Stratalize Finance

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description does not need to repeat that. The description adds that the tool provides benchmark data, but does not disclose behavioral traits beyond what annotations cover (e.g., data freshness, pagination, or authentication needs).

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

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

The description is exceptionally concise: two sentences that front-load the core topic and target audience. Every sentence adds value, with no redundant or extraneous text.

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 the complexity of AML regulatory data, the description omits details on output format, units, data sources, or limitations. Without an output schema or parameter guidance, the description leaves the agent without enough information to correctly invoke the tool and interpret results.

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

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

Schema description coverage is 0%, so the description must add meaning for the two parameters (focus, institution_type). However, the description does not mention either parameter or explain how the listed data types map to parameter values. The enum names provide some self-evident guidance, but the description fails to compensate for the lack of schema 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 identifies the tool as providing AML regulatory benchmarks, listing specific data types (FinCEN SAR, OFAC, BSA enforcement, etc.). It distinguishes from sibling benchmark tools by specifying the AML focus, though it does not explicitly contrast with other benchmark 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 only states the tool is 'for compliance agents and financial institution risk officers,' which is a target audience, not usage guidance. It does not indicate when to use this tool versus alternatives, nor does it provide context for selection among the many sibling benchmark 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, so the description is not required to reiterate safety. It adds value by describing the data source (Audit Analytics public aggregate data) and the content (fees by revenue band and auditor tier), which helps the agent understand what the tool does 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 (three sentences), well-structured, and front-loaded with usage scenarios. Every sentence adds value: usage, data content, source, and target users. 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?

Given no output schema, the description explains the return content (total fees and fees as a percentage of revenue by revenue band and auditor tier) and source. It is fairly complete for a read-only benchmarking tool, though it could be slightly more explicit about how inputs affect the output.

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

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

Schema description coverage is low (33%: only annual_revenue_usd has a description in the schema). The description does not mention any of the three parameters (industry, auditor_tier, annual_revenue_usd) or explain how they map to the data content. With such low coverage, the description should compensate but fails to add parameter-level meaning.

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: benchmarking audit costs, evaluating auditor proposals, and preparing audit committee RFPs. It specifies the resource (audit fee benchmarks) and provides concrete usage scenarios. The purpose is distinct from sibling tools, which cover other financial/macro 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 explicitly says 'Use when benchmarking audit costs, evaluating auditor proposals, or preparing an audit committee RFP,' providing clear usage context. It also identifies target users (CFOs and audit committees). While it doesn't explicitly state when not to use or mention siblings, the context is sufficient for most agents.

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 value by specifying the data source (FDIC) and providing an example output, but does not mention rate limits or data freshness.

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

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

The description is concise with two sentences plus an example. It is front-loaded with the use case and efficiently conveys all necessary 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 simple tool with one parameter and full annotation coverage, the description is complete. It covers purpose, data returned, and provides an example, leaving no ambiguity for the agent.

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

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

With 100% schema description coverage, the description does not add much beyond the schema for the single parameter. The example in the description provides a concrete instance but no additional constraints.

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 with explicit use cases (acquisition, partnership, etc.) and lists the data returned (assets, deposits, capital ratios, loan quality, peer benchmarks). This differentiates it from sibling tools like get_ncua_credit_union_financials.

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 starts with 'Use when evaluating a bank...' specifying the context. While it does not explicitly exclude alternatives, the sibling list provides context. The example further clarifies usage.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the agent knows it's a safe read. The description adds value by specifying the data source (FDIC call report) and metrics, but doesn't disclose additional behaviors like update frequency or rate limits.

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

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

The description is concise (3 sentences) with front-loaded content: first sentence states purpose and metrics, second sentence cites source, third sentence identifies audience. 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?

Given no output schema and two parameters (one optional), the description should clarify return format and parameter details. It mentions metrics but not how data is returned, nor does it explain bank_type or asset size tier values. The provided information is insufficient for complete 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 description coverage is 0%, and the description only vaguely references 'by asset size tier' without explaining the specific enum values or the optional bank_type parameter. The description does not adequately compensate for the lack of schema documentation.

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

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

The description clearly states the tool provides bank regulatory capital and financial performance benchmarks with specific metrics (CET1, NIM, etc.) by asset size tier. It distinguishes from siblings by focusing solely on bank regulatory data, not other benchmarks like AML or audit.

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 identifies target users (bank CFOs, risk officers, analysts) and data source, implying context, but does not explicitly state when to use this tool versus alternatives or provide exclusion criteria like direct competitors.

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 value by citing the BLS as the data source and illustrating the output format with a real-world example (Medical care CPI +3.8% vs headline +3.1%). 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 a single, well-structured paragraph that front-loads usage and provides a concrete example. Every sentence adds value, with no wasted words. It is appropriately sized for the tool's simplicity.

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

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

Given a single optional parameter with enums, no output schema, and annotations present, the description sufficiently covers the tool's purpose and data source. It lacks explicit return format details, but the example implies the structure. Completeness is high for a straightforward tool.

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

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

The input schema has one parameter (category) with enum values, but schema description coverage is 0%. The description provides context about categories (medical care, housing) but does not enumerate or explain all options. Baseline 3 is appropriate as the enum is somewhat self-explanatory, though the description could compensate more for the coverage gap.

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 BLS inflation components for analyzing inflation exposure by spending category, with specific examples like medical care and housing CPI. It distinguishes from siblings such as get_inflation_benchmark and get_bls_sector_employment by emphasizing component-level detail and divergence from headline inflation.

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 when-to-use scenarios (e.g., vendor contract escalation clauses, healthcare budget planning) and gives a concrete example. It does not explicitly state when not to use, but the context is clear enough for an agent to infer appropriate usage.

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

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

Annotations indicate readOnlyHint=true and destructiveHint=false, so the tool is safe. The description adds valuable behavioral context: it mentions the data source (BLS Current Employment Statistics, 650,000 US worksites), its credibility (used by the Federal Reserve), and the nature of the output (month-over-month, year-over-year, trend). This fully discloses the tool's behavior.

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

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

The description is a single paragraph that front-loads use cases, then explains output, then provides an example. It is slightly wordy but each sentence adds value. A more concise structure could improve readability, but it is 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 has one parameter, no output schema, and good annotations, the description provides comprehensive context: it explains what the output contains (MoM, YoY, trend), gives a specific example, and notes the authoritative source. An agent can fully understand the tool's inputs and expected output.

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

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

The input schema has one parameter 'sector' with enum values, and the description adds an example output for healthcare, showing the format and metrics. While the schema already clearly defines allowed values, the description enhances understanding with a concrete example and hints at the output structure.

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 BLS payroll employment by major sector with specific metrics (MoM change, YoY change, trend classification). It distinguishes from siblings by focusing on sector employment data, while many sibling tools cover benchmarks, financials, or other domains. The use cases are explicitly listed, making the purpose unambiguous.

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

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

The description provides explicit scenarios for when to use the tool, such as benchmarking workforce planning, assessing industry growth, and providing economic context. However, it does not mention when not to use it or suggest alternative tools among the many siblings, which is a minor gap.

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 discloses that it returns data from a synced CFPB database, implying it is a read-only operation, which aligns with annotations (readOnlyHint=true). It adds context beyond annotations by detailing the nature of the data (volume, issue themes, response rates). 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 three sentences with no wasted words. It efficiently conveys purpose, output, and example. Front-loaded with use case, then output, then concrete illustration.

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 explains what is returned (volume, issue themes, response rate trends). The annotations confirm read-only. Parameters are contextualized. The tool is simple, and the description provides sufficient completeness 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?

The description explains that it returns rollups 'by company and product', covering both parameters. Although the schema has no descriptions (0% coverage), the description compensates by indicating the role of company_name (required) and product (optional filter). It adds meaning beyond the bare 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 when to use the tool (assessing consumer finance risk, benchmarking complaints, due diligence) and what it returns (complaint rollups by company and product). It distinguishes from sibling tools by specifying the CFPB complaint domain, which is unique among the listed 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?

It provides explicit use cases (risk assessment, benchmarking, due diligence) and an example. However, it does not mention when not to use it or alternatives among siblings, though the specificity makes the intended context 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?

The description adds value beyond annotations by disclosing data sources (FEMA NFIP, NGFS scenarios) and the categories covered. Annotations already indicate read-only and non-destructive; no contradictions. It provides 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 is two sentences, concise and front-loaded with the main purpose. Every sentence provides relevant information 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?

Given the complexity of climate risk and lack of output schema, the description provides a good overview but lacks detail on parameter selection and expected output. It 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 description coverage is 0%, and the description does not explain any parameters. With three enum parameters, the description should clarify their meanings or usage to compensate, but it fails to do so.

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 climate financial risk benchmarks, listing physical and transition risk categories and sources. It distinguishes from sibling tools by focusing specifically on climate risk, unlike other get_*_benchmark 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 mentions 'For ESG and risk agents,' implying target users. However, it does not explicitly state when not to use this tool versus siblings like get_esg_benchmark, though the focus on climate risk provides implicit differentiation.

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 important behavioral context: it returns HTTP 503 on upstream failure (with no charge), includes pricing ($0.10/call), and mentions the data_source field discloses provenance. This 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 front-loads the purpose but contains redundancy: it mentions the HTTP 503 condition twice. It also includes details like SLA pricing and data_source field that could be separated. It is moderately concise but could be tightened.

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

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

For a simple tool with one optional parameter and no output schema, the description covers main data, source, update frequency, audience, and error handling. However, it omits the parameter behavior and return format specifics, leaving gaps for an agent to fully understand usage.

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 0%, meaning the description provides no explanation for the only parameter 'category' (an enum of energy/metals/agriculture/all). The description lists specific commodities but does not connect them to the parameter, leaving the agent without guidance on how to filter by category.

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

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

The description clearly states it provides live commodity price benchmarks, listing specific commodities (WTI crude, natural gas, gold, copper, wheat, soybeans) and mentions weekly/monthly changes and inflation signal. It distinguishes from sibling tools which cover different financial domains (e.g., audit fees, climate risk).

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 traders and macro analysts and notes HTTP 503 behavior, but it lacks explicit guidance on when to use this tool versus alternatives like get_bls_inflation_components or get_eia_energy_public_snapshot. No exclusions 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 provide read-only/non-destructive status. Description adds specific error behavior (503 only if >50% fields unavailable, no charge), pricing ($0.10 per call), and data provenance field. Adds useful context 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?

All sentences contribute value, but the description mixes data sources, error handling, pricing, and SLA in a somewhat scattered manner. Could be more concise and front-loaded without losing 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?

Covers error handling, pricing, and data provenance well, but with no output schema and no parameter explanation, the agent lacks information on return structure and filtering options. Moderately complete for a tool with one optional parameter.

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 0% and description does not mention the 'focus' parameter or its enum values, leaving the agent without guidance on how to filter results. No additional semantics over the bare 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 live consumer sentiment benchmarks from FRED, listing specific components (University of Michigan sentiment, Conference Board confidence, retail sales, PCE, saving rate), which distinguishes it from sibling financial benchmarks.

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?

Implies usage for GDP and equity agents via consumer signal strength, but does not explicitly state when to use over alternatives or provide exclusion criteria. No direct comparison to 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 only declare readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context: data source (S&P Capital IQ and Damodaran), typical users (CFOs, treasurers), and practical applications (refinancing, covenant setting, credit rating management). 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 consists of two sentences: first provides usage context, second describes the output and source. It is efficient, front-loaded, and every sentence adds value. No 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?

Given the tool has only two parameters (one required) and no output schema, the description adequately covers what the tool returns (debt benchmarks). It does not detail the output format but the context (used by CFOs/treasurers) implies standard financial tables. Almost complete for its simplicity.

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

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

Schema description coverage is 0% so the description must compensate. It mentions 'by credit rating tier and industry' indicating the parameters' roles, but does not detail enum values or parameter format. The parameter names are self-explanatory, but more explicit guidance would be beneficial.

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 is for assessing debt capacity, benchmarking leverage, and preparing refinancing or credit rating discussions. It lists specific metrics (Net Debt/EBITDA, interest coverage, debt maturity profiles) and distinguishes itself from other benchmark tools in the sibling list by focusing on corporate debt.

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 begins with 'Use when...' providing explicit usage scenarios. It lacks explicit when-not-to-use or alternative tool references, but the context of sibling tools implies other benchmarks exist for different purposes (e.g., credit spreads, public market multiples). The given usage context 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 show readOnlyHint=true and destructiveHint=false. Description adds critical details: HTTP 503 on upstream unavailability, data_source field provenance, SLA, and cost, going well 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?

Front-loaded with main purpose, well-structured with necessary details (error handling, cost, data source). Slightly verbose with repeated HTTP 503 note, but overall efficient.

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

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

For a one-parameter tool with no output schema, the description covers input, error scenarios, audience, and provenance. Sufficient for confident agent selection, though missing output structure details.

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 0% (no parameter descriptions). Description mentions 'OAS by rating tier' but does not explicitly define the enum values (all, ig, hy, bbb) or their mapping to outputs, partially compensating.

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

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

The description clearly states it provides 'Live investment grade and high yield credit spread benchmarks from FRED ICE BofA indices' with specific components (OAS, TED spread, etc.), distinguishing it from sibling benchmark 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?

Target users ('credit analysts and fixed income PMs') and 'Live source' are mentioned, but the description does not explicitly compare to sibling benchmarks or specify when to avoid 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 declare readOnlyHint=true, so safety is clear. The description adds the data source (NCUA quarterly call report public data) and audience, but provides no additional behavioral details like rate limits, pagination, or update frequency. This is adequate but not extensive.

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 adding distinct value: first describes the tool's output, second provides source and target audience. No redundant words or trivial 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 two parameters and no output schema, the description is somewhat complete for a benchmark tool but lacks detail on expected output format (e.g., single number vs. table) and parameter value implications. This leaves gaps for automated usage.

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

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

Schema description coverage is 0% and the description does not explain either parameter ('charter_type' or 'asset_size_tier') despite mentioning 'by asset size'. It fails to compensate for the lack of schema documentation, leaving the agent guessing about parameter meanings.

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 ('provide') and clearly identifies the resource ('credit union financial performance benchmarks') with concrete metrics listed. It also distinguishes from the sibling 'get_ncua_credit_union_financials' by focusing on benchmarks rather than 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?

Includes explicit usage context ('for credit union CFOs preparing for NCUA exams and board reporting'), indicating when to use. However, does not mention when not to use or explicitly differentiate from similar siblings like 'get_ncua_credit_union_financials'.

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 adds value by disclosing data sources (SEC EDGAR, Sloan model) and the analytical scope. It does not contradict annotations. It could add more about limitations (e.g., data recency), but the current information is adequate.

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

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

The description is two sentences, front-loaded with the core purpose and source, followed by target audience. Every sentence provides essential information without redundancy. Structure is optimal for quick understanding.

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

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

With no output schema, the description gives a good sense of the output by listing the metrics (accruals ratio, cash conversion, revenue recognition risk) and data source. It is sufficient for an agent to understand what the tool returns. Minor gap: does not specify that output is likely sector-level aggregates.

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

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

Schema description coverage is 0%, so the description must compensate. It mentions 'revenue recognition risk' which relates to the second parameter, but does not explicitly explain each parameter or add format details. The enum values are descriptive, making parameters somewhat self-explanatory, but additional context would improve usability.

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 'Earnings quality and financial statement risk benchmarks' with specific metrics (accruals ratio, cash conversion, revenue recognition risk) by sector. The source (SEC EDGAR + Sloan model) is mentioned. This uniquely identifies the tool compared to siblings like get_esg_benchmark or get_wacc_benchmark.

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 specifies target users (CFOs, auditors, analysts) and context ('assessing financial reporting risk before M&A or investment'), giving clear guidance on when to use. It does not explicitly exclude use cases or mention alternatives, but the context is sufficient for an AI agent to infer appropriate usage.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. Description adds that results depend on EIA API configuration and provides an example, but doesn't discuss rate limits or update frequency.

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 concise and front-loaded with usage context. Every sentence adds value, though the example could be slightly trimmed.

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

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

For a zero-parameter read-only tool with annotations, the description covers usage scenario, source, and example output. No output schema exists, but the example compensates. It is adequately 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?

No parameters exist, so baseline is 4. Description adds meaning by explaining the output and providing an example, which is helpful beyond the empty 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 WTI crude and natural gas spot prices, which is a specific verb+resource. It distinguishes from sibling tools by focusing on energy prices from EIA.

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 says 'Use when current energy price data is needed for a commodity brief, input cost analysis, or energy sector context', providing clear context. However, no explicit exclusion of alternatives 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 indicate readOnlyHint=true and destructiveHint=false, confirming safe read-only behavior. The description adds value by specifying the exact output fields (wave position, degree, target high/low, invalidation, confidence) and the list of assets covered, which is consistent 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 two sentences, front-loaded with usage context and output details. It is concise but could benefit from a more structured format (e.g., bullet points). 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 no output schema, the description lists key return fields and example outputs (wave label, target band, invalidation, confidence). It covers the main aspects for a moderately complex tool, though additional detail on output structure would improve completeness.

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

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

Schema coverage is 100% with one parameter 'asset' described as 'Asset symbol or "all" (default all)'. The description does not add further meaning beyond the schema, so baseline 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 that the tool provides Elliott Wave analysis (wave counts, targets, invalidation levels) for major assets (BTC, SPY, TLT, Gold). It uses specific verbs like 'returns' and lists output fields, distinguishing it from sibling tools that focus on benchmarks or regulatory 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 explicitly says 'Use when a technical trader needs...', providing clear context for when to use the tool. However, it does not mention when not to use it or suggest alternatives, though the sibling tools are clearly different in nature.

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 value by disclosing specific data points, sources (EPA GHGRP, MSCI ESG methodology), and target audience, going 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 two sentences: first sentence lists content, second sentence provides sources and audience. Every sentence is meaningful and 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?

Covers purpose, data points, and sources adequately for a simple two-parameter tool. Minor gap: no mention of what is returned if no parameters are provided (since none are 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 coverage is 0%, but the description implies parameter meaning by mentioning 'sector' and listing metrics that align with enum values for focus (carbon, social, governance, all). This adds sufficient context for understanding parameter usage.

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

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

The description explicitly states 'ESG benchmarks by sector' and lists specific metrics (carbon intensity, net zero commitments, etc.) and sources, clearly distinguishing it from sibling benchmarks like get_climate_risk_benchmark.

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?

Target users are identified ('For sustainability agents and ESG analysts'), but there is no explicit guidance on when to use this tool versus alternatives or 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 declare readOnlyHint=true and destructiveHint=false, so the agent knows it is a safe read operation. The description adds context that the tool is based on current FRED data and is illustrative/scenario planning, which goes beyond the annotations. However, it does not mention data freshness or specific limitations, so a 4 is given.

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 five sentences that each add value. It front-loads the usage context and includes an example. There is no waste, and 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?

Given the tool has no parameters and no output schema, the description adequately explains what it returns (probabilities for three meetings), provides an example, and cites the source (FRED St. Louis Fed). It is complete for the tool's simplicity.

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 no parameters, and schema coverage is trivially 100%. The description does not need to explain parameters, and it adds no additional parameter information. Per guidelines, when schema coverage is high and no parameters exist, a 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 it returns illustrative cut, hike, and hold probabilities for the next three FOMC meetings. It identifies the resource (FOMC meeting probabilities) and the specific outputs (cut, hike, hold probabilities). While it distinguishes from sibling tools by its unique purpose, it does not explicitly contrast with siblings, so a 4 is appropriate.

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 when providing monetary policy narrative context for a macro brief, investment committee, or CFO rate planning session.' It also clarifies when not to use it by stating it is a 'scenario planning tool — not futures-implied market odds.' This 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?

Beyond the annotations (readOnlyHint=true, destructiveHint=false), the description discloses specific error behavior (HTTP 503 if upstream unavailable for >50% of fields), pricing ($0.10 USDC per call), and the presence of a data_source field. This adds valuable transparency.

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 core functionality (currency pairs, metrics, source), followed by error and pricing details. It is informative but slightly dense; minor restructuring could improve readability.

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 many aspects (available data, source, error handling, pricing), it fails to explain the optional base_currency parameter and does not describe the response structure beyond the data_source field. Given the lack of output schema, this is a notable gap.

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

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

The description does not explain the base_currency parameter at all, despite the schema having an enum. It provides no guidance on how the parameter modifies the output, leaving a critical gap. Schema coverage is 0%.

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 live major currency pair benchmarks and lists specific pairs and metrics (e.g., USD/EUR, DXY TWI, carry trade spread). This distinguishes it from siblings. However, the optional base_currency parameter's effect is not explained, creating slight ambiguity.

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 implies it is for FX benchmarks, but does not differentiate from similar tools like get_credit_spread_benchmark, leaving the agent to infer usage 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 indicate readOnlyHint=true and destructiveHint=false. The description adds behavioral context: HTTP 503 on upstream unavailability, data_source field disclosing provenance, and pricing (x402 SLA: $0.10 per call). This 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 moderately sized and front-loaded with purpose. It contains useful details about indices, data points, error handling, and pricing. Minor redundancy (two mentions of 503) but overall efficient.

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

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

For a simple tool with one optional parameter and no output schema, the description covers purpose, data types, indices, errors, and pricing. It lacks explicit region-to-index mapping but is sufficient 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 description coverage is 0%. The description lists indices but does not explicitly map the 'region' enum values to those indices. The enum is self-explanatory, but the description should compensate for low coverage by explaining parameter use, which it does not.

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

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

The description clearly identifies the tool as providing global equity index benchmarks, listing specific indices (S&P 500, Nasdaq, etc.) and data points (YTD returns, P/E ratios). It distinguishes from siblings by focusing on equity benchmarks versus other asset classes or topics.

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 specifies when to use the tool (global equity benchmarks) and includes reliability and pricing details (HTTP 503 behavior, SLA). However, it does not explicitly exclude alternatives among the many sibling tools or provide 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. The description adds valuable behavioral context: 'IMF WEO static composite, semi-annual update' and that it returns composites by country group. 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?

Three sentences efficiently cover usage, example output, and source/update frequency. Front-loaded with key information, 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?

Given no parameters or output schema, the description explains what the tool returns (GDP growth, inflation, current account balance by country group) and provides an example. Could mention exact output structure or which country groups are available, but still reasonably 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?

Input schema has 0 parameters with 100% schema description coverage (vacuous). No parameters need explanation. The description does not need to add parameter info; baseline 4 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 'IMF WEO macro composites — GDP growth, inflation, and current account balance by country group', specifying the verb, resource, and metrics. It distinguishes from sibling tools by focusing on country group composites rather than individual country 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?

Explicitly states when to use: 'global macro context for an international expansion brief, country risk assessment, or board-level economic outlook presentation'. While it does not explicitly mention alternatives, the context implies it is for high-level composites, distinguishing it from sibling tools like get_world_bank_country_indicators.

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 readOnly and non-destructive. The description adds valuable behavioral details: HTTP 503 return when upstream is unavailable (no charge), SLA and cost, and data_source field provenance. This goes well 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 dense with multiple sentences, each providing value (data types, error handling, cost, provenance). It is front-loaded with the core purpose. However, it could be slightly more concise by separating SLA/cost details.

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

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

Given the simple schema (one optional enum) and no output schema, the description covers the main aspects: data types returned, error behavior, cost, and provenance. It does not specify default measure or response structure, but that is acceptable given the 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 coverage is 0% with no description of the 'measure' parameter. The description lists the data types available (cpi, pce, etc.) but does not explicitly map them to enum values or explain the default behavior. Some compensation but still incomplete.

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 live inflation benchmarks from FRED, listing specific metrics like CPI, core CPI, PCE, core PCE, breakeven expectations, and components. This distinguishes it from sibling benchmarks like get_bls_inflation_components or get_yield_curve_benchmark.

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 macro agents needing inflation data (e.g., Fed target gap, policy implication), but it does not explicitly state when to use this tool versus alternatives or when not to use it. No direct comparison to 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 and destructiveHint=false, so the tool's safety is known. The description adds useful context (data source, included ratios) but does not disclose further behavioral traits like rate limits or authentication needs. This is adequate given 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?

Two succinct sentences with front-loaded purpose. The first sentence states the tool's output and source, the second identifies the target audience. 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?

The description lists the ratios returned and the source, providing adequate context for a simple benchmark tool. However, without an output schema, it omits details on return format or historical scope. The lack of parameter explanation also reduces completeness.

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

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

Schema description coverage is 0%, so the description must compensate. It mentions 'by line of business' which hints at the required parameter, but does not explain the optional company_size parameter or the enum values. The agent must rely solely on the schema, which is a significant gap.

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

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

The description clearly states it provides insurance financial performance benchmarks (combined ratio, loss ratio, etc.) by line of business, sourced from NAIC. This is precise and distinguishes it from sibling benchmark tools like get_aml_regulatory_benchmark.

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 targets insurance CFOs, actuaries, and analysts reviewing underwriting performance, giving clear context for use. However, it does not explicitly state when not to use or name alternative tools, so it falls short of a 5.

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 important behavioral details: it returns HTTP 503 if upstream source is unavailable for >50% of fields, includes SLA pricing, and mentions a data_source field for provenance. These are useful 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 mixes functional description with pricing and SLA details. It is somewhat cluttered and could be more structured. However, it is not overly long.

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. The description mentions a 'tight/balanced/loosening signal' and a data_source field but does not fully describe the response format. Given the complexity of labor market data, more detail on output structure would be helpful 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?

The input schema has one optional parameter 'focus' with enum values. The description lists the indicators covered but does not explicitly map them to the enum values. With 0% schema description coverage, the description could have been more explicit about how the focus parameter filters results.

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 live labor market benchmarks from FRED, listing specific indicators like unemployment, U-6, JOLTS, etc. It explains the output includes a tight/balanced/loosening signal. The name and description distinguish it from sibling tools which focus on other benchmarks (e.g., AML, audit fees).

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 says it is for 'macro agents and portfolio managers' implying high-level labor market analysis. However, it does not explicitly state when to use this tool versus alternative labor market tools like get_bls_sector_employment. No guidance on when not to use or 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. Description adds valuable behavioral context by listing returned items and intended use, 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?

Two sentences plus an example, concise and front-loaded with use case. 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 lists return items adequately. Could mention format, but overall sufficient for a parameterless 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?

No parameters in schema, schema coverage 100%. Baseline for 0 params is 4. Description adds no parameter info but none needed.

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

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

The description clearly states the tool returns current regime label and tactical positioning for traders/portfolio managers. It lists specific outputs (FOMC facts, market snapshot, etc.) and distinguishes from sibling tools focused on benchmarks.

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 says 'Use when a trader or portfolio manager needs current regime label and tactical positioning,' providing clear context. Does not explicitly exclude alternatives, but the context is sufficient.

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 tool is safe to use. The description adds value by disclosing the data source (Damodaran dataset, public deal aggregates) and the type of multiples provided, 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, consisting of two sentences that front-load the primary use case and quickly provide supporting details. Every sentence adds value 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?

Given the simple parameter structure (two enums) and lack of output schema, the description adequately informs the user of the output content (multiples by industry and deal size). It would benefit from specifying the output format, but overall it is complete enough for effective use.

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 description references both industry and deal size, matching the two parameters. However, it does not explicitly enumerate the allowed values or explain their meaning in detail. With 0% schema description coverage, the description partially compensates but could be more specific.

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: to retrieve M&A transaction multiples (EV/EBITDA, EV/Revenue, control premiums) for valuing acquisition targets, benchmarking deal pricing, or preparing fairness opinions. It distinguishes from sibling benchmarks by specifying its M&A focus and data sources.

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 states when to use the tool (acquisition valuation, benchmarking, fairness opinions) and identifies target users (corp dev, PE, M&A advisors, CFOs). It does not explicitly mention when not to use it or list alternative tools, but the context is clear enough for appropriate usage.

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

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

Annotations already indicate read-only behavior. The description adds context beyond annotations by noting the data source (NCUA examiners) and regulatory threshold (7% net worth ratio), but does not detail error cases or side effects.

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

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

The description is three sentences, conveying purpose, use case, and an example efficiently. It is front-loaded with the primary usage scenario, though the first sentence is slightly dense.

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

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

For a simple tool with two parameters and no output schema, the description covers the core purpose, use cases, a threshold value, and an illustrative example. It is sufficient for an agent to understand the tool's function.

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

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

The input schema has 0% description coverage for its two parameters. The description does not explain the 'state' parameter or provide details on 'credit_union_name' beyond its implication from the tool name, failing to compensate for the schema gaps.

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

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

The description clearly states the tool's verb ('returns') and resource ('NCUA call report financials' for credit unions), with specific metrics listed. It distinguishes itself from siblings like 'get_credit_union_benchmark' by referencing NCUA data and peer comparison, making the purpose unambiguous.

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

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

The description explicitly starts with 'Use when evaluating a credit union for partnership, acquisition, membership, or competitive benchmarking,' providing clear usage context. While it doesn't list alternatives or exclusions, the purpose is sufficiently scoped.

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 read-only and non-destructive. Description adds source (Cambridge Associates) and metrics (IRR, TVPI, DPI), and target users. 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?

Three sentences, front-loaded with usage guidance, then tool description, then context. No redundant information.

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

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

Provides sufficient context for a simple tool: use cases, metrics, source. No output schema, but description sets expectations. Minor gap: no mention of data range or limitations.

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 0%; description does not detail the parameters beyond mentioning strategy (buyout, growth equity, venture) and vintage year. Does not list enum values or explain vintage year format. High coverage needed, but description provides minimal parameter clarification.

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 private equity and venture return benchmarks (IRR, TVPI, DPI) by vintage year and strategy, distinguishing it from sibling benchmark tools like get_venture_benchmark or get_public_market_multiples.

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 use: benchmarking fund performance, setting LP return expectations, evaluating GP track record. Does not explicitly exclude scenarios or mention alternatives, but context from siblings makes usage 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 indicate readOnlyHint=true, and description adds dataset source and free nature, beyond what annotations provide; 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?

Three sentences, front-loaded with usage guidance, each sentence adds value; minor redundancy in use cases.

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 mentions specific metrics and bands; missing explanation of 'context' parameter; otherwise adequate given 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 coverage 0%, description explains sector usage implicitly but does not explain the 'context' parameter; partly compensates with usage context.

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 states verb 'get' and resource 'public market valuation multiples' with specific metrics and bands, distinguishing it from sibling benchmark 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?

Explicitly mentions when to use (comps table, benchmarking, fundraising) and lists use cases, but lacks explicit 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 declare readOnlyHint=true and destructiveHint=false. The description adds significant behavioral context: it states that every response is cryptographically signed (ML-DSA-65) and verifiable, and includes a verification link. This goes beyond the annotations and provides transparency about data integrity. 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 three sentences, each adding essential information without redundancy. It front-loads the core purpose and follows with verification details. No extraneous content; 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?

Given the tool's simplicity (one parameter, no output schema), the description is nearly complete. It explains what is returned (activist signal, verification receipt) and the source. However, it lacks information on the output format or example response, which would be helpful but not critical for a straightforward read-only tool.

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

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

The schema has one parameter (ticker) with 0% description coverage, and the tool description does not mention or explain the parameter. The description adds no meaning beyond the schema, which is insufficient for a required parameter. The agent has no guidance on what the ticker should be (e.g., format, 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?

The description clearly identifies the tool's purpose: retrieving Schedule 13D/13G beneficial ownership filings, distinguishing between activist and passive shareholders, and returning an activist signal. It also specifies the source (SEC EDGAR) and provides a unique verification feature. This strongly separates it from sibling tools like get_sec_insider_trading.

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. It mentions the purpose and source but lacks guidance on context, such as when to choose this over get_sec_insider_trading or other SEC-related tools. The usage is implied but not clarified.

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?

Beyond annotations indicating read-only safety, the description adds transparency about data source (SEC EDGAR), response signing, and cryptographic receipt, enhancing trust. However, it does not disclose potential rate limits or the exact nature of the 'insider activity signal'.

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 three sentences that each provide distinct information: purpose and content, output signal, and source/verifiability. It is well-structured and front-loaded.

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

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

For a single-parameter read-only tool, the description covers essential aspects: data type, time frame, source, and verification. However, it does not specify the format of the returned data (e.g., JSON structure) or any limitations such as rate limits, which would enhance completeness.

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

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

The description adds semantic context for the single parameter 'ticker' by specifying that the tool returns insider transactions for the given ticker, with a 90-day lookback. This meaningfully complements the schema, which only provides length constraints. However, it does not explicitly state that the ticker should match a publicly traded company symbol.

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 retrieves SEC Form 4 insider transaction history for executive buy/sell filings in the last 90 days, distinguishing it from sibling tools like get_sec_beneficial_ownership.

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 insider transaction data but does not provide explicit guidance on when to use this tool versus alternatives like get_sec_beneficial_ownership, nor does it mention 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 mark it as read-only and non-destructive. The description adds details about public access, x402 micropayments, and OAuth requirements, providing useful 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?

Two sentences: first states purpose upfront, second adds pricing and auth details. No wasted words, efficient for a discovery 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 zero parameters, no output schema, and annotations covering safety, the description fully covers the tool's role as a catalog entry point, including usage order, auth, and pricing.

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 exist, so baseline 4 applies. The description adds no parameter info, which is acceptable as there are none to describe.

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 'START HERE — Returns the complete Stratalize tool catalog', clearly indicating the tool's purpose as a discovery entry point. It distinguishes from siblings by specifying it is a catalog, not a specific data 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?

Explicitly says 'Call this first to discover tools by role or vertical', providing clear guidance on when to use. Also notes OAuth requirements for certain features, helping with usage 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 indicate a read-only, non-destructive operation. The description adds detail beyond annotations by specifying the exact data returned, including 'verifiable FOMC facts' and 'model interpretation' separated, plus data sources (FRED/EIA). 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 three concise sentences that front-load the use case, list the returned data, and provide an example and source. No 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?

Given no parameters and no output schema, the description is completely adequate. It explains what the tool does, when to use it, and what it returns in sufficient detail for an agent to invoke it correctly.

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

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

The input schema has zero parameters, so baseline is 4. Description does not need to add parameter info; it appropriately focuses on the output nature.

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 'full live signal stack' with a specific list of indicators (Fed funds, 2s10s, VIX, etc.), distinguishing it from sibling tools that focus on individual benchmarks or snapshots.

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 when a macro agent needs a full live signal stack in one call' and provides an example of usage. While it doesn't mention when not to use, the context from sibling tools makes the distinction 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 indicate readOnlyHint=true and destructiveHint=false, so the tool is safe and read-only. The description adds value by specifying the data source (Carta State of Private Markets quarterly), implying public report data and update frequency, which enriches 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?

Two sentences: first sentence states what the tool provides, second sentence covers source and use case. No fluff, well front-loaded, every sentence earns its place.

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

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

For a read-only benchmark tool with clear parameters and no output schema, the description covers purpose, metrics, source, and use case adequately. It lacks explicit parameter guidance but is otherwise complete. Could mention limitations or how to interpret benchmarks, but 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 0%, so the description must compensate. It mentions 'by stage and sector' but does not explain the enum values or provide additional meaning. The parameter names are clear, but the description adds minimal value beyond what the names imply.

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 the tool provides venture capital round benchmarks including specific metrics (pre-money valuation, round size, dilution, option pool standards) by stage and sector. It names the data source (Carta) and target users, distinguishing it from sibling tools like get_pe_return_benchmark or get_valuation_benchmark.

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 says 'Used by founders, VC CFOs, and early-stage investors for round pricing and cap table modeling,' giving a clear use case. It does not explicitly state when not to use it or compare to alternatives, but the specificity of venture capital stages implicitly differentiates it from other benchmark 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, establishing the tool is safe. The description adds that the data comes from the Damodaran annual dataset and is updated annually, which provides helpful behavioral context beyond annotations, but does not disclose further traits like response format or rate limits.

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

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

The description is concise: three sentences with front-loaded usage guidance. Every sentence is informative (source, update frequency, typical use) with no 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?

Given the tool's low complexity (two enum parameters, no nested objects) and lack of output schema, the description covers key aspects: purpose, use cases, data source, update schedule. It does not explain the return format, but that may be self-evident for a benchmark tool.

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

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

Schema description coverage is 0%, so the description must compensate. It mentions 'by sector and market cap tier', mapping to the two parameters. However, it does not elaborate on enum values (e.g., what qualifies as 'micro' vs 'mega') or add meaning beyond the schema. The parameters are self-explanatory but the description adds minimal value.

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

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

The description clearly states the tool's purpose: provide WACC benchmarks by sector and market cap tier from the Damodaran dataset. It specifies use cases (business valuation, hurdle rates, M&A analysis) and distinguishes from siblings which are other benchmarks (e.g., inflation, audit fees).

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 states when to use the tool ('Use when valuing a business, setting hurdle rates, or benchmarking discount rates...'). While it does not provide negative guidance or mention alternatives, the sibling list implicitly covers other benchmarks, making the usage context 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 indicate read-only and non-destructive behavior. The description adds value by specifying the exact metrics returned and the data sources, 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 two sentences, front-loaded with usage context, and contains no fluff. Every sentence contributes meaning.

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 covers purpose, metrics, sources, and usage context. Minor omissions like output format or limitations exist, but for a simple query tool it is largely sufficient.

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

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

The description mentions 'by industry and company size' which corresponds to the two parameters, but does not explain the enum values or provide detailed semantics. With 0% schema description coverage, the description partially compensates but leaves gaps.

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 the tool benchmarks working capital efficiency with specific metrics (DSO, DPO, DIO, CCC). It provides context for use (CFO cash management brief) and sources, distinguishing it from sibling benchmark 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 starts with 'Use when...' providing clear context for when to apply the tool. However, it does not explicitly mention when not to use it or alternatives, but the sibling list makes it implicit that this is the only working capital benchmark.

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) indicate safe read-only operation. The description adds value beyond annotations by detailing data source (World Bank), coverage (200+ countries, 1,400+ indicators, quarterly updates), and includes an illustrative example for Brazil, aligning with the read-only nature.

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 three well-structured sentences: first sets usage context, second describes functionality and data source, third provides a concrete example. It is front-loaded and every sentence earns its place 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?

Given no output schema, the description adequately explains return value (indicators with 5-year trend and direction) and provides data context. The example gives concrete expectation. Minor gap: precise output format is not specified, but the description is sufficient for tool selection and 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?

The input schema covers two parameters with 50% description coverage (country_code explained; indicator has enums but no description). The description lists many indicators and explains their context (e.g., '5-year trend and direction'), adding meaning beyond the schema. However, not all enum values are explicitly enumerated in 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's verb (Returns) and resource (World Bank development indicators) with specific indicators listed: GDP, inflation, unemployment, ease of doing business, government debt, FDI inflows. This distinguishes it from numerous sibling benchmark tools that focus on other domains.

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 lists use cases: assessing country risk, evaluating foreign markets, benchmarking economic trajectory, ESG scoring. It provides clear context for when to use, though it does not explicitly mention when not to use or direct to alternative 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 indicate readOnlyHint=true and destructiveHint=false, which the description complements with important behaviors: returns HTTP 503 when upstream source unavailable, SLA pricing, and data_source field disclosure. No contradiction with annotations. Adds value beyond annotations by explaining fallback behavior and pricing model.

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 a single paragraph but packs relevant information: content, source, fallback, pricing, and field disclosure. No wasted sentences, though structure could be improved (e.g., bullet points). Adequately concise for the amount of information conveyed.

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 lists many fields but omits the parameter semantics and overall response structure. The tool is relatively simple with one optional parameter, so the incompleteness is notable but not critical. The annotations cover safety, and the description covers source and fallback.

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

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

The schema lists a single optional 'tenor' parameter with enums, but the description does not explain what the parameter does or its allowed values. Schema description coverage is 0%, leaving the agent uninformed about how to filter the yield curve. The description should mention that 'tenor' allows selecting specific maturities.

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 it provides 'Live US Treasury yield curve' with specific details (yields, changes, spreads, etc.). Clearly distinguishes from sibling benchmark tools (e.g., get_aml_regulatory_benchmark) by specifying the domain and content.

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 Treasury yield data but does not explicitly state when to use this tool vs siblings. No exclusion criteria or alternative recommendations. The context of 'Live source' and SLA pricing gives some guidance but lacks direct comparison to other benchmark tools.

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