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

Annotations already cover safety (readOnly, idempotent, openWorld), so the bar is lower, yet the description still adds real behavior: automatic price fetching, the tickers precondition, and that omitting the indicator returns the available-indicator list. It does not mention rate limits or the cost of the 19-parameter surface, but the added context is meaningful.

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

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

Two short blocks, front-loaded with the domain and the auto-fetch behavior, followed by the indicator list. The indicator enumeration mirrors the enum but is reasonable given the tool's dispatch-style design; no filler sentences.

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 19-parameter dispatcher with an output schema, the description covers the required selection (indicator), the data precondition (tickers), and the discovery behavior (omitting indicator lists options). Remaining details — window defaults, benchmark_ticker, show_columns — are fully specified in the schema, so nothing critical is missing.

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 all 19 parameters are already documented with defaults (window defaults per indicator, standardize, growth, etc.). The description only restates the tickers comma-separated format and the indicator requirement, adding little beyond the schema — the baseline 3 for high coverage 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 names the specific resource domain ('Market breadth technical indicators') and enumerates the exact indicator family it computes (McClellan, OBV, A/D, Chaikin), so an agent can tell it apart from momentum/volatility siblings by scope. It does not explicitly contrast itself with those siblings, which is the only thing keeping it from a 5.

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

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

It gives one genuinely useful routing hint ('Applied to price data automatically — no need to fetch prices first') and a precondition (tickers must be set). However it never says when to choose breadth versus momentum/volatility/performance siblings, so selection guidance is only implied.

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

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, covering the safety profile. The description adds the scoping fact that it doesn't need tickers or countries, but it doesn't disclose other behavioral aspects like pagination behavior or response shaping beyond what's in the schema. With annotations provided, this meets a baseline but adds limited extra context.

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

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

The description is concise: a short purpose statement followed by a list of 27 indicators. The list is necessary to inform the agent of valid choices and avoids excessive prose. It front-loads the core purpose and is structured clearly, though the indicator list could have been linked to the enum rather than repeated.

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 21 parameters, the description is notably incomplete. It does not explain that parameters like price_lower, volume_higher, etc., are filters applied only to certain indicators, nor does it clarify which indicators accept which filters. The schema's placeholder descriptions ('Value for price_lower') add little. While an output schema exists, the description still fails to give the agent a coherent picture of how to combine the indicator with the other parameters. This is a significant gap given the complexity.

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% (though most are trivial 'Value for X'), so the baseline is 3. The description itself adds no parameter semantics beyond listing possible indicator values, which are already in the enum. The schema's indicator description (with the example and behavior of omission) is helpful, but it's not from the tool description. Thus, no added value from the 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?

The description clearly states the tool provides 'market discovery tools' (lists, screeners, gainers/losers, etc.), and the 'No tickers or countries needed' hint distinguishes it from sibling tools dealing with specific instruments or metrics. It's specific enough about the resource category, though it covers a broad set of sub-functions via the 'indicator' 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 explicitly says 'No tickers or countries needed', which tells agents when to use this tool (for market-wide discovery without specific instrument identifiers). It lists available indicators, giving a clear menu of use cases. However, it doesn't explicitly name alternative tools for scenarios with tickers or countries, so guidance is clear but not exhaustive.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and openWorldHint=true, so the agent knows this is a safe, cached, external-data operation. The description adds no meaningful behavioral traits beyond what annotations provide — no mention of rate limits, data-source constraints, or output behavior. With annotations covering the safety profile, a 3 is appropriate for adding little extra 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 first sentence front-loads the test taxonomy, and the second sentence provides usage context and a key parameter example. However, the 48-item 'Available indicators' list is a repetitive dump of the enum already present in the schema, adding noise and length without new 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 a tool with 80 parameters and 48 sub-indicators, the description is surprisingly thin on how to select the correct indicator and which parameter combinations matter. It doesn't cover rate limits, data availability, or output format, though an output schema exists. More context on indicator selection and parameter interactions would be needed for an agent to call this correctly without trial and error.

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 80 parameters, including indicator-specific defaults and required-by relationships. The description only restates the tickers='AAPL' format, adding marginal value 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 opening clause names a specific set of statistical tests (unit root, cointegration, Granger causality, ARCH-LM, etc.) on price or return series, which is a specific verb+resource. However, it does not explicitly differentiate from siblings like 'models' or 'performance' that could also involve returns, though the test taxonomy is reasonably distinctive.

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

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

It provides a use case ('pairs-trading/spread-modeling foundations or diagnosing return-series properties') and a required-parameter instruction ('Requires tickers=...'), but there are no explicit when-not-to-use or alternative tool comparisons. The 48-indicator list is a menu, not guidance on selection.

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

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, so the safety profile is covered. The description adds operational context such as ticker formatting and indicator availability, but does not disclose rate limits, auth requirements, or other behavioral traits. It also states tickers are required despite the schema marking tickers optional, which may mislead.

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

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

The description is front-loaded with purpose and key usage notes, which is good. However, the long list of available indicators duplicates the enum already present in the schema, bloating the description without adding new 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 rich input schema (100% coverage), output schema, and annotations, the description covers the core purpose, indicator selection, and main optional parameters sufficiently. The many advanced parameters are documented in the schema, so the description does not need to repeat them, though the ticker requirement inconsistency is a minor gap.

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

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

Schema description coverage is 100%, so parameter meanings are already fully documented. The description mostly repeats enum values and comma-separated ticker syntax, though it does add a useful 'use instead of raw financial statements' framing. The claim that tickers are required conflicts with the schema and reduces added 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 names the resource category (pre-computed efficiency ratios) and gives concrete examples such as asset turnover and cash conversion cycle. It distinguishes itself from raw financial statements, but does not explicitly differentiate from sibling category tools like profitability or liquidity.

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

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

It states a key use case: 'Use instead of raw financial statements,' and documents required ticker formatting and supported parameters like quarterly and start_date/end_date. It lacks explicit when-not guidance or comparisons to sibling ratio tools, but the intended context is clear.

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

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

Annotations already declare readOnlyHint, idempotentHint and openWorldHint, so the safety profile is covered. The description adds useful dispatch behavior (tickers vs countries, indicator acting as a sub-selector) but says nothing about pagination, date-range defaults, or result-shaping behavior beyond what the schema already documents.

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?

Three short sentences, front-loaded with the domain and immediately followed by the parameter-routing rules and the indicator list. Minimal waste; the indicator enumeration is somewhat redundant with the enum in the schema but aids quick 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?

With an output schema present, return-value detail is unnecessary, and the 13-parameter schema is fully self-documented. The description covers the essential decision (which of the three indicators and which entity parameter), leaving only minor gaps around date defaults and smoothing behavior that the schema handles.

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 schema coverage is 100%, the description adds cross-parameter semantics the schema cannot express: tickers= applies to ESG scores while countries= applies to carbon/renewable indicators, and it highlights that this tool uniquely accepts both. That is meaningfully more than the per-field descriptions provide.

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

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

The description opens with a specific domain statement ('Environmental and ESG data') and enumerates the three indicators the tool exposes, so an agent knows this is ESG/carbon/renewables, not general financial data. It does not explicitly contrast with sibling categories like macroeconomics or market_data, but the domain is unmistakable.

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

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

It gives concrete usage conditions: 'For ESG scores use tickers=...', 'For carbon footprint and renewable energy use countries=...', and notes rolling/trailing support. This is real routing guidance that maps indicator to parameter, though it never names an alternative sibling tool or states when NOT to use this one.

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

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, so the safety profile is covered. The description adds that parameters are passed directly without tickers or countries, which is helpful behavioral context, but it does not disclose anything further about execution, error handling, or output behavior beyond what the output schema provides.

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

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

The description is front-loaded with the scope and the 'no tickers' caveat, then lists available indicators, which is reasonable for a gateway tool. It is somewhat repetitive because it names metrics in the opening parenthesis and again in the explicit indicator list, but that redundancy helps map human-readable concepts to exact enum values.

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 40 parameters, an output schema, and read-only/idempotent annotations, the description covers the essential calling context: the domain, the required indicator concept, and the direct-argument usage style. It does not explain which parameters apply to which indicator, but that detail is largely available in the schema and output schema.

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

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

Schema description coverage is 100%, so the structured schema already documents all 40 parameters, setting the baseline at 3. The description adds only a general note that bond/derivative parameters like face_value, coupon_rate, maturity, and spot_rates should be supplied directly, which is marginally useful but does not meaningfully extend 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 names a specific domain (bond and derivative valuation) and enumerates twelve concrete calculations, so an agent can tell it apart from generic siblings like valuation or risk. However, it does not explicitly differentiate itself from potentially overlapping siblings such as options or rates, which limits it from a 5.

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

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

It gives one useful usage note ('No tickers or countries needed — provide bond/derivative parameters directly as arguments') and lists available indicators, which implies when the tool is relevant. But it provides no explicit when-to-use or when-not-to-use guidance relative to the many sibling category 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, idempotentHint and openWorldHint, so safety is covered. The description adds operationally important context beyond that: the tickers= prohibition, the countries-required constraint, and how rolling/trailing transform the returned series (moving average vs trailing sum). It stops short of describing return shape, which is acceptable given an output schema exists.

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

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

Front-loaded with purpose and hard constraints before the indicator enumeration; every sentence carries practical information. The enumerated indicator list is somewhat redundant with the schema enum, costing a little conciseness.

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

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

For an 11-parameter tool with an output schema, the description covers the non-obvious constraints (countries required, no tickers, quarterly/rolling/trailing behavior). Remaining params (lag, growth, standardize, show_columns) are adequately documented in the schema, so nothing critical is missing for correct invocation.

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 would be 3, but the description adds real meaning: it warns that countries is effectively required despite the schema default of empty string, and clarifies the rolling (smoothing) vs trailing (N-period sum) distinction with a concrete quarterly example. The indicator list merely duplicates the enum, which does not earn extra credit.

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

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

States a specific resource class (government fiscal metrics by country) and enumerates the exact metrics available, so an agent can immediately see this covers debt/deficit/expenditure/revenue/tax/trust rather than generic macro data. It is clearly distinguishable from siblings like macroeconomics, rates, and fixed_income.

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?

Gives concrete usage rules: countries must be specified (with a literal example), do NOT use tickers=, and how to enable quarterly/rolling/trailing. It does not, however, say when to prefer this tool over sibling tools such as macroeconomics or rates, so routing among siblings is left partly to inference.

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

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

Annotations already cover the read-only/idempotent profile, and the description adds non-obvious behavior beyond them: two indicators require a FRED API key and return only a 'United States' column regardless of countries=, and gmdb_source switches to an independent provider with different coverage. That is meaningful operational context the annotations do not supply.

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?

It is front-loaded with the resource and the hard 'countries=' requirement, then the indicator list, and closes with the FRED caveat. The only mild redundancy is restating rolling/trailing behavior already in the schema, but the density is justified for a 13-parameter tool.

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

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

With an output schema present, return formatting need not be explained, and the description covers the indicator set, source caveats, auth requirement and parameter behaviors. It is close to complete; adding a note on default date range behavior or the FRED-key failure mode would close the remaining gap.

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

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

Schema coverage is 100%, so the schema already documents lag, growth, period, rolling, trailing, dates, countries, quarterly, standardize and show_columns. The description reinforces rolling/trailing semantics and the countries requirement but adds no syntax or format detail beyond the schema, so the baseline 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 states a specific resource ('Labour and social metrics by country') and enumerates the exact indicators available, so the agent knows what content lives behind the generic name 'jobs'. It also draws a boundary against ticker-based tools ('Do NOT use tickers='), though it does not distinguish itself from close siblings like 'macroeconomics' or 'government'.

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

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

It gives explicit usage constraints: 'Requires countries=United States', comma-separated multi-country syntax, and an exclusion ('Do NOT use tickers= for this tool'). What it lacks is guidance on when to prefer this tool over the government/macroeconomics siblings, so it is clear context without full routing logic.

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

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, so the safety profile is covered. The description adds that values are pre-computed and lists the supported indicators, but says nothing about response shape, empty results, or rate/coverage limits. Adequate, not rich.

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

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

Front-loaded with the resource and metric list, then usage notes, then indicators. Tight overall, though the indicator enumeration largely duplicates the schema enum and costs tokens the schema already spends.

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 and full parameter descriptions, the description needn't explain returns; it covers the primary tickers/date/quarterly controls. Slight gap: it asserts 'Requires tickers=...' while the schema marks tickers optional with a default and only 'indicator' required, which could mislead a caller.

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 lag, days, growth, trailing, standardize, show_columns, and benchmark_ticker. The description merely echoes tickers/quarterly/start_date/end_date, adding no syntax or semantics beyond the structured fields — the baseline 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?

States a specific verb+resource ('Pre-computed liquidity ratios') and enumerates the exact metrics covered, which cleanly separates it from siblings like solvency, profitability, and efficiency. An agent can select it without opening the enum.

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?

Gives a clear 'use instead of raw financial statements' routing rule plus ticker and date-range usage notes, but never frames when to prefer this over the sibling ratio families (solvency, profitability) that an agent is choosing among. Context is present, exclusions are not.

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

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

Annotations already declare readOnlyHint/idempotentHint/openWorldHint, so the safety profile is covered; the description goes further by disclosing the FRED API-key dependency, that those indicators return only a 'United States' column regardless of countries=, that OECD vs GMDB are independent providers with different coverage, and that the forward curve returns dated futures contracts rather than a country series. The truncated trailing sentence leaves some behavioral description incomplete.

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 opening parenthetical enumerates dozens of indicators already present verbatim in the indicator enum, which is pure duplication, and the single enormous run-on sentence buries the most actionable guidance (source toggles, exclusions) mid-text. It also cuts off mid-sentence, so it is oversized and poorly front-loaded simultaneously.

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

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

With an output schema present, return-value explanation is unnecessary, and the description covers the high-risk gotchas for a 19-parameter mega-tool: required argument format, source switching, special-cased indicators, and the API-key caveat. Only the truncated final sentence leaves an evident gap.

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

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

Schema coverage is 100%, so the baseline is 3, but the description adds genuine cross-parameter semantics: the countries= format, that commodity= replaces countries= for one indicator, that tickers= is invalid here, and how rolling/trailing alter the returned series. These constraints are not fully expressible in the flat 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?

States the verb+resource clearly ('Macroeconomic indicators by country') and enumerates the covered indicator families, plus calls out special cases like the commodity forward curve. However, the purpose statement is padded with a sprawling parenthetical list that duplicates the schema enum rather than crisply characterizing the tool, and the text is truncated mid-sentence.

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 operational routing: countries='United States' is required, 'Do NOT use tickers= for this tool', get_commodity_forward_curve needs commodity= instead of countries=, and get_consumer_price_index switches sources via oecd_source=true. It also flags the six US-only FRED-backed indicators and their API-key requirement, all of which steer correct invocation.

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

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

Annotations already establish readOnlyHint=true, idempotentHint=true and openWorldHint=true, so the safe-read profile is covered. The description adds genuinely non-obvious behavior: the ticker requirement and the fact that two indicators are ticker/country-agnostic snapshots that silently ignore most other parameters. It does not discuss rate limits or response size limits beyond the show_columns tip.

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?

Purpose and routing rule are front-loaded in the first two sentences, and the special-case note follows. The trailing 'Available indicators' list duplicates the enum already in the schema verbatim, which is wasted length, though it does aid quick 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 20-parameter aggregator with an output schema, the description covers routing, prerequisites, and the notable indicator quirks. The gaps are the opaque placeholder parameters, which neither the description nor the schema explains, but the presence of an output schema means return values need not be described here.

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 per-parameter baseline is 3. The description adds cross-parameter semantics the schema does not: the required tickers format, the conditional irrelevance of parameters for two indicators, and the show_columns workflow for reducing token usage. It does not clarify the many placeholder parameters (fill_nan, show_errors, enforce_source, divide_ohlc_by), which remain 'Value for X' in 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 names a concrete resource (raw financial data) and enumerates the data families it covers (prices, statements, profile, quotes, calendars, COT report). It explicitly distinguishes itself from the sibling family of computed-metric tools ('for pre-computed ratios, performance, risk, or model metrics use the dedicated tools instead'), so an agent can route without opening the schema.

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

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

It gives an explicit when-to-use ('ONLY for raw data needs') and a when-not with the alternative class of tools to use instead. It also states the hard prerequisite (tickers='AAPL', comma-separated) and the edge case that get_market_risk_premium and get_commitment_of_traders ignore most other parameters.

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

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

Annotations already declare readOnlyHint, idempotentHint and openWorldHint, so the safety profile is covered and the description only needs to add non-obvious behavior. It adds that resolution is per-indicator and that several inputs are optional, but not much beyond the schema. It also asserts 'Requires tickers=AAPL' while the schema marks only indicator as required, which is a mild inconsistency with structured data.

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 opening sentence is well front-loaded and the usage line is terse. The 'Available indicators:' block duplicates the indicator enum wholesale, which is pure token bloat in the tool description for a 25-param tool, though it does map human-readable model names to the enum values.

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?

An output schema exists so return values need no explanation, and annotations cover the safety profile. For a 25-parameter tool, however, the description only covers four inputs and leaves many vaguely documented parameters (periods, diluted, show_full_results, cash_flow_type) to the schema, and does not differentiate this tool from the valuation sibling.

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% and 1 of 25 parameters carries an enum, so the schema does the heavy lifting; baseline is 3. The description restates tickers/quarterly/start_date/end_date semantics that already exist verbatim in the schema and adds no new meaning for the many bare 'Value for X' parameters.

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

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

Names a specific resource (pre-computed financial models) and enumerates the concrete metrics it produces (WACC, DuPont, DCF, Altman Z-Score, etc.), so an agent knows exactly what family of computations this covers. It does not, however, differentiate itself from siblings like valuation or profitability, which overlap with several of the listed indicators.

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?

Gives operational usage for the main inputs (tickers comma-separated, quarterly=true, start_date/end_date), which is real guidance for calling it. It never states when to prefer this tool over the valuation/solvency/profitability siblings, so the routing decision between overlapping tools is left to inference.

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

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

Annotations already declare readOnlyHint, idempotentHint and openWorldHint, so safety is covered. The description usefully adds that price data is applied automatically and that tickers/indicator are the driving inputs, but says nothing about output shape, latency, rate limits, or what the many window parameters do per indicator.

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 opening two sentences are well front-loaded and earn their place. The trailing list of 22 indicator names duplicates the enum already present in the schema verbatim, adding bulk without new 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?

For a 27-parameter tool with a full output schema, the description covers the essential contract (indicator selection plus tickers) but leaves the interaction between indicator choice and the numerous window parameters entirely to the schema. It is adequate but does not reduce the trial-and-error of configuring per-indicator windows.

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 27 parameters, and the description is not obliged to compensate. It reinforces only the tickers and indicator parameters, adding the comma-separated multi-ticker convention, which is marginally helpful but below the schema's own detail level.

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 first sentence names a specific resource ('momentum technical indicators') and enumerates the families (RSI, MACD, Stochastic, Williams %R, Aroon), so an agent knows it is the technical-indicator calculator rather than a raw price fetcher. It stops short of distinguishing itself from siblings like volatility or market_data, which also deal with price series.

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

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

It gives concrete invocation guidance ('Requires tickers="AAPL"', comma-separated for multiples) and notes that prices are fetched automatically. It never says when to prefer this over volatility, models, or market_data, nor when-not to use it, so selection guidance is only implied.

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

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, so the safety profile is covered. The description adds nothing behavioral beyond annotations — no mention of data-source/auth requirements, compute cost for Monte Carlo or simulation indicators, or what happens when an indicator's params are omitted.

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 purpose sentence is front-loaded and useful, but roughly half the text is a verbatim re-listing of the 33 enum values already in the schema. That duplication inflates the description without adding selection-relevant information, which is a significant structural 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?

For a dispatcher exposing 39 parameters and 33 indicators with an output schema present, the description never explains how the parameter set maps to individual indicators — an agent must infer from scattered schema hints which of the 39 params matter for a chosen indicator. The critical per-indicator parameter guidance is absent from the description.

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% across 39 parameters, so baseline is 3. The description's ticker-format note ('comma-separated values for multiple tickers') merely restates the schema's own text, and the indicator list duplicates the enum, adding no semantic depth about which params apply to which indicator.

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

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

The description names the resource (option pricing and Greeks) and enumerates the model families it covers (Black-Scholes, binomial tree, implied volatility), so the domain is unambiguous. It does not differentiate itself from siblings like volatility, models, or econometrics, which partly overlap this territory.

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 only usage guidance is a prerequisite ('Requires tickers="AAPL"'), which is already documented in the schema. There is no statement of when to select this tool over volatility/models, and no guidance on choosing among the 33 registered indicators beyond listing their names.

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

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

Annotations already declare readOnly, idempotent, and openWorld, so the safety profile is covered. The description adds useful context that prices are fetched automatically and tickers accept comma-separated values. It adds nothing about how indicator-specific parameters behave or what happens with unknown tickers.

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

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

Front-loaded with the core purpose and the two operational facts that matter (auto price fetch, tickers syntax). Reasonably tight, though the full enumeration of 13 indicator names duplicates the schema enum and consumes tokens without new 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?

A 21-parameter tool with only 1 required parameter is complex, and the description leaves the relationship between parameters and the chosen indicator unexplained (e.g., that af_start/af_increment/af_max apply only to get_parabolic_sar, or that window defaults differ per indicator). The output schema covers return values, so that omission is acceptable, but the parameter-to-indicator mapping gap is significant.

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 21 parameters, including tickers formatting and the indicator enum. The description only repeats the tickers format and lists the indicator values verbatim, adding no meaning beyond the structured fields (e.g., which params apply to which indicator).

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

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

States a specific verb+resource combination: computing overlapping technical indicators (SMA, EMA, Bollinger, Keltner) on price data. The category name 'overlap' is TA jargon, but the parenthetical examples make the intent concrete. It does not distinguish itself from category siblings like momentum or volatility, which also compute indicators.

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?

Gives some framing ('applied to price data automatically — no need to fetch prices first') and usage syntax for tickers, which implies how to call it. But it never says when to pick this over momentum/volatility/performance or other indicator categories, and it falsely asserts tickers is required when the schema lists only 'indicator' as required.

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 readOnly, idempotent, open-world, so the safety profile is covered. The description adds meaningful behavior beyond that: the unsupported 'daily' period, cumulative compounding rebased to 1, rolling=N spanning full history, and multi-ticker support. It does not describe rate limits or failure modes, but for a read-only metric tool this is solid added 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 core prose is dense and front-loaded, but the trailing 'Available indicators:' list duplicates the indicator enum already present in the schema, consuming tokens for no new information. The middle paragraph is efficiently packed.

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 26 parameters and an output schema present, the description need not explain return shapes, and it correctly focuses on the required indicator plus cross-cutting flags. It leaves some generic params (alpha, order, n_trials, trials_window, adjustment) unexplained, but these are largely niche, so the definition is reasonably complete for a dispatch-style metric 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 coverage is 100%, so baseline is 3, but the description adds real semantics the schema does not: cumulative compounding/rebasing behavior, the rolling-window meaning (e.g. monthly + rolling=6), and the tickers multi-value convention. It is above baseline because it clarifies cross-parameter interaction, not just restating individual fields.

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

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

The description states a specific verb class (computes) and resource (pre-computed risk-adjusted performance metrics) and enumerates the covered measures: Sharpe, Sortino, Alpha, Beta, CAPM, Treynor, Tracking Error, Information Ratio, Fama-French, etc. That is enough for an agent to distinguish it from siblings like volatility or momentum, though it never explicitly names a competing 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?

It gives parameter-level usage rules (tickers='AAPL' required, comma-separated for multiple, period='daily' not supported, cumulative=true rebases to 1), which is real guidance. However it offers no when-to-use/when-not guidance relative to sibling tools such as volatility or risk, so routing is left to inference.

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

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, so the safety profile is covered. The description adds useful context beyond them: values are pre-computed rather than raw statements, and quarterly/date-range scoping is supported. Auth, rate limits, and response shape are not addressed, but the output schema covers the latter.

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 purpose and usage guidance are front-loaded and tight, but the 23-item indicator list is duplicated verbatim from the schema enum, adding substantial bulk without new 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?

With an output schema present and 100% schema coverage, the description need not explain return values or secondary params (lag, growth, trailing, standardize). It covers the essential calling pattern well; only explicit sibling routing is missing.

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 restates tickers formatting and the quarterly/date-range options that the schema already documents, adding little new semantic detail; it also says tickers is "required" while the schema marks only indicator as required.

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?

Names a specific resource (pre-computed profitability ratios) and enumerates the metrics it covers (gross/operating/net margin, ROE, ROA, ROIC, ROCE), which implicitly separates it from solvency, valuation, and efficiency siblings. It does not explicitly name a sibling it is not, so it stops short of the top tier.

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?

Gives a clear when-to-use directive ("Use instead of raw financial statements") plus the required tickers form ("Requires tickers='AAPL'") and supported modifiers (quarterly, start_date/end_date). No explicit when-not-to-use or sibling routing (e.g. vs. efficiency or valuation) is 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 declare readOnlyHint, idempotentHint, and openWorldHint. The description goes beyond them by disclosing that a FRED API key is optional but needed for three indicators, that those three return no data without it, and that FRED-backed indicators always return a 'United States' column regardless of countries=. That is genuine behavioral context, though response format and rate-limit behavior are not covered.

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 front-loaded scope and usage rules are efficient, but the trailing enumeration of all 15 indicators duplicates the schema enum (and is even truncated mid-word), adding length without new information. Useful caveats are buried after the redundant indicator list.

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 an 18-parameter tool with an output schema and full annotation coverage, the description supplies the essential operating caveats (country requirement, no tickers, API-key dependency, FRED column behavior). Remaining gaps are minor given the schema carries parameter detail.

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, and the description adds real meaning by scoping rolling=N and trailing=N to only the policy-rate, short/long-term rate, and yield-curve-slope indicators, plus quarterly=true and date-range support. It does not explain the odd maturity/nominal/short_term/gmdb_source flags, leaving some ambiguity.

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?

Names a specific resource (interest rate data) and enumerates the covered indicator families (central bank policy rates, bond yields, ICE BofA series, EURIBOR, Treasury curve, yield curve slope), so an agent knows exactly what domain this tool serves. It does not explicitly differentiate itself from siblings like fixed_income or macroeconomics, which is the main gap.

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?

Gives several concrete usage constraints: countries='United States' is required for some indicators, comma-separated values for multiple countries, an explicit 'Do NOT use tickers= for this tool', and supported date/quarterly arguments. Strong context, but no explicit pointer to which sibling tool to use instead when tickers= is wanted.

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

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

Annotations already declare readOnlyHint, idempotentHint and openWorldHint, so the safety profile is covered. The description adds real behavioral context: which indicators support rolling=N and that rolling spans the full history instead of one value per period. It does not disclose failure modes, data latency, or cost of heavy computations (n_simulations=10000, n_bootstrap=1000), so it is adequate rather than rich.

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 first paragraph is dense but informative; the trailing 'Available indicators:' list of 42 names is a verbatim duplication of the indicator enum with no added meaning, adding bulk without value. The whole block is an undifferentiated wall of text with no headings, so scanning for the relevant parameter or metric is slow.

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 47-parameter mega-dispatcher with an output schema and safety annotations, the description covers the essentials: what it returns, the required tickers/indicator inputs, and rolling/period semantics. It omits parameter-to-indicator mapping details (which of the 47 params apply to a given indicator) and sibling routing, which would matter most for a tool this broad.

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, but the description contributes beyond the schema by enumerating exactly which metrics accept rolling=N, restating the tickers requirement, and clarifying the period/rolling output granularity. The many 'Value for k.' placeholders in the schema mean the description carries some of the semantic load, though not for most of the 47 parameters.

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

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

The description names the resource precisely — 'Pre-computed risk metrics' — and enumerates the specific metric families (VaR variants, CVaR, EVaR, GARCH volatility, drawdowns, CDaR, etc.) plus the full set of 42 indicator identifiers, so an agent knows exactly what this dispatcher computes. It does not, however, differentiate itself from siblings like 'volatility', 'models' or 'econometrics', which plausibly overlap with indicators such as get_volatility and get_garch listed here.

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?

Operational guidance is present for parameters: 'Requires tickers=AAPL', comma-separated multi-ticker support, period='daily' for whole-series calculations, and which outputs accept weekly/monthly/quarterly/yearly. But there is no when-to-use/when-not guidance or named alternative tool for overlapping indicators, leaving the risk-vs-volatility-vs-models choice to inference.

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

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

Annotations already declare readOnlyHint and idempotentHint, indicating safe read operation. The description adds that it returns a Markdown table or error message, providing context beyond 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?

The description is reasonably concise and front-loaded with purpose. However, the Args/Returns section duplicates schema information, making it slightly less efficient than necessary.

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 (1 required parameter, output schema present, annotations provided), the description covers all essential aspects: purpose, parameter, and return format. 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 is 100% with a clear parameter description. The description text repeats the same information (category examples) without adding new meaning 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 action ('List every available metric/tool') and the resource ('within a category'), with specific examples of categories. It distinguishes itself from sibling tools which are individual category names, as this tool aggregates items within a given category.

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 (to discover available metrics/tools in a category) but does not explicitly state when to use this tool versus alternatives like 'search_metrics' or individual category tools. There is no '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.