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 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. 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 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 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?

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 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 already declare readOnlyHint=true and destructiveHint=false. Description adds value by noting 'No auth required' and specifying what is returned (catalog, briefs, etc.), enhancing transparency 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?

Description is dense but well-structured, starting with 'START HERE' to front-load purpose. It efficiently packs essential details (counts, pricing, examples) without waste, though slightly verbose.

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 thoroughly explains what the tool returns: tool catalog breakdown by namespace, pricing tiers, access methods, and types of content (briefs, benchmarks, compliance tools). It is complete for an overview 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, so description need not add parameter info. Baseline for 0 params is 4. Description does not introduce confusion about parameters.

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

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

The description clearly states 'START HERE - Returns the complete Stratalize tool catalog' and details tool counts, namespaces, pricing, and access methods, distinguishing it from sibling tools that are specific data 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 says 'Call this first to discover...', giving clear context for when to use. Does not explicitly state when not to use or provide alternatives, but the instruction is strong enough for an entry point.

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.