Server status, API connectivity' is a noun phrase that vaguely indicates the tool reports on server/API health, but lacks a clear verb and does not explicitly state an action such as 'checks' or 'returns'. It does differentiate from sibling data retrieval tools by its health-check focus, but without a verb the purpose is ambiguous.

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 guidance on when to use this tool versus alternatives. It does not mention any prerequisites, exclusions, or typical scenarios such as verifying connectivity before making data queries, leaving the agent without direction.

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

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

No annotations are provided, and the description gives no behavioral context—no mention of read-only, data source, time range, response format, or limitations. The phrasing is a noun-phrase tag rather than an explanation of what the tool returns or how it behaves.

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 fragment that front-loads the subject and lists content areas without waste. Every word adds value.

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

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

The description lists the data categories but omits crucial context such as time period, frequency, units, and output shape. Without an output schema or additional details, an agent cannot fully infer what the tool returns, making the coverage 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 tool has zero parameters, and the schema shows an empty object, so there are no parameter semantics to clarify. Per the rubric, 0 params yields a baseline of 4, and the description adds no irrelevant parameter information.

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

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

The description clearly identifies the tool as providing US housing market data, listing specific indicators like housing starts, permits, median prices, 30Y mortgage rates, and home sales. This distinguishes it from sibling tools focused on employment, inflation, and other sectors, despite lacking an explicit verb.

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

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

The description implies the tool should be used when housing market data is needed, but does not explicitly state when to use it compared to alternatives or exclude cases. No alternatives or exclusions are mentioned, leaving usage to inference.

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 lists data categories without explaining the return format, time range, data source, or whether the data is historical or current. This leaves the agent uncertain about the tool's actual behavior when invoked.

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 efficiently captures the tool's content with no filler. Every phrase adds value, making it an appropriately sized and front-loaded description.

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 provides a reasonable high-level overview, but it omits details such as whether it returns historical series, current values, or a specific time period. With no output schema or annotations, this leaves gaps in the agent's understanding of the tool's full 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?

The tool has zero parameters and the schema contains no properties, so schema coverage is trivially 100%. The description adds no parameter details, but none are necessary for a parameterless tool; the baseline of 4 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 clearly states what the tool provides: US inflation data including CPI, PCE, core inflation, and YoY/MoM changes. It specifies a clear resource (US inflation) and scope, but does not differentiate it from overlapping siblings like bls_inflation or inflation_v2, so it stops short of a 5.

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 such as bls_inflation or inflation_v2. It does not mention any exclusions, alternatives, or prerequisites, leaving the agent with no decision criteria for 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?

With no annotations, the description must carry the full burden of behavioral disclosure. It lists output components (regime, trend, percentile, ranked actions) but does not explicitly state that the tool is read-only, has no side effects, or requires any permissions. It also fails to mention any limitations, rate limits, or data source caveats, leaving the agent with an incomplete picture of the tool's behavior.

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

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

The description is a single, information-dense sentence that front-loads the core purpose ('CPI YoY') and then details the interpretation layer. The phrase 'Built for autonomous agents' is somewhat redundant with the 'AGENT-NATIVE' tag, but overall the structure is efficient and avoids unnecessary verbosity.

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

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

Given the tool's complexity and the absence of an output schema, the description does list the key output fields (regime, trend, percentile, ranked actions), which is helpful. However, it lacks context on the data source, update frequency, and how it relates to other inflation tools. In a landscape with multiple inflation siblings, the description is insufficiently complete for an agent to make an informed choice.

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 tool has zero parameters, and the schema is empty with 100% coverage. Per the baseline for no parameters, the description need not explain parameter semantics. It correctly provides no parameter information, and this does not detract from its 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?

The description clearly identifies the tool as providing CPI YoY data with an interpretation layer, distinguishing it from raw inflation tools. While it lacks an explicit verb like 'retrieve' or 'get', the intent is evident from the context, and the mention of 'regime', 'trend', and 'ranked actions' specifies what is offered.

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 explicit guidance on when to use this tool compared to sibling tools like bls_inflation or inflation. The phrase 'Built for autonomous agents' hints at a target audience but does not provide criteria for selection or mention alternatives. The description lacks any exclusions or recommendations.

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

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

No annotations are provided, so the description carries the full burden. It clearly discloses what the tool returns (signal_strength, risk_band, reason_codes, allowed/blocked categories) and explicitly states what it does NOT do (no personalized advice, sizing, or venue picks). The 'evidence only' positioning further signals a strict non-advisory role. It stops short of explicitly stating read-only behavior or data freshness, but its safe, evidence-only nature is well conveyed.

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 highly concise—three short sentences plus a product positioning tag. It front-loads the core role, lists output fields, and conveys exclusions in a scannable format. Every word earns its place with no redundancy or 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?

The tool is simple (no params, no output schema), and the description covers the essential points: what it returns, what it doesn't, and its positioning. However, it leaves some ambiguity about the underlying data source (given many sibling inflation tools like bls_inflation, ecb_inflation) and does not explain the semantics of signal_strength/risk_band, which could confuse an agent trying to differentiate it from alternatives.

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?

This tool takes no parameters (empty input schema, 0 parameters), so the baseline is 4. The description adds no param-specific information because none is needed; it simply references 'CPI YoY' which is the tool's fixed data series. This is perfectly acceptable given the zero-parameter 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 identifies the tool as providing CPI YoY evidence with specific output fields (signal_strength, risk_band, reason_codes, allowed/blocked action categories). The explicit exclusions ('NO personalised recommendations, NO sizing, NO venue picks') distinguish it from other inflation-related tools. Although no verb like 'returns' is used, the purpose 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?

The phrase 'as evidence only' indicates the appropriate use case, and the bolded exclusions explicitly tell the agent when NOT to use this tool (when recommendations, sizing, or venue picks are needed). However, it does not name alternative tools or provide a direct 'use this when' statement, making the guidance somewhat implicit.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only lists the data topics covered, with no mention of data source, update frequency, units, or return format. This leaves significant ambiguity about what happens when the tool is invoked.

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 that lists the core indicators without filler. Every word adds informational value, making it maximally 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?

For a tool with no parameters and no output schema, the description should clarify what the response contains and how it differs from closely related sibling tools. It lists indicators but not the period, units, data source, or whether it returns structured dataset or headline figures, leaving the tool under-specified for an agent to use confidently.

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 is empty (zero parameters), so parameter semantics are not applicable. The baseline for zero-parameter tools is 4, and the description adds no unnecessary parameter documentation, which 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 identifies the tool's resource (US labor market) and enumerates specific indicators it covers (unemployment rate, nonfarm payrolls, wages, jobless claims, labor participation). It lacks an explicit verb like 'provide' or 'retrieve,' but the intent is unmistakable. It distinguishes itself from siblings by its broad labor-market scope, though it does not explicitly name alternatives.

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 guidance on when to use this tool versus sibling tools such as bls_employment or bls_series. The description implies a general overview of US labor data, but it does not state when to prefer this tool or what situations it is not suitable for.

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 takes full burden and delivers well. It discloses the payment requirement ($0.05 via x402), free units for new wallets, the return structure (reason-code index, distributions, cross-tool concentration), and the neutral nature of the aggregation. This is valuable behavioral context beyond a simple read operation.

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

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

The description is slightly long due to the payment details and URL, but the content is front-loaded with the purpose ('NEUTRAL AGGREGATION') and each piece of information is necessary for correct invocation. It could be tighter, but all sentences are functional.

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

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

Given no output schema, the description thoroughly explains what the tool returns (reason-code index, distributions of signal_strength/risk_band/staleness_tier, cross-tool concentration) and what it aggregates. For a parameterless tool, this is complete enough for an agent to understand the tool's role and expected output.

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 tool has zero parameters, so the schema provides no parameter semantics. The description appropriately does not add parameter details since there are none; instead, it focuses on payment and output structure, which is the only context needed. This is a baseline of 4 for zero-parameter tools.

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 verb as 'aggregation' and specifies the exact input resources (fed_rates_v3, inflation_v3, yield_curve_v3) and output contents (reason-code index, distributions, cross-tool concentration). It also distinguishes from siblings by noting 'No directional labels, no alignment score, no venue hints' and framing itself as a one-call aggregator.

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

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

It explicitly says 'One call replaces three for macro-aware agents', which clarifies when to use it: when you need a neutral aggregate of those three tools. It also lists what it does NOT provide (directional labels, alignment score, venue hints), helping agents avoid using it for those purposes, though it doesn't explicitly name alternative tools for those cases.

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. It discloses a critical behavioral trait: the x402 payment requirement ($0.08) and the 5 free units for new wallets, which is essentially an auth/rate-limit detail. It does not describe response format or data freshness, but for a no-parameter dashboard tool, payment disclosure is the most important 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?

The description is two sentences and reasonably tight. It front-loads the core purpose with 'Full US economic dashboard' and then provides the key payment/call instructions. The emoji and promotional 'PREMIUM' are stylistic but not bloated; each sentence carries necessary information, though the call URL could be considered slightly extraneous.

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 (0 params, no output schema) and no annotations, the description is fairly complete: it states purpose, lists the indicator categories, and gives the payment mechanism and call endpoint. It doesn't describe the response structure, but for a dashboard aggregator with no parameters, this is adequate. The sibling-tool context further clarifies its role as a broad alternative to individual indicators.

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 tool has 0 parameters and an empty schema, so the baseline is 4. The description adds no parameter details, but none are needed. It mentions the content areas (Fed, inflation, etc.) which indirectly explains what the tool returns without parameters.

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

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

The description clearly states a specific verb and resource: a "Full US economic dashboard" covering all key indicators (Fed, inflation, yields, labor, GDP, housing). This distinguishes it from sibling tools that focus on individual indicators, providing excellent purpose 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 phrase "all key indicators at once" implies use for a comprehensive overview versus individual indicator tools, but there is no explicit guidance on when to choose this over siblings or when not to use it. Usage is implied rather than clearly stated with alternatives.

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

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

With no annotations, the description carries the full burden. It discloses payment requirements, the call URL and header, and a limitation (no stress_level categorical). It implies read-only behavior by stating 'evidence only' and 'Returns', but does not explicitly declare safety or describe error handling or rate limits.

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

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

The description is a single, information-dense paragraph covering payment, components, output, limitation, and access URL. All sentences are relevant, though the emoji and 'New wallets get 5 free units' add minor fluff. It is well-structured and front-loaded with the most critical payment requirement.

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?

There is no output schema, so the description explains the return fields (reason_codes, signal_strength, risk_band) and the data sources. It also covers the notable absence of the stress_level field. While it does not detail the meaning of the return fields, the description is reasonably complete for a no-input evidence 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 tool has zero parameters, so the schema is already complete. The description does not need to explain inputs, and it doesn't introduce any parameter-related ambiguity. The baseline of 4 is appropriate as no compensation is needed.

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

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

The description clearly states the tool provides a 'US financial stress composite' composed of VIX, HY spread, TED spread, and STLFSI, and explicitly lists the output fields (reason_codes, signal_strength, risk_band). It distinguishes itself from siblings by emphasizing 'evidence only' and noting the absence of the 'stress_level categorical' field.

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

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

It provides clear contextual guidance by stating it is a premium tool requiring x402 payment, and notes this is 'evidence only' with no categorical stress level, indicating when not to rely on it for categorical outputs. However, it does not explicitly name alternative sibling tools for similar purposes.

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

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

With no annotations, the description carries the full burden of disclosure. It transparently covers the premium payment requirement, the evidence-only nature, the returned fields, and the fact that it was rebuilt from a retired tool. It does not mention side effects or rate limits, but for a read-only, zero-parameter tool, this is adequate.

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

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

The description is front-loaded with the core purpose but includes extraneous elements like emojis, marketing language, and explicit call instructions that could be streamlined. It contains several clauses and asides (e.g., 'Rebuilt from retired macroracle') that add context but reduce conciseness.

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

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

For a zero-parameter tool with no output schema, the description covers the key invocation details: what it returns, the payment requirement, and the evidence-only constraint. It does not explain the meaning of returned reason_codes, but given the tool's simplicity, this is a minor gap.

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 tool has zero parameters, so the schema already covers all inputs vacuously. The baseline for 0 params is 4, and the description does not need to explain parameters. The payment and header details are invocation context, not parameter 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 clearly identifies the tool as providing US recession indicators as evidence only, with a specific resource type and scope. It distinguishes itself from siblings by explicitly stating it returns reason_codes, signal_strength, and risk_band, and by noting the absence of recession probability labels or risk classification.

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

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

The description provides clear usage context by stating the tool is evidence-only and explicitly excludes probability labels and LOW_RISK/HIGH_RISK classification, signaling when not to use it. However, it does not name alternative tools for those cases, so it lacks explicit 'use instead' 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?

There are no annotations, so the description carries the burden of behavioral disclosure. It lists the data contained but does not describe output format, whether the country_code is required or optional, historical vs. current values, or any limitations. The agent is left uncertain about the behavior beyond the basic data contents.

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 that is front-loaded with the tool's core purpose and key indicators. It contains no filler, repetition, or irrelevant details, 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?

Given the simple schema (one parameter) and lack of output schema, the description provides a reasonable summary of the tool's content. However, it omits important context such as the optionality of the parameter and the nature of the return values (e.g., latest figures vs. time series). This leaves some gaps for an agent attempting to use the tool correctly.

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

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

The input schema already provides full coverage of the only parameter with an explanatory description listing example ISO-3 codes. The description adds no additional parameter semantics beyond what the schema provides, 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 identifies the resource as a World Bank country economic profile and lists the key indicators (GDP, inflation, trade balance, population). This distinguishes it from sibling tools like wb_gdp, which focuses only on GDP. However, it lacks an explicit verb like 'retrieves' or 'gets', making the action slightly implicit.

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. With many sibling macro-economic tools, the absence of any contextual hint or exclusion (e.g., 'use for broad country profiles, not specific metrics') leaves the agent to infer usage. The description only states what it returns, not when it is the best choice.

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 present, so the description carries the full burden. It conveys a read-only data lookup and indicates the output includes GDP rankings and growth rates, but it does not clarify ranking size, ordering, units, or update frequency. The phrase provides a basic sense of behavior but lacks detail.

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 phrase with no filler. Every word contributes meaning, identifying both the data source and the content. It is appropriately sized for the tool's simplicity.

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 no annotations and no output schema, the description must explain return values and context. It falls short by not specifying how many economies are included, the ranking criteria, whether values are current or historical, or any data-format details. Given the rich sibling list, more context is needed to avoid ambiguity.

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 tool has zero parameters, and the schema is fully covered with no properties. Per the rubric, the baseline for zero parameters is 4. The description adds no need for parameter explanations, as no inputs exist.

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 domain (World Bank data) and subject matter (top global economies by GDP with growth rates). While it lacks an explicit verb, it distinguishes itself from siblings like gdp_growth and wb_country by specifying a ranking of economies and including growth-rate 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. With numerous sibling tools covering economic indicators, the description offers no exclusions, prerequisites, or comparative context, leaving the agent to infer appropriate usage from the title and phrase 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 carries the full burden of behavioral disclosure. It mentions an 'economic risk score and key indicators' but does not specify what the indicators are, how the score is scaled, or whether any prerequisites (like a required country code) exist. The acronym 'RWA' is also ambiguous, leaving the data source and output format unclear.

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 is concise and front-loaded with the tool's name and purpose. It avoids unnecessary filler, though the phrase 'World Bank RWA risk context' is somewhat redundant with 'for country risk assessment'. Overall, it is efficient but could be more specific without adding length.

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

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

Given the tool has one parameter, no output schema, and no annotations, the description should explain what the tool returns more thoroughly. It only mentions 'economic risk score and key indicators' without detailing which indicators or how the score is interpreted. The description also fails to clarify whether the country_code parameter is required, especially since the schema lists zero required parameters. This leaves the tool under-specified for an agent to use confidently.

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 one parameter, country_code, with a clear description 'ISO-3 country code', providing 100% schema description coverage. The tool description does not add anything beyond this, so the baseline of 3 is appropriate. The optional status of the parameter (required: 0) is not addressed in the description, but the schema itself communicates the basic 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 clearly states it provides World Bank risk context with an economic risk score and key indicators for country risk assessment. This distinguishes it from sibling tools like wb_gdp or wb_country, which are more general data tools. However, it lacks an explicit verb like 'retrieve' or 'get', making it slightly less direct than ideal.

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 phrase 'for country risk assessment' provides an implied usage context, but it does not explicitly state when to use this tool over alternatives like wb_country or wb_gdp. No exclusions or alternative tool references are provided, so the guidance remains implicit 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, the description carries the full burden. It lists the included data elements but does not disclose data frequency, historical depth, or the methodology behind the recession probability. The mention of 'inversion signals' hints at derived metrics but does not explain their calculation or behavior.

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

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

The description is a single sentence that front-loads the core topic ('US Treasury yield curve') and then lists outputs in a compact, structured way. No unnecessary words or repetition—every phrase adds value.

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

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

Given the tool's low complexity (0 params, no output schema), the description covers the primary outputs effectively. However, it omits whether the data is historical or current, and it does not differentiate from yield_curve_v2/v3, leaving minor gaps for an agent in a broader context.

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 tool has zero parameters, so the schema is vacuous. The description adds meaning by clarifying what the tool returns (yield curve data, spread, signals, probability), which is essential when there are no parameters. Per calibration baseline, 0 params warrants a 4.

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 (US Treasury yield curve) and enumerates specific outputs: all maturities, 10Y-2Y spread, inversion signals, and recession probability. This level of detail distinguishes it from sibling tools like yield_curve_v2 and other economic indicators, making the tool's purpose 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?

No guidance is provided on when to use this tool versus alternatives such as yield_curve_v2 or other dashboards. The description lacks any exclusions, prerequisites, or context about selection, leaving the agent without direction on tool choice.

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

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

With no annotations, the description carries the burden of disclosing behavior. It does reveal the key output fields and interpretation layer, which is useful, but it leaves important behavioral details vague—such as what 'ranked actions array' contains and how 'recession_imminent detection' is determined. It does not mention side effects, data source, or caveats, though this likely a read-only data 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, dense sentence that packs all core information—the resource, the interpretation layer, and the main outputs—without wasted words. It is efficiently structured 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?

In the absence of an output schema and annotations, the description outlines the main return fields, but it is not fully complete. Terms like 'ranked actions array' and the specifics of 'un-inversion pattern' are undefined, leaving the agent with an incomplete picture of what the tool returns. For a zero-parameter tool, this is adequate but not rich enough for a higher score.

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 tool has zero parameters, so the schema provides no parameter details. The description is not required to explain parameters. The baseline of 4 applies because there is nothing to document and no ambiguity about invocation inputs.

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 'Treasury 10Y-2Y spread' and adds an 'interpretation layer' with specific outputs (curve_state, days_inverted_last_365, recession_imminent, ranked actions array). However, it lacks a direct verb and does not explicitly differentiate from siblings like yield_curve or yield_curve_v3, so it falls short of a 5.

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 such as yield_curve or yield_curve_v3. There is no mention of exclusions, prerequisites, or preferred scenarios, making the description purely positional rather than instructive about 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?

With no annotations, the description carries the behavioral disclosure burden. It discloses the tool's nature ('evidence only'), its specific outputs, and limitations (no recommendations, sizing, or venue picks). This adds meaningful context beyond a simple 'returns spread' statement, though it omits potential details like data source or update frequency—acceptable for a read-only, zero-parameter 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 concise and front-loaded, with the core purpose in the first phrase ('Treasury 10Y-2Y spread as evidence only'). It efficiently lists outputs, exclusions, and product positioning in three compact sentences, with 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?

As a zero-input tool with no output schema, the description adequately summarizes the return values (signal_strength, risk_band, reason_codes, allowed/blocked categories) and the evidence-only positioning. It could elaborate on the meaning of 'risk_band' or 'allowed/blocked categories', but for a simple evidence tool, the provided context is largely sufficient.

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 tool accepts no parameters, so the description has no parameter semantics to clarify. Per the rubric, a zero-parameter tool receives a baseline of 4.

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 computes the Treasury 10Y-2Y spread and returns specific evidence outputs (signal_strength, risk_band, reason_codes, action categories). It differentiates itself from advisory tools by explicitly noting 'NO personalised recommendations, NO sizing, NO venue picks' and positioning as 'evidence_infrastructure', though it does not directly name sibling 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 implies usage for evidence-gathering rather than decision-making, explicitly excluding personalized recommendations, sizing, and venue picks. However, it lacks explicit 'when to use' guidance or comparisons to alternatives like yield_curve or yield_curve_v2, so the guidance is implied rather than fully specified.

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