FXMacroData

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

Annotations already declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds that an API key is needed, which is valuable behavioral context. No contradictions with annotations.

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

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

Two efficient sentences: first states purpose and slug guidance, second lists prerequisites and supported values. No wasted words, front-loaded with key info.

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 4 optional parameters, full schema descriptions, and an output schema, the description covers the tool's purpose, prerequisites, valid parameter values, and alias clarification. It is adequate for an agent to invoke correctly.

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

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

Schema coverage is 100% with descriptions. The description adds context: 'symbol' is a backward-compatible alias for 'indicator', and lists the exact supported slugs. This clarifies parameter semantics beyond the schema.

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

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

The description clearly states the tool retrieves historical price series for commodity indicators, specifying the verb 'get' and resource 'historical price series'. It lists supported indicators, aiding identification. However, it does not explicitly differentiate from sibling tools like 'commodities_visual_artifact', so it's clear but not perfectly distinguished.

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 notes 'Requires an API key' and instructs using exact slugs from the schema. It does not provide guidance on when to use this tool versus alternatives (e.g., when to choose commodities over cot_data or visual_artifact), leaving the agent to infer.

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

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

Annotations already indicate read-only, non-destructive, and open-world. Description adds that it packages chart metadata, but doesn't disclose other behavioral traits like data freshness, auth requirements, or error handling beyond what annotations provide.

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?

Single sentence, front-loaded with key distinction, 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?

No output schema, but description implies same payload as 'commodities'. Lacks details on return format or chart metadata structure. Adequate for a read-only tool with good annotations.

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

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

Schema covers 100% of parameters with descriptions. Description adds no extra meaning to parameters; baseline 3 for high schema coverage.

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

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

Description clearly states it is like 'commodities' but with MCP Apps chart metadata for inline rendering. Distinguishes itself from sibling 'commodities' by specifying the visual artifact aspect.

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

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

No explicit when-to-use or when-not-to-use guidance. Implies use for chart rendering, but doesn't compare with alternative 'commodities' or other tools.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds useful behavioral traits beyond annotations: 'Updated every Friday at 15:30 ET reflecting the Tuesday cutoff' and 'Requires an API key'. No contradictions.

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

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

The description is concise with approximately 6 sentences. It front-loads the main purpose and usage context. Every sentence provides value, including update schedule, auth, and supported currencies. No waste.

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 an output schema, the description does not need to explain return values. It covers update schedule, auth requirements, supported currencies, and usage context. For a simple tool with 3 parameters, this is fully adequate.

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

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

Schema description coverage is 100%, so the description does not need to add much. It lists supported currencies, which is already in the schema. No additional parameter semantics beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool gets 'weekly CFTC Commitment of Traders (COT) positioning data for a currency's FX futures contract on the CME'. The verb 'Get' and specific resource 'CFTC COT positioning data' are precise. It distinguishes from siblings like commodities and forex by specifying CFTC data, and there is no direct COT data sibling.

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 explicitly says 'Use this when the user asks about speculator positioning, non-commercial longs vs shorts, hedge-fund FX positioning, or wants to gauge sentiment extremes'. This provides clear context. It does not explicitly list when not to use, but the usage guidance is strong.

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

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

Annotations already indicate read-only, open-world, non-destructive behavior. The description adds that the tool returns chart metadata (beyond raw data) and mentions the default metric. It does not contradict annotations and provides useful context, though it could detail what 'chart metadata' entails.

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

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

Two sentences that are front-loaded with the core purpose and a clear instruction for customization. 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 tool with 4 parameters all documented in schema and simple behavior, the description is nearly complete. It lacks an explicit statement about the return format (visual artifact), but the term 'chart metadata' implies it. Overall adequate.

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

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

Schema description coverage is 100%, so the schema already documents each parameter well. The description only adds that the metric defaults to noncommercial_net, which is already in the schema. It provides no additional meaning beyond the schema.

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

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

The description clearly states it charts COT data with MCP Apps chart metadata, distinguishing it from cot_data which returns raw data. It specifies the default chart and how to customize via the metric parameter.

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

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

The description indicates when to use this tool over cot_data (when a chart is desired) and how to customize the chart. However, it does not explicitly exclude use cases or compare to other visual artifact tools like commodities_visual_artifact.

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

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

Annotations declare readOnlyHint=true and destructiveHint=false. The description adds behavioral context by warning about checking coverage for staleness and unsuitability of stale/partial rows for real-time analysis, which goes beyond annotations.

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

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

The description is two sentences, front-loaded with the main action and followed by usage guidelines. It is informative without being overly verbose, though a slightly tighter structure could improve conciseness.

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

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

Given 4 parameters with 100% schema coverage and an output schema existing, the description covers when to use, what is returned, and behavioral notes. It does not need to explain return values as output schema is present.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaning by explaining the currency list, optional indicator usage, and practical advice for include_coverage and include_capabilities parameters, enriching the schema.

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

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

The description clearly states the tool lists all macroeconomic indicators for a currency with metadata, and distinguishes itself from siblings by specifying it returns indicator slugs for other tools like indicator_query and release_calendar. The verb 'list' and resource 'every macroeconomic indicator' provide a specific purpose.

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

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

Explicit guidance is given: 'ALWAYS call this first when the user asks about a country's macro data,' and instruction to check coverage before calling indicator_query. Supported currencies are listed, providing clear when-to-use and 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?

Annotations already indicate readOnlyHint=true (safe read) and destructiveHint=false. The description's use of 'Create' could be mildly misleading but aligns with generating a view, not mutation. Adds the async execution nuance but little else beyond annotations. No contradiction found.

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

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

Two sentences, front-loaded with the core purpose, no fluff. Efficiently communicates the essential action and async support.

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 7 parameters and no output schema, the description is brief. It does not explain the output structure, how the replay timeline is structured, or provide usage examples. The async execution note helps but completeness is moderate.

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

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

Schema description coverage is 100% with detailed parameter descriptions including examples and supported values. The description adds no additional parameter context beyond what the schema provides, so baseline 3 applies.

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

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

The description specifies 'Create a point-in-time replay timeline mapping macro announcements to FX context and heuristic impact markers', clearly indicating the action (create timeline) and resource (replay of macro announcements with FX context). However, it does not strongly differentiate from sibling task tools like indicator_intel_task or macro_war_room_task beyond the specific replay focus.

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

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

No explicit guidance on when to use this tool versus alternatives. The description mentions async execution support but does not provide criteria for choosing this tool over other task tools.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. Description confirms by stating 'Return stored forecasts' (read-only) and adds that rows are keyed by announcement_id/date/indicator with prediction source metadata. No contradiction; adds mild behavioral context beyond annotations.

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

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

Description is lengthy due to redundant listing of all supported currencies and indicators (already present in schema). The essential purpose and usage guidance is front-loaded, but the duplicate lists waste tokens and 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?

Given 9 parameters (1 required), output schema present, and no nested objects, the description covers the key points: what it returns (forecasts), filtering by currency/indicator/dates, and how rows are keyed. Lack of detail on pagination is offset by schema descriptions.

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

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

Schema description coverage is 100%, so baseline is 3. Description repeats the lists of supported currencies and indicators already in the schema property descriptions, adding no new meaning. Does not explain pagination or grouping of predictions beyond schema.

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

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

Description starts with a specific verb 'Return' and resource 'stored forecasts, consensus-style predictions', listing multiple prediction types. It explicitly states when to use with sibling tools (release_calendar, indicator_query) for actual-vs-consensus context, clearly distinguishing its purpose.

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

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

Provides explicit usage context: 'Use this with release_calendar and indicator_query when a report needs actual-vs-consensus...' This guides when to invoke. Does not explicitly mention when not to use or provide alternative sibling tools, but context signals show many siblings, so the guidance is helpful.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds context: daily granularity from central-bank reference rates, multi-year history, optional technical indicators. No contradiction with annotations.

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

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

The description is well-structured and front-loaded with the core purpose and usage guidance. It is slightly lengthy due to detailed currency and indicator lists, but every sentence adds value. Could be trimmed slightly, but still effective.

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

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

Given the tool's complexity (currency pairs, technical indicators, date ranges) and the presence of an output schema, the description covers inputs and behavior comprehensively. It mentions output as raw rows/plain-text table, which is 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?

Schema coverage is 100%, baseline 3. The description adds value by explaining each parameter with examples, listing supported currencies and indicators, and clarifying defaults (end_date today, start_date ~5 years ago). This goes beyond the schema.

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

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

The description clearly states 'Get raw historical FX spot-rate rows for a currency pair' with specific examples. It distinguishes itself from the sibling 'forex_visual_artifact' by explicitly stating when to prefer this tool over that one, showing clear differentiation.

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

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

The description provides explicit guidance: 'Prefer this tool when the user explicitly wants a plain-text table... If the user asks more generally... prefer forex_visual_artifact'. It also lists supported currencies and indicator values, helping an AI agent decide when to use this tool.

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

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

Annotations already provide readOnlyHint and destructiveHint, so the description adds context about chart rendering via MCP Apps metadata, which is consistent and useful. No contradictions.

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

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

Two sentences, front-loaded with the most important distinction (chart vs raw data), and no unnecessary words.

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

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

For a tool with 5 parameters, no output schema, and strong annotations, the description is complete in explaining purpose, when to use, and what it returns (interactive chart). Minor gap: no mention of output format, but it's implied by 'chart metadata'.

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

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

Schema description coverage is 100%, so parameters are well-documented in the schema. The description adds no additional meaning beyond stating 'same payload as forex', which is sufficient given the schema coverage.

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

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

The description clearly defines the tool as providing the same payload as forex but with chart metadata for inline visualization, and distinguishes it from the plain forex tool by specifying the use case for chart rendering versus raw data.

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

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

Explicitly states when to prefer this tool ('by default for FX pair time-series requests') and when to use the alternative plain forex ('when the user explicitly asks for a table, raw values, JSON, CSV-style output, or exact row-by-row data').

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, indicating a safe read operation. The description adds value by noting 'transparent rule-based' behavior and async execution support, which are beyond annotations. However, it does not detail auth needs or specific 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 concise at two sentences, front-loaded with the core purpose. Every sentence adds value: the first explains what the tool does, the second highlights async capability. 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?

Despite having 10 parameters and no output schema, the description does not explain what the tool returns (e.g., performance metrics, equity curves). For a complex backtest, this is a significant gap; the agent lacks guidance on expected output format or post-backtest actions.

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

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

Schema description coverage is 100%, so the baseline is 3. The description does not add additional parameter-level meaning beyond what is already in the input schema, but it also does not introduce confusion.

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 runs a 'rule-based FX backtest on historical spot data using carry and/or momentum signals', specifying the resource (FX backtest) and action (run). It distinguishes from sibling tools like fx_trade_setup_task and event_impact_replay_task by explicitly focusing on backtesting with defined signals.

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

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

The description mentions it 'Supports MCP Tasks for async execution', providing some usage context, but does not explicitly state when to use this tool versus alternatives or when not to use it. No comparison with sibling task tools is given, so guidance is minimal.

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

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

Annotations already set readOnlyHint=true and destructiveHint=false. The description adds behavioral context by mentioning async MCP Tasks and listing the data sources used, which goes beyond the annotations.

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

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

The description is concise at two sentences, with the primary purpose front-loaded. Every sentence adds value without redundancy or fluff.

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 lacks an output schema, and the description does not explain what the tool returns (e.g., format, structure, or example). Given the complexity of building a setup, the return behavior is missing critical 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?

Schema description coverage is 100%, so the schema already documents all parameters. The description lists high-level components but does not add new semantic meaning beyond what is in the schema's parameter descriptions.

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

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

The description clearly states the tool builds a trader-oriented FX pair setup using specific components (spot context, macro differentials, catalyst risk, COT). It distinguishes from sibling tools like 'pair_intel_task' by focusing on setup generation rather than just querying data.

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

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

The description mentions async execution support but does not explicitly specify when to use this tool versus alternatives. It lacks exclusionary guidance or direct comparisons to sibling tasks, leaving usage context implied.

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

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

Annotations already indicate readOnlyHint and no destructive side effects. The description adds the async execution behavior, which is useful. No contradiction.

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

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

Two concise sentences: first states purpose, second adds execution context. No wasted words, front-loaded.

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

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

The description provides a good overview of the output (intelligence pack with data, analytics, timing) but lacks detail on structure. Given no output schema, this is adequate but not fully complete.

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

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

Schema coverage is 100%, so the schema documents all parameters well. The description adds no extra parameter-level meaning beyond the high-level purpose.

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 builds an intelligence pack for one indicator, combining series data, analytics, and release timing. This distinguishes it from sibling tools like indicator_query (raw data) or indicator_visual_artifact (visualization).

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 notes async execution for task-augmented requests, hinting at appropriate usage. However, it does not explicitly provide when-not-to-use or alternatives, leaving some ambiguity.

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

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

Annotations declare readOnlyHint, openWorldHint, destructiveHint. Description adds rich behavioral context: data sourced from official central banks/statistical agencies, response format (date, val, announcement_datetime), chart-ready default, pagination details, official-only filter, removal of private aggregator rows, and API key condition. No contradiction.

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

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

Well-structured, front-loads main purpose. However, the list of indicators is repeated both in description and in parameter descriptions, adding redundancy. Could be slightly trimmed without loss of meaning. Generally efficient.

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

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

Given the complexity (9 parameters, many indicators, pagination, external data source), the description covers all essential aspects: pagination behavior, response structure, prerequisite call to data_catalogue, API key condition, official-only policy. Output schema exists, so return values are handled. No gaps.

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

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

Schema coverage 100%, so baseline is 3. Description adds value beyond schema by explaining slug compound format, how pagination parameters interact, and the role of official_only. Also reinforces supported enums for currency and indicator. Provides examples for slug and other params.

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

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

Description begins with a specific verb+resource: 'Get a paginated historical time series of a single macroeconomic indicator for a currency'. Lists supported indicators and currencies, and explicitly contrasts with siblings by mentioning separate tools for commodities and forex. Clearly states what it does.

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

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

Provides explicit when-to-use: 'Use this for CPI/inflation, GDP, unemployment, policy rates...'. Includes prerequisites: 'Always call data_catalogue(currency) first'. Explains pagination and filtering via limit/offset/page. Mentions API key requirements for non-USD, and data sourcing policy.

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?

The description adds value beyond annotations by explaining that the tool returns MCP Apps metadata for chart rendering, and it mentions pagination behavior (inspect pagination.next_offset). Annotations already indicate read-only and non-destructive nature, so the description supplements this with useful 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 begins with a clear, front-loaded purpose statement, but it becomes verbose with a full list of supported indicators that is already present in the schema. While the structure is logical, the redundancy reduces conciseness.

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

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

Given the parameter count, no output schema, and rich annotations, the description adequately covers when to use the tool, key parameter usage, and behavioral traits. It could provide more detail about the response structure (e.g., MCP Apps metadata fields), but the existing information is sufficient for an AI to select and 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?

Although the schema has 100% description coverage, the description adds extra guidance on pagination ('use limit, offset, or page controls') and lists supported currencies and indicators, which reinforces but does not significantly enhance the schema. The added pagination advice provides some extra 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 explicitly states that this tool returns the same payload as indicator_query but with MCP Apps metadata for rendering interactive line charts in compatible clients. It clearly identifies the tool's specific function and distinguishes it from indicator_query.

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

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

The description provides explicit guidance on when to use this tool ('Prefer this by default for indicator time-series requests, especially when the user asks to show, tell, explain, compare, inspect a trend, or review a recent window') and when to fall back to indicator_query. It also gives pagination advice.

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

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

Annotations already declare readOnlyHint, openWorldHint, and destructiveHint. The description adds value by explaining the asynchronous MCP Tasks support and the announcement_datetime integrity boundary, which are not captured by annotations.

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

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

Two sentences with no wasted words. The main action and key concept are front-loaded, making it efficient for scanning.

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 query tool with well-documented parameters and safety annotations, the description covers the core functionality and async execution. It does not explain return format or edge cases, but those are partially mitigated by schema examples.

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

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

Schema description coverage is 100%, so the schema already documents each parameter thoroughly. The description does not add significant new parameter details beyond mentioning the 'as_of' timestamp relation to announcement_datetime.

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 a slice of a macro series known at a specific timestamp, using announcement_datetime as the boundary. It is specific about the resource and action, and the title aligns well.

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 point-in-time queries but does not explicitly state when to use this tool versus sibling tools like indicator_query. No exclusion criteria or alternative suggestions are provided.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds useful behavioral context: it combines catalogue, policy rate, GDP, and release-calendar data, and supports async execution. It does not detail output format but provides enough beyond annotations.

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

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

Description is two sentences, front-loaded with the core purpose and followed by an important async detail. No redundant or unnecessary 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 moderate complexity (combining multiple data sources) and that annotations and schema are well-covered, the description is largely complete. It explains what the tool does and mentions async capability. Minor gap: no description of output format, but overall adequate.

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

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

Schema coverage is 100%, with the currency parameter fully described (3-letter ISO, case-insensitive, supported values). The description adds no additional parameter information, so 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?

Description explicitly states 'Build a compact macro briefing for a currency by combining catalogue, policy rate, GDP, and release-calendar data.' The verb 'build' and the specific data sources clearly define the tool's purpose and differentiate it from sibling tools like macro_heatmap_task or macro_research_pack_task.

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

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

Description implies usage for creating a compact briefing when combining those data sources, and mentions async execution for task-augmented requests. However, it provides no explicit guidance on when not to use it or how it compares to other macro tools in the sibling list.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the read-only nature is clear. The description adds value by stating async execution support and detailing the output structure (latest values, changes, z-scores), which complements the annotations without contradiction.

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

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

Two sentences, each serving a distinct purpose: the first clearly states the core function and output, the second adds the async task capability. No extraneous words, front-loaded with the primary action.

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

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

Given the complexity (4 optional parameters, no required, no output schema), the description covers the main purpose and output format effectively. However, it lacks usage guidance and does not explain how parameters interact or what defaults apply, leaving some gaps for an agent unfamiliar with the domain.

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

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

Schema coverage is 100% with descriptions and examples for all parameters (end_date, currencies, indicators, start_date). The description does not add any extra parameter-level meaning beyond what the schema already provides, so the baseline score of 3 applies.

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

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

The description uses a specific verb 'Build' and clearly states the resource: 'cross-currency macro heatmap from indicator time series.' It explicitly mentions the output: 'matrix with latest values, recent changes, and z-scores.' This distinguishes it from sibling task tools like macro_briefing_task and indicator_intel_task, which serve different analytical purposes.

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

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

The description mentions 'Supports MCP Tasks for async execution' which indicates a usage context (async vs sync), but it does not explicitly state when to use this tool versus alternatives like macro_war_room_task or forex. It lacks 'when-not-to-use' guidance and does not reference sibling tools.

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

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

Annotations already indicate readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds that it returns 'official-source headline rows and lightweight keyword-derived affected_indicators and sentiment fields when a headline is classifiable.' This provides some behavioral detail beyond annotations but does not significantly expand on the annotation-provided traits.

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 consists of two concise sentences. The first sentence clearly states the purpose, and the second adds usage context. Every word serves a purpose without unnecessary elaboration.

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

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

With an output schema present, the description does not need to explain return values. It covers the tool's scope, currency list, and narrative contexts. The description is sufficient for an agent to understand when and how to use 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?

Schema description coverage is 100%, so the schema already documents all parameters. The description lists supported currencies again but does not add new meaning or usage details beyond what the schema provides. Baseline 3 is appropriate.

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

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

The description clearly states the tool returns 'recent official central-bank news and press-release headlines for a currency.' It specifies the verb (return) and resource (news headlines), and lists supported currencies. This distinguishes it from sibling tools like commodities, forex, and release_calendar.

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 explicitly says 'Use this when a report needs headline context for central-bank policy, inflation, employment, GDP, trade, fiscal, energy, or commodity narratives.' It provides clear context for when to use the tool, though it does not explicitly state when not to use it or compare to 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?

Annotations already provide readOnlyHint, openWorldHint, and destructiveHint. The description adds context about providing 'explicit assumptions and confidence notes' and supporting MCP Tasks for async execution, which are behavioral traits not covered by annotations. No contradictions.

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

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

Two sentences with no wasted words. The first sentence covers purpose and inputs, the second adds async support. Front-loaded and efficient.

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

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

Given the schema covers all parameters and annotations provide safety hints, the description is mostly complete. It mentions 'assumptions and confidence notes' hinting at output style, but could be more explicit about return format. The openWorldHint already suggests data availability may vary.

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

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

Schema coverage is 100% with detailed descriptions for all three parameters. The tool description does not add significant new meaning beyond what the schema provides for individual parameters, so the baseline of 3 is appropriate.

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

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

The description clearly states the tool classifies a currency's macro regime using specific economic indicators (policy rate, inflation, GDP, unemployment). The verb 'classify' and resource 'macro regime' are precise, and it distinguishes from sibling tools like macro_briefing_task and macro_heatmap_task.

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 does not explicitly state when to use this tool versus alternatives. There is no mention of when-not or which sibling tool might be more appropriate. Usage is implied by the classification purpose but lacks explicit guidance.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds context about persistence and async task support, but does not detail other behaviors like rate limits or pagination. No contradiction with annotations.

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

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

Two concise sentences, no redundant information. The first sentence clearly states the purpose, and the second adds context about async execution. Every sentence earns its place.

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

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

No output schema exists, and the description does not explain the structure or format of the returned research payload. For a tool that bundles multiple data types, more detail on the return value is needed to be fully complete.

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

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

Schema coverage is 100%, so the schema already describes all parameters. The description adds minimal extra meaning beyond grouping the parameters into categories (catalogue, history, timing, FX context), which does not significantly enrich understanding.

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

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

The description clearly states the tool bundles catalogue, indicator history, next release timing, and optional FX pair context into a research payload, which distinguishes it from sibling tools that focus on individual data types.

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

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

Mentions support for MCP Tasks for async execution but does not explicitly state when to use this tool versus others, nor does it provide exclusions or alternatives among the many sibling tools.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, establishing the tool as safe and non-mutating. The description adds that it 'builds' a cockpit and supports async execution, which is consistent and provides minor behavioral context beyond the annotations. However, it omits details like the format of the output or any 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 two sentences, with the first sentence efficiently summarizing the tool's output. The second sentence adds context about async execution. No words are wasted, though it could be slightly more structured.

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

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

The description explains what the tool does (builds a cockpit) but does not specify the output format or what the response will contain. Given the absence of an output schema, the description should provide more guidance on return values or structure. 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?

Schema description coverage is 100%, with each parameter having a clear description in the schema. The tool description itself adds no additional meaning beyond what the schema provides. Given the baseline of 3 for high coverage, this score is appropriate.

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

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

The description clearly states it builds a multi-panel macro market cockpit combining FX sessions, release queue, pair context, and risk alerts. This is specific and distinguishes it from sibling tools that focus on individual components (e.g., forex, release_calendar), though it does not explicitly differentiate from other task-based tools like macro_briefing_task.

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 such as macro_briefing_task or release_calendar. There is no mention of prerequisites, context, or exclusions. The only usage hint is that it supports MCP Tasks for async execution, which is not specific enough.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so safety is clear. The description adds valuable context about accounting for weekends and holidays, and explains the optional timestamp parameter for specific moments.

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

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

Two concise sentences with key information front-loaded. No wasted words; parameter explanation is integrated naturally.

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 output schema exists and annotations are provided, the description covers purpose, usage, parameter details, and behavioral nuances (weekends/holidays). It is complete for an agent to select and invoke correctly.

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

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

Schema coverage is 100% and both description and schema explain the 'at' parameter's purpose and format. The description adds context for its use case beyond the schema's description.

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

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

Clearly states the tool tells which FX sessions are open and when next opens/closes. The verb 'tell' and resource 'FX trading sessions' are specific. It does not explicitly distinguish from the sibling 'forex' tool, but the purpose is distinct enough.

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

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

Provides explicit when-to-use guidance with example user questions. Does not mention when not to use or suggest alternatives, but covers common use cases well.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description's mention of 'Explain' confirms read-only behavior. The description adds little beyond annotations, but does not contradict them, earning a baseline score.

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

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

The description is two sentences: first states purpose, second provides usage guidance. No wasted words, front-loaded with the most important 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 simplicity (no parameters, no output schema, clear annotations), the description fully covers what the tool does and when to use it. No further information is needed.

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 does not need to add parameter information. Schema coverage is effectively 100% (empty schema), and the baseline for 0 parameters is 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's purpose: explaining authentication modes for FXMacroData MCP across major clients and platforms. It uses a specific verb ('Explain') and resource ('authentication mode'), and distinguishes itself from sibling tools (which are data or task tools) by focusing on authentication guidance.

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 explicitly says 'Use this when the user asks whether to use OAuth, an API key, a bearer token...' providing clear context for when to invoke the tool. While it doesn't include negative examples or alternatives, it is sufficient for a simple guide tool.

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

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

Annotations already provide readOnlyHint, openWorldHint, destructiveHint. The description adds value by detailing the scope of explanations (tools, public vs subscriber, etc.). No contradictions.

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

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

Two sentences with clear front-loading of purpose and usage guidance. Every sentence is essential and earns its place.

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

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

For a tool with no output schema and zero parameters, the description sufficiently covers the purpose and usage. It could optionally mention return format, but current completeness is adequate.

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

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

No parameters, baseline 4. The description does not need to add parameter info as schema coverage is 100%.

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 explains the MCP server's capabilities, distinguishing it from sibling tools that perform specific tasks. It uses specific verb 'Explain' and resource 'MCP server capabilities'.

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?

Explicitly specifies when to use this tool: 'when a user asks what is available, why visuals are not showing, or how to get the same result in a different interface.' No alternatives are needed as it is a meta-tool.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds useful behavioral context (metadata-first, grouped by endpoint type) and lists supported values, which aids understanding 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 two sentences plus a list of supported values, front-loaded with the primary purpose. Every sentence adds necessary context without 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?

While the description covers the main selection logic, it does not mention the 'component' parameter, which is conditionally required for the bop dataset. The schema covers this, but for complete context, the description should note this dependency. Output schema exists, so return values are not required.

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

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

Schema description coverage is 100%, giving a baseline of 3. The description adds value by explaining the role of endpoint_type and dataset in selecting the series family, and lists supported values, which goes beyond the schema's individual parameter descriptions.

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

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

The description clearly states the tool's purpose (Get metadata-first official dataset payloads grouped by API endpoint type) with a specific verb and resource. It distinguishes itself from siblings by focusing on 'official dataset families' but does not explicitly contrast with any 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 explains how to use the tool by selecting endpoint_type and dataset, and lists supported values. However, it provides no guidance on when not to use this tool or mention of alternative tools for similar data retrieval (e.g., data_catalogue).

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, which the description does not contradict. The description adds that it combines three context types and supports async, but does not disclose additional behavioral traits like required permissions or data 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 consists of two concise sentences with the primary purpose front-loaded. No unnecessary words; each sentence adds value.

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

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

Given the presence of annotations and full schema coverage, the description is fairly complete. It lacks an explicit statement of return values, but the output is described as an 'intelligence pack', and no output schema exists. Minor gap: no mention of date range usage, but parameters cover that.

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

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

Schema description coverage is 100%, so the schema already explains all parameters. The description adds no extra meaning beyond what is in the property descriptions, meriting a baseline score of 3.

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

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

The description clearly states the verb 'Build' and the specific resource 'intelligence pack for an FX pair', listing three components (policy-rate spread, spot-rate, release-timing). It distinguishes from siblings like fx_backtest_task and fx_trade_setup_task by focusing on an intelligence pack.

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 async execution via MCP Tasks, but does not specify when to use this tool vs alternatives (e.g., other FX tasks) or provide exclusion criteria. It lacks explicit guidance on context.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds quick health check context and reaffirms non-destructive nature. No contradictions.

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

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

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

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

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

Given zero parameters, annotations present, and output schema exists, the description fully covers the tool's role and usage 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?

No parameters; schema coverage is 100%. The description does not need to add param semantics. Baseline 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?

Clear verb 'Quick health check' specifies action, and 'confirms the FXMacroData API and MCP server are reachable' identifies the resource. Distinguishes from sibling tools by being a connectivity test.

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?

Explicitly states 'Use this only if other tools fail unexpectedly — it is not needed before normal calls.' Provides clear when-to-use and when-not-to-use guidance.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the description's claim of building a chart is consistent. No additional behavioral traits (e.g., return format, data sources, or limitations) are disclosed beyond the annotations.

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

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

The description is a single sentence of 19 words with no redundant phrasing. It is front-loaded and efficiently conveys the tool's purpose.

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

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

The description is adequate for a simple read-only tool but lacks details about the output (e.g., chart format, URL, data source). Given no output schema, the description could be more specific about what is returned.

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

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

Schema coverage is 100% with descriptions for all 4 parameters, so the description does not add extra meaning. The baseline of 3 is appropriate as the schema already handles the 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 the tool builds a two-series chart comparing policy-rate history of base and quote currencies to visualize rate differential for an FX pair. It is specific and distinguishes from sibling tools like forex_visual_artifact which are more general.

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 policy rate differential visualization but does not explicitly state when to use over alternatives or provide exclusion criteria. Sibling tools exist (e.g., forex_visual_artifact) but no guidance is given 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?

Annotations indicate readOnly and non-destructive nature. The description adds that it estimates directional spot impact using a heuristic, which is a key behavioral trait, and mentions async support, providing context beyond annotations.

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

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

Two sentences: the first conveys the core purpose, the second adds async support context. No wasted words, and the essential information is front-loaded.

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

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

The description explains what the tool does but omits output format or how results are returned. Since there is no output schema, the description should clarify the return structure. It also lacks details on prerequisites or error handling.

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

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

Schema coverage is 100%, and all parameters have clear descriptions in the schema. The tool description does not add new information about parameters but is adequate given the high schema coverage.

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

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

The description clearly states it runs a policy-rate spread what-if scenario for an FX pair and estimates directional spot impact using a heuristic elasticity assumption. It specifies the verb 'run', the resource 'scenario', and the method, distinguishing it from sibling tools like 'quant_scenario_lab_task'.

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

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

The description mentions async execution via MCP Tasks, providing context for when to use it, but does not explicitly state when not to use or compare to alternatives. It implies usage for scenario modeling but lacks exclusions.

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

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

Annotations indicate readOnlyHint=true, openWorldHint=true, and destructiveHint=false, which the description complements by stating the tool analyzes risk and supports async tasks. It provides behavioral context beyond annotations (e.g., async execution), but does not detail what 'event-driven catalyst risk' entails or the output format.

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

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

Two sentences, no fluff, front-loaded with purpose. Every sentence adds value. Highly concise for a tool with 3 parameters and no output 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?

Given no output schema, the description could mention the output format (e.g., 'returns a risk report'). It also lacks guidance on when to use this tool vs. similar sibling tasks. Adequate but 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?

Schema coverage is 100% with each parameter having a description and examples. The tool description does not add meaning beyond the schema; it only provides high-level context. Baseline 3 is appropriate as the schema already handles parameter semantics adequately.

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

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

The description clearly states the tool's purpose: 'Analyze a multi-position FX book for concentration, stress exposure, and event-driven catalyst risk.' The verb 'Analyze' and the specific resource 'multi-position FX book' precisely define its function, distinguishing it from sibling tools that handle data retrieval or other analyses.

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

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

The description mentions 'Supports MCP Tasks for async execution when clients send task-augmented requests,' which provides invocation context but does not explicitly guide when to use this tool over alternatives like fx_backtest_task or macro_briefing_task. No exclusions or comparative guidance is given.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description's value is in adding async execution behavior. It correctly characterizes the tool as a read-only scenario runner. The description adds useful context beyond annotations, though more details (e.g., rate limits, data freshness) could be included.

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

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

Two sentences, no redundancy. Every part adds value: the first defines the tool's core function, the second specifies async execution support. Ideal concise structure.

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

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

With 9 parameters and no output schema, the description should hint at the return structure or post-execution behavior. It does not describe what the tool returns (e.g., a scenario projection object). This gap reduces completeness despite clear purpose.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds no new parameter meaning beyond what the schema provides; it mentions 'deterministic projection' and 'stress percentiles' without elaborating on how parameters map to these concepts.

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 runs an expanded quant scenario for an FX pair with deterministic projection, stress percentiles, and horizon assumptions. It distinguishes from siblings by mentioning MCP Task async execution, which is unique among sibling tools (e.g., fx_backtest_task, policy_scenario_modeler_task).

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

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

The description mentions support for async execution when clients send task-augmented requests, providing some context. However, it lacks explicit guidance on when to use this tool versus alternatives, such as policy_scenario_modeler_task or fx_backtest_task, and does not state when not to use it.

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

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

Annotations already indicate read-only and non-destructive behavior. The description adds key behavioral details: unbounded calls return only future releases, timezone parameter adds an extra field, and output format includes ISO-8601 and local timestamps. This goes beyond what annotations provide.

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 clear and front-loaded but somewhat verbose, as it repeats full lists of currencies and indicators that are already in the schema. It could be more concise by omitting these repetitions. Still, the structure is logical.

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 covers return values (ISO-8601 timestamps, timezone fields, release name, currency). It also clarifies boundary cases like past data handling and parameter usage for date ranges, making it complete for a tool with 5 parameters.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining that timezone enables an extra field, indicator narrows the series, and start_date/end_date are used when a date range is mentioned. This enriches parameter understanding beyond the schema.

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

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

The description clearly states the tool's purpose: 'Get upcoming scheduled macroeconomic release timestamps for a currency.' It provides specific use cases like 'when the user asks when is the next CPI/GDP/payrolls/policy decision,' making it easy for an agent to understand when to invoke this tool.

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

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

The description gives explicit guidance on when to use the tool (e.g., for next release timestamps, planning trades) and what not to do (e.g., 'do not show stale past rows unless explicitly asked'). It also instructs on how to present results, which helps in agent behavior.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds that it triggers inline rendering in compatible clients, which is useful behavioral context. No contradictions.

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

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

The description is unnecessarily long because it repeats the full list of currencies and indicators already present in the schema. The core guidance is front-loaded, but the lists add verbosity. Could be shorter.

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?

No output schema is provided, and the description does not detail the return payload beyond saying it's the same as release_calendar. Since the tool is for visual rendering, the return format is likely handled by the client, but more detail would be beneficial for agent understanding.

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

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

Schema coverage is 100%, so the description is not strictly needed, but it adds value by specifying when to use optional parameters (e.g., 'when the user names a specific series'). The lists of values are redundant with schema.

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

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

The description clearly states it returns the same payload as release_calendar but is intended for visual rendering by compatible clients. It explicitly differentiates from the sibling tool by naming it as a visual artifact.

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

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

Provides explicit guidance on when to use this tool vs. release_calendar: prefer this for 'show, display, visualize' requests, and plain for raw table/JSON/text. Also advises when to pass optional parameters like indicator, start_date, end_date, and timezone.

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

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

Annotations already declare readOnlyHint and openWorldHint, so the description adds value by explaining the scoring methodology and async task support. No contradictions. It could further detail rate limits or data freshness but overall transparent enough.

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

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

Two sentences, front-loaded with core purpose, no redundancy. Every word earns its place.

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

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

While the description explains what the tool does and its async nature, it omits details about the output format or return values. For a tool with no output schema, this leaves some gap. Adequate but not complete.

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

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

Schema description coverage is 100%, so baseline is 3. The description does not add any additional parameter semantics beyond what the schema provides. Horizon_events is not mentioned in the description, but schema covers it with defaults and examples.

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

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

Description uses specific verb 'Score' and defines resource 'upcoming releases for a currency pair', with clear methodology 'release-calendar proximity and indicator-level heuristics'. This distinguishes it from sibling tools like 'event_impact_replay_task' or 'pair_intel_task' which likely have different scoring approaches.

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

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

Description mentions async execution via MCP Tasks but provides no guidance on when to use this tool versus sibling tools like 'pair_intel_task' or 'indicator_intel_task'. No exclusions or alternative recommendations are given, leaving selection decisions ambiguous.

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

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

Annotations already declare readOnlyHint and destructiveHint. The description adds details on output components (composite score, regime label, pagination, data quality), which provides useful behavioral context beyond annotations and does not contradict them.

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 with two sentences that front-load the core purpose and immediately follow with usage guidance. Every sentence adds value with no redundancy.

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

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

Given the tool's simplicity (two optional date params, output schema exists), the description adequately covers purpose, usage context, and key output components. It is sufficiently complete for a read-only query tool.

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

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

Schema description coverage is 100% for both parameters. The tool description does not add any parameter-specific information beyond what the schema already provides, so it meets the baseline expectation.

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 a global risk-on/off sentiment series for FX regime analysis, including specific output components. It also differentiates from siblings by specifying its use as a precursor to classification of FX conditions.

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 explicitly advises using this tool for cross-asset regime context before classifying certain FX conditions. While it lacks explicit 'when not to use' guidance, the context is clear enough to guide selection.

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

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

Annotations (readOnlyHint=true, destructiveHint=false) already indicate safe, non-destructive behavior. The description adds transparency by detailing the return fields (e.g., monthly average, median return, win rate, sample size), providing full context without contradicting annotations.

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

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

The description is concise at two sentences, front-loading the purpose and usage. It is well-structured but could be slightly more scannable, e.g., using bullet points for output fields.

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 moderate complexity, the presence of an output schema (indicated true) reduces the need to detail returns. The description covers inputs and outputs adequately, though it lacks examples of parameter combinations.

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

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

Schema description coverage is 100%, so baseline is 3. The description does not add new parameter semantics beyond what the schema provides; it only lists output data. Hence, no extra value for parameter understanding.

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

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

The description clearly states the tool retrieves monthly return seasonality for FX pairs or XAU/USD. It lists specific use cases like seasonal patterns, month-of-year tendency, and historical win rate, which distinguishes it from sibling tools that likely provide other analytics.

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 explicitly advises usage when the user asks for seasonal patterns, month-of-year tendency, or gold seasonality. It does not, however, mention when to avoid this tool or suggest alternatives like other forecast tools, which would strengthen guidance.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true, covering the non-destructive and navigational aspects. The description adds that it returns a checkout path, but does not elaborate on authentication requirements or potential side effects (e.g., opening an external site). The description adds marginal value beyond annotations.

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

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

The description is two sentences, front-loaded with the action, and contains no unnecessary words. Every word serves a purpose.

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

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

The tool is simple with no parameters, and the description covers its purpose and return value. It could mention if a user session is required or if the checkout path opens in a new window, but for a basic subscription access tool, it is sufficiently complete.

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

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

The tool has zero parameters, and schema coverage is 100% (no parameters to document). According to rubric, 0 params = baseline 4. The description does not need to add 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 states the tool's action ('Open subscription options') and its trigger condition ('when a user needs to unlock MCP app visuals, charts, and advanced analytical tools'). It also specifies the outcome ('Returns a direct checkout path'). This distinguishes it from the sibling tools, which are focused on data retrieval and analysis rather than subscription management.

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 explicitly describes when to use this tool: when a user needs to unlock premium features. It implies that for free features, other sibling tools (e.g., indicator_query) would be used. However, it does not provide explicit exclusions or a list of alternatives.

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