Stratalize AI Governance Intelligence

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

Annotations already mark readOnlyHint=true. The description adds context about the tool's scope (public vs. org) and what it returns, which is beyond the annotations. No contradictions.

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

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

Two sentences, front-loaded with key output details, no extraneous wording. Every sentence adds value.

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

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

Despite no output schema, the description is self-contained for a tool with 0 parameters. It explains what is returned and the limitation of public mode.

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 parameters with 100% coverage, so the baseline is 4. The description adds no parameter info, which is appropriate since none exist.

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

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

The description clearly states the tool returns FS AI RMF Adoption Stage references (INITIAL, MINIMAL, EVOLVING, EMBEDDED) and distinguishes between public mode (framework stages only) and org-scoped classification. This specificity differentiates it from sibling tools.

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

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

The description explicitly says when to use the tool (public mode) and when not to (org-scoped), pointing to alternatives like 'Connect org MCP or the dashboard questionnaire'. This provides clear usage boundaries.

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

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

With no annotations, the description reveals that the tool uses narrative fallbacks and performs multi-platform belief synthesis. However, it does not detail behavior on errors, data freshness, or limitations, leaving some gaps.

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

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

The description is extremely concise: two sentences that front-load the purpose and output, followed by an example. 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 lack of output schema and annotations, the description provides a high-level overview of the output fields and use case. However, it does not explain parameter meanings or detailed output structure, which is needed for a tool with no supporting schema.

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

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

Schema coverage is 0%, yet the description adds no meaning for the parameters 'topic' and 'category'. It only implicitly mentions 'topic' without explaining 'category'. The description should compensate for the lack of schema descriptions but 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 that the tool returns a JSON with specific fields (consensus_score, sentiment_mix, key_themes, platform_breakdown) for researching business topics or vendors from AI citations. It distinguishes itself from sibling tools (e.g., benchmarks) by focusing on AI consensus synthesis.

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 it is for analysts researching a business topic or vendor and mentions multi-platform belief synthesis, implying it is for generalized AI consensus. It does not provide when-not-to-use or alternatives, but the context is clear.

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

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

No annotations provided, so the description is the only source of behavioral info. It only lists content, omitting details like whether the tool is read-only, requires authentication, has rate limits, or how data is sourced. No mention of mutability 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?

Two sentences, no unnecessary words. Front-loaded with the most important content (list of benchmarks) and ends with audience. Efficient and to the point.

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 listing key data categories, the description lacks details on return format, data structure, pagination, or completeness. No output schema exists, so the agent must guess the output shape. For a data-heavy tool, this is insufficient.

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 has two parameters with clear enums (focus, institution_type), but description coverage is 0%—it doesn't mention parameters at all. However, the schema itself is descriptive enough so that a 3 is appropriate as baseline. The description does not add value beyond the schema.

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

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

Description starts with 'AML regulatory benchmarks' and lists specific data types (SAR filing rates, OFAC SDN counts, enforcement fines, etc.), clearly stating the tool's purpose. It also identifies the target audience (compliance agents, risk officers). This distinguishes it from siblings like get_bank_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?

Implied usage through the listing of data types and audience, but no explicit guidance on when to use this tool vs alternatives or when to avoid it. No comparison with siblings or context on prerequisites.

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?

Without annotations, the description carries the full burden. It discloses that the data is static and from a specific source, implying read-only, but does not mention any behavioral traits such as error handling, auth needs, or what happens with invalid inputs.

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, front-loading the core output and data source. It wastes no words, though the example could be integrated more tightly.

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 provides a high-level structure (cost_per_case_median, revenue-mix percentages) but lacks details on the JSON format, possible multiple records, or handling of missing parameters. It is adequate but not fully comprehensive.

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 string parameters. The description adds context for specialty by giving an example but does not explain valid values for either state or specialty or how they affect the output, thus only 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 returns JSON cost_per_case_median plus revenue-mix percentages for ASC administrators by specialty, specifying the data source (ASCA plus CMS 2024 table) and giving an example. It effectively distinguishes itself from many sibling benchmark tools by focusing on ASC costs.

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

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

No guidance is provided on when to use this tool versus alternatives, nor any conditions or exclusions. Given the large number of sibling tools, the lack of usage comparison is a significant 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?

With no annotations, the description carries full burden. It clearly indicates a read-only benchmark retrieval by stating data source (Audit Analytics public aggregate data) and usage context. No contradictions.

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

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

Three sentences, each providing distinct value (what, source, use case). No redundancy or 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, the description adequately explains the returned data (total fees and percentage of revenue by band and tier) and source. Minor omission: no mention of output format or pagination.

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 low (33%), and the description mentions revenue band (mapping to annual_revenue_usd) and auditor tier, adding meaning. However, the 'industry' parameter is not explained 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 provides audit fee benchmarks, specifying the metrics (total fees and percentage of revenue) and breakdowns (by revenue band and auditor tier). It distinguishes from siblings by naming specific use cases and data source.

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 identifies target users (CFOs, audit committees) and use cases (auditor RFPs, fee negotiations), providing clear context. However, it lacks explicit 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?

No annotations are provided, so the description must disclose behavioral traits. While it mentions the FDIC source and that it returns data, it does not specify if the operation is read-only, any required permissions, or potential side effects. The read-only nature is implied by the 'get' prefix but not confirmed.

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 three concise sentences, each adding value: purpose, example, summary. It is front-loaded with the main action and resource, making it easy to parse quickly.

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 simplicity of the tool (one parameter, no output schema), the description covers the key elements: data returned (assets, deposits, capital ratios, loan quality), source (FDIC), and audience (bank credit analysts). It could be more specific about the output structure, but it is largely adequate.

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

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

The schema provides 100% coverage with a description for the single parameter (bank_name). The description adds value by contextualizing the parameter (querying FDIC-sourced intel) and giving examples (JPMorgan, Wells Fargo, etc.).

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 specific financial data (assets, deposits, capital ratios, loan quality) sourced from FDIC for bank credit analysts. It distinguishes itself from sibling tools by focusing on bank financial intelligence.

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 bank analysis and provides an example of a $200B assets institution, but does not explicitly state when to use this tool versus alternatives like get_bank_regulatory_benchmark or get_public_company_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?

With no annotations provided, the description must cover behavioral traits, but it only mentions the source (FDIC call report) and metrics. It lacks details on data freshness, update frequency, scope limitations (e.g., US only), return format, or whether averages/aggregates are returned. This leaves significant ambiguity for an AI agent.

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

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

The description is two sentences: the first lists metrics and grouping, the second gives source and audience. It is concise without unnecessary words and front-loads the key information.

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

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

Given the tool has two parameters, no output schema, and no annotations, the description is incomplete. It does not explain the bank_type parameter, the output format, or data time range. An AI agent would likely need additional clarification to use the tool correctly.

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

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

The schema has two parameters: asset_size_tier (required) and bank_type (optional). The description only explains asset_size_tier ('by asset size tier') and omits any mention of bank_type, its enums, or its effect. Since schema description coverage is 0%, the description should compensate but fails to fully explain the 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 specifies the tool provides bank regulatory capital and financial performance benchmarks (CET1, Tier 1 leverage, NIM, efficiency ratio, charge-off rates, loan-to-deposit ratio) sourced from FDIC call report aggregates. It targets bank CFOs, risk officers, and analysts, distinguishing it from siblings 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 implies usage for US bank regulatory and performance benchmarking by listing relevant metrics and source, but it does not explicitly state when to use this tool versus alternatives or provide any exclusions or prerequisites.

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

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

With no annotations, the description carries the transparency burden. It discloses that the model is a static composite model, not claim-level coding, which is valuable. However, it does not mention rate limits, auth requirements, or whether the operation is read-only (though 'get' implies it). This is adequate but not comprehensive.

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

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

The description is two sentences and front-loads the key outputs. It is concise and avoids verbosity, though it could be slightly more structured. Every sentence adds value related to purpose and nature of the tool.

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

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

Given the lack of an output schema and annotations, the description should provide more completeness about the return format and how parameters affect results. It covers the high-level topic but leaves gaps about expected output structure and error conditions, making it incomplete for reliable agent 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?

Schema coverage is low (33%): only 'level_4_5_percentage' has a description. The description does not clarify 'specialty' or 'annual_claim_volume' beyond the schema. Without further parameter explanation, the agent must infer meaning from the overall tool purpose, which is insufficient.

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: returning E/M mix benchmarks, upcoding risk heuristics, OIG themes, etc. It specifies the resource (billing coding risk) and distinguishes itself from claim-level coding. The verb 'Returns' and the list of outputs make the purpose specific and actionable.

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

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

The description implies the tool is for revenue integrity risk screening but does not explicitly state when to use it versus alternatives. Among many sibling 'get_benchmark' tools, this one's unique focus on coding risk helps differentiate, but no direct guidance is provided.

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

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

No annotations are provided, so the description carries the full burden. It discloses a key behavioral trait: momentum_4week may default to ~1.8 when history is sparse, and it uses a heuristic for trend direction. However, it does not cover authentication, rate limits, or whether the operation is read-only.

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 appropriately short (two sentences) and front-loads the key output. However, there is slight redundancy between 'Returns JSON momentum_4week...' and 'Brand trajectory monitoring...' The structure is adequate but not maximally efficient.

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

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

Given the tool has one required parameter, no output schema, and no annotations, the description provides sufficient context for a simple data retrieval tool: return shape, default behavior, and heuristic nature. It lacks details on error handling or sparse data conditions beyond the default, but overall is complete for basic 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?

With 0% schema description coverage, the description should compensate but fails to add meaning for the single parameter brand_name. It does not specify format, examples, or constraints beyond what is obvious from the name. The description focuses solely on the output, ignoring the input.

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 JSON with momentum_4week, trend_direction, and week_over_week score series for marketers. It specifies the resource (brand momentum) and the verb (get). The mention of 'Brand trajectory monitoring' distinguishes it from other sibling tools that focus on different domains (e.g., benchmarks, signals).

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

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

The description implies usage for brand trajectory monitoring and targets marketers, but it does not provide explicit when-to-use or when-not-to-use guidance. It does not mention alternatives among the many sibling tools, leaving the agent to infer 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?

No annotations are provided, so the description must disclose behavioral traits. It notes the tool returns static data ("Static Stratalize go-to-market benchmark") but fails to mention authentication needs, rate limits, or how the data is updated. This leaves significant ambiguity about side effects and prerequisites.

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

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

The description is a single, efficient sentence that front-loads the key output (CAC payback ranges) and purpose. It includes all necessary information without redundancy, though it could be split into two sentences for improved 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?

Given the lack of an output schema, the description should provide more detail about the returned JSON structure. It explains the high-level components and parameter roles but omits specifics like field names, data types, or any pagination. This is sufficient for a simple lookup tool but leaves gaps for precise 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 description adds meaning beyond the schema by explaining the optional avg_contract_value_usd for LTV:CAC calculation and the output includes channel efficiency notes. It also clarifies the purpose of the tool for SaaS CMOs/CFOs. However, it does not describe the gtm_motion enum values or the structure of the JSON response.

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 CAC payback ranges, LTV:CAC ratio guardrails, and channel efficiency notes. It specifies the target audience (SaaS CMOs and CFOs) and lists the relevant parameters, distinguishing it from generic benchmarking 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?

While the description implies using this tool for CAC benchmarking by SaaS finance leaders, it does not explicitly state when to use it over alternatives (e.g., get_saas_metrics_benchmark) or provide any exclusion criteria. The context is clear but lacks guidance on tool selection.

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

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

No annotations are provided, so the description must disclose behavioral traits. It only mentions data source (CBRE and JLL quarterly surveys), but fails to state that the tool is read-only, any rate limits, data freshness, or limitations. For a benchmark tool, this lack of behavioral context is a significant gap.

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 totaling about 25 words. The first sentence states core functionality; the second adds context (source and users). No superfluous information. 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 3 enum parameters and no output schema or annotations, the description is adequate for simple use but incomplete. It does not describe the output format (e.g., single cap rate or range, per geography or combined), nor how optional parameters affect results. This leaves ambiguity for an AI 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 0% schema description coverage, the description should compensate by explaining parameters. It mentions 'asset class, market tier, and geography' in a general sense, but does not clarify which parameters exist, their valid values (though enums are in schema), or how they interact. This adds minimal value beyond the schema 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 cap rate benchmarks for commercial real estate, specifying dimensions (asset class, market tier, geography) and data source. It distinguishes itself from sibling benchmark tools by naming CRE-specific fields and target users.

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 intended use cases (property pricing, portfolio valuation) and target users (CRE acquisition teams, asset managers, CFOs), implying when to use. However, it does not explicitly state when to avoid this tool or suggest alternatives among the many sibling benchmarks.

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

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

With no annotations, the description reveals it returns up to 15 ranked leaders from ai_citation_results or composite fallback (e.g., Salesforce 42 mentions). It does not mention auth needs, rate limits, error conditions, or data freshness, which is acceptable for a read-only tool but could be more transparent.

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 but includes a fragment ('Unprompted AI leader mentions by category query.') and some redundancy. It could be more concise and better structured.

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

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

For a single-param tool with no output schema, the description explains the return type, ranking, limit, and source. However, it lacks details on output format, error handling, and pagination, leaving gaps for an agent to invoke correctly.

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

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

Schema coverage is 0%, so the description must explain the 'category' parameter. It only says 'by category query' without specifying valid values, format, or examples, leaving the agent with minimal guidance.

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 JSON leaders ranked by mention_count (up to 15) for brand strategists, from specific sources. It also labels it as a category share-of-voice leaderboard, distinguishing it from many sibling 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?

The description implies use for brand strategists seeking AI leaders by category, but provides no explicit when-to-use or when-not-to-use guidance, nor alternatives among the many sibling tools.

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

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

No annotations are provided, so the description must fully disclose behavioral traits. It mentions a static AI disruption radar and citation-volume heuristics but does not explain data sources, update frequency, auth needs, or potential side effects. The output format is mentioned but not thoroughly.

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 brief, consisting of two sentences and a clarifying tagline. It is well-structured and front-loaded with key information (output, audience), though it lacks some 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?

For a simple single-parameter tool with no output schema or annotations, the description covers output type and audience adequately. However, it omits parameter guidance, data freshness, and potential values, leaving gaps 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 input schema has one parameter 'category' (string) with 0% description coverage. The description does not define what a valid category is, provide examples, or explain how it affects output. The agent receives no added meaning beyond the schema.

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

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

The description clearly states the tool returns a disruption_risk_score (0 to 1) with evidence strings, targeting product strategists. It distinguishes itself from equity research, making its purpose specific and 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 minimal usage guidance. It mentions 'not equity research' to exclude one use case, but does not explain when to prefer this tool over its many siblings. No alternative tools are named, leaving the agent without clear selection criteria.

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

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

No annotations provided, so description carries full burden. Describes output format (JSON with median, p25, p75, sample_size), gives example values, and clarifies the data is a static industry composite model. Lacks details on error handling or data freshness, but sufficient for a read-only benchmark tool.

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

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

Three sentences, front-loaded with key output details. Every sentence adds value without redundancy. Highly efficient.

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

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

Covers output structure, model type, and provides example values. However, lacks explanation of sample_size and does not specify if industry is optional or how it affects results. Given no output schema, description could be slightly more 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 only 33% (category described). Description mentions benchmarking 'by company_size' but does not elaborate on possible values or the role of the industry parameter. Schema itself lacks descriptions for industry and company_size, and the description adds minimal detail beyond the schema.

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

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

Clearly describes the tool returns median monthly spend, p25/p75, and sample_size for a software category, benchmarked by company_size. It distinguishes itself from org-specific spend tools, but does not explicitly differentiate from similar sibling benchmark tools like get_industry_spend_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?

States it's for finance teams benchmarking software categories and explicitly notes it is a static composite model, not org-specific. This provides clear context on when to use, but no explicit exclusion of alternatives.

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

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

With no annotations provided, the description adds minimal behavioral context: it notes the tool returns JSON and includes a disclaimer ('not legal advice'), but fails to disclose data freshness, rate limits, or what happens when no data is found.

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

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

The description is three concise sentences with critical info front-loaded: function, parameters, format, and disclaimer. No superfluous content.

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

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

For a tool with no output schema, the description could better specify the return structure (e.g., fields in the rollup). It mentions 'complaint volume and issue themes' but lacks detail on output format, leaving gaps for an AI 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?

Schema coverage is 0%, but the description explicitly states the tool filters 'by company_name' and includes an 'optional product filter,' adding meaningful context beyond the bare schema properties.

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 CFPB consumer complaint rollup JSON for risk teams, using company_name with an optional product filter. It specifies the data source, format, and intended users, distinguishing it from sibling tools like generic 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?

The description implies usage for consumer finance complaint surveillance by risk teams but does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives among the many sibling tools.

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

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

No annotations are provided, so the description carries the full burden. However, it does not disclose whether the data is live or cached, any API rate limits, authentication requirements, or whether the operation is read-only. It only lists the data returned, which is useful but insufficient for behavioral 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 a single efficient sentence (23 words) that front-loads the core purpose and key metrics. Every word adds value, and there is no redundancy.

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

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

Given no output schema and 0% schema coverage, the description provides a reasonable list of returned metrics (e.g., rankings, 1D change, protocol counts), but it does not specify the format (e.g., JSON object, array) or how the comparison data is structured. Thus, it is adequate but leaves gaps for an agent to infer the full response shape.

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 two parameters (limit, sort_by) with types and enums, but the description does not mention or explain any of them. Schema description coverage is 0%, and the description fails to compensate by adding meaning to the parameters, leaving the agent to rely solely on the schema.

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

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

The description clearly identifies the tool as providing live TVL by blockchain from DeFiLlama, listing specific chains and metrics like rankings, change percentages, protocol counts, dominance, and a comparison. This distinguishes it from sibling tools that cover other benchmarks (e.g., defi yield, gas). The verb 'get' is implicit, and the resource 'chain TVL benchmark' is explicit.

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 x402 agent context' but provides no explicit guidance on when to use this tool versus alternatives (e.g., get_defi_yield_benchmark). It does not state when not to use it or specify prerequisites. The context is implied by the tool's name and content.

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

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

With no annotations, the description must fully disclose behavior. It mentions data sources (FEMA NFIP, NGFS scenarios) and content types, but does not disclose whether the tool is read-only, requires authentication, or has rate limits. It is adequate but not thorough.

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

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

The description is extremely concise: two sentences that front-load the purpose, list key risks, sources, and audience. Every sentence adds value with no redundancy.

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

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

Given the moderate complexity (3 optional enum parameters, no output schema), the description provides source and audience context but omits return format, error handling, or example usage. It is incomplete for an agent to effectively use the tool without further schema info.

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%, meaning the description must compensate. However, it only adds high-level context about risk types and sources without explaining the three parameters (region, risk_type, property_type) or their allowed values. The agent must rely on enum names alone.

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 climate financial risk benchmarks, listing specific physical and transition risks and sources. It distinguishes itself from many sibling benchmark tools by focusing on climate risk and ESG/risk agents.

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

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

The description indicates the tool is for ESG and risk agents, giving a hint of intended use, but does not explicitly state when to use it versus other benchmark tools like get_esg_benchmark or others. No when-not-to-use or alternative guidance is provided.

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

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

With no annotations provided, the description carries full burden. It discloses that the tool is read-only (returns data), requires no login, and returns JSON format. It does not cover rate limits or error handling, but for a benchmark data retrieval tool, the transparency is above average.

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 four sentences, front-loaded with the main purpose. It is mostly concise, though the last sentence ('CMS HCRIS-style facility benchmark pack') adds redundancy and could be merged. Overall, it earns its space with minimal waste.

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

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

Given the tool has 3 parameters, no output schema, and no annotations, the description provides adequate but not complete context. It explains the benchmark type and metrics, but does not specify output structure, error cases, or data sources (beyond CMS HCRIS). It is minimally viable.

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 mentions only two parameters (bed_size and state) explicitly, with a hint about hospital_type via 'acute facility'. It does not explain valid values (e.g., state format, bed_size range, hospital_type options) or how they affect the output. This is insufficient for 3 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 it returns JSON CMS cost report benchmarks for specific metrics (IT, labor, supply chain, cost per adjusted patient day) by bed_size and state. The verb 'returns' and resource 'CMS cost report benchmarks' are explicit, distinguishing it from sibling healthcare tools like get_cms_star_rating or get_hospital_care_compare_quality.

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 hospital CFOs and ops looking at cost benchmarks, and states 'No login' suggesting ease of use. However, it does not explicitly mention when to use this tool over alternatives like get_hospital_supply_chain_benchmark or get_ehr_cost_per_bed, nor does it provide exclusions or prerequisites.

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

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

No annotations are provided, so description carries full burden. It states returns 'aggregate JSON' implying read-only, but lacks details on data freshness, pagination, or error handling. Adequate but not rich.

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

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

Three sentences, each adds value: purpose, scope distinction, domain. 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?

No output schema, but description hints at return type (aggregate JSON) and domain (Sunshine Act). Lacks detail on output fields or error scenarios. Adequate for a simple data retrieval tool.

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

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

Schema description coverage is 0%, so description must compensate. It lists the three parameters but adds no meaning beyond their names (e.g., format constraints, matching behavior). 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?

Description clearly states it returns CMS Open Payments aggregate JSON, specifies the data source (Sunshine Act), target audience (compliance teams), and distinguishes from individual attestations. This differentiates it from sibling tools which are 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?

Description indicates use for compliance teams and aggregate payment transparency, and explicitly contrasts with individual attestations. While it doesn't list alternatives or when-not-to-use, the domain specificity provides clear guidance.

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

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

With no annotations provided, the description carries the full burden. It discloses the tool is 'static' and returns educational material, but it does not mention authentication needs, rate limits, or any side effects. This is adequate but could be more explicit.

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

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

Two sentences, front-loaded with the key output, no wasted words. Efficient and clear.

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 3 parameters, no output schema, and no annotations, the description is insufficient. It explains the output but omits how inputs (state, hospital_name, star rating) influence results, leaving the agent unable to use 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 description does not mention the three input parameters (state, hospital_name, current_star_rating) from the schema. Since schema coverage is 0%, the description should clarify how these parameters affect the output, but it provides no guidance.

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 'JSON CMS Hospital Star methodology notes, domain weights, national distribution benchmarks, improvement checklist for quality leaders.' It specifies a specific resource (CMS Hospital Star methodology) and distinguishes from siblings like other benchmark tools by mentioning it's a CMS-specific static composite.

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

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

The description provides clear context that the tool returns static methodology notes and is not live patient-specific data, indicating it is appropriate for educational purposes. However, it does not explicitly name alternatives or state when not to use this tool.

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

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

With no annotations, the description carries the full burden. It discloses that data is live, updated daily, sourced from FRED, and includes weekly and monthly changes. This provides sufficient behavioral context for a read-only benchmark tool, though it does not mention potential rate limits or output size.

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

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

The description is two sentences with no wasted words. Key information (purpose, examples, source, update frequency, audience) is front-loaded and efficiently 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?

For a simple tool with one enum parameter and no output schema, the description covers purpose, examples, source, update cadence, and target audience. It omits mention of return format or error handling, but these are less critical for a straightforward benchmark retrieval.

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 sole parameter 'category' has an enum but no schema description (0% coverage). The description adds value by listing example commodities (WTI, gold, etc.) that map to categories, but it does not explicitly explain the parameter's role or the meaning of each enum 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 provides 'live commodity price benchmarks' and lists specific commodities (WTI crude, natural gas, gold, etc.), which precisely defines the resource and verb. It distinguishes from sibling tools by focusing on commodities rather than other 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?

The description implies usage for 'traders and macro analysts' and mentions 'inflation pressure signal,' but lacks explicit guidance on when to use this tool versus alternatives like get_gas_benchmark or get_inflation_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?

With no annotations provided, the description must fully disclose behavior. It indicates that data comes from 'synced public filing rollups' and describes it as an 'employer-sponsored wage transparency lookup,' implying it is a read operation. However, it does not cover other relevant aspects such as auth requirements, rate limits, or whether the aggregation is pre-computed or computed on the fly. The description provides moderate transparency but leaves gaps.

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: two sentences with no filler. It front-loads the core purpose ('Returns DOL LCA and H-1B wage disclosure aggregates JSON') and includes filtering options and data source in the second sentence. Every word is informative and well-structured.

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

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

Given the absence of annotations, output schema, and high context signals (4 parameters with 0% schema coverage, many siblings), the description is incomplete. It does not mention the aggregation level (e.g., per company, per year), whether results are paginated, the time range of the data, or example usage. This leaves the agent with insufficient context to reliably invoke the tool for non-trivial queries.

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, so the description must compensate. It enumerates the optional filters (job_title, state, fiscal_year) and implies they are used for filtering. However, it does not add details about valid values, formats, or behavior when omitted (e.g., defaults). The description adds some semantic value beyond the bare schema, but not enough to fully 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 states that the tool returns DOL LCA and H-1B wage disclosure aggregates in JSON format, specifying the required parameter (company_name) and optional filters (job_title, state, fiscal_year). While it distinguishes itself by mentioning 'aggregates' for comp analysts, it does not explicitly differentiate from sibling tools like get_employer_h1b_wages, so it loses a point for not fully clarifying unique value.

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

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

The description provides no guidance on when to use this tool versus alternatives such as get_salary_benchmark or get_employer_h1b_wages. It implies the target audience ('comp analysts') but fails to state when this tool is appropriate or when other tools should be used instead. This omission leaves the agent without 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?

With no annotations, the description carries the full burden. It mentions a fallback behavior ('May fall back to Microsoft or Google style rows') and hints at switching signals, but does not disclose whether the operation is read-only, requires authentication, or has side effects. Some behavioral context is provided but incomplete.

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

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

The description is brief (two sentences) and front-loads key information (return structure, audience). However, the phrase 'Switching narrative signal' is cryptic and reduces clarity. Overall, it is fairly concise and well-structured.

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

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

For a simple tool with one parameter and no output schema or annotations, the description partially explains the return format and fallback behavior. The mention of 'Switching narrative signal' is unexplained, leaving ambiguity. It is adequate but not fully comprehensive.

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 only parameter, vendor_name, lacks description in the schema (0% coverage). The tool description adds meaning by referring to it as 'target_vendor', indicating it is the vendor being replaced. However, it does not specify format, examples, or constraints, so the value added is marginal.

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 JSON with specific fields (top_replacing_vendors, mention counts, evidence_snippets) for competitive intel teams tracking rip-and-replace of a target vendor. It effectively communicates the core function, though it could explicitly state 'retrieves' instead of 'returns'.

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

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

The description implies usage for competitive intelligence but provides no explicit guidance on when to use this tool versus the numerous sibling tools (e.g., get_market_intelligence_brief, get_vendor_alternatives). No when-not-to-use or alternative references are 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?

No annotations are provided, so the description carries full disclosure responsibility. It mentions 'live material cost escalation signals,' hinting at dynamic data, but does not explain update frequency, caching, or any limitations. Behavioral traits like read-only nature or required permissions are omitted.

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

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

The description is very concise: two sentences. The first sentence lists all key outputs, the second provides data sources and audience. No superfluous text, front-loaded with the most important information. Efficient and well-structured.

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

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

The description covers inputs (building type, region) and output types (cost per SF, ratios, escalation) but fails to explain the output format or structure. No output schema exists, so the description should provide more details on what the tool returns (e.g., a single number, a range, or multiple values). The 'construction_class' parameter is also not addressed. Adequate but incomplete.

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

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

The schema has 0% description coverage, so the description must compensate. It explains region and building_type in context (cost per SF by type/region) but does not clarify 'construction_class' (A/B/C). The enum values are self-documenting, but the specific meaning of class in this domain is not provided. Overall, partial but not complete parameter 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?

The description clearly states the tool provides construction cost benchmarks including hard/soft costs, contingency standards, and material cost escalation. It names its purpose and target audience. However, given the large set of sibling benchmark tools, it does not explicitly differentiate itself (e.g., from financing or market rent benchmarks), so it does not achieve the highest score.

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 developers, lenders, and project owners,' which implies appropriate use cases. However, it does not specify when to avoid this tool or recommend alternative siblings for different benchmark needs. Usage guidance is implied but not explicitly comparative.

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

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

No annotations are present, so the description must carry the full burden. It fails to disclose whether the tool is read-only, any authentication needs, rate limits, or what exact data format is returned. It only mentions 'live' but lacks depth.

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

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

Two short sentences with no filler. Every word adds value, front-loading the source and indicators.

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 and basic use case, but lacks details on data frequency, time range, and response structure. Given no output schema, more completeness would help agents.

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 has 0% description coverage; the description lists indicators that map to the 'focus' enum values, but does not explicitly map each enum to its indicators (e.g., which correspond to sentiment, spending, saving). Additional clarification would improve parameter understanding.

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

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

Clearly states it provides live consumer sentiment benchmarks from FRED, listing specific indicators. The verb 'get' and resource 'consumer sentiment benchmarks' are specific, and the tool is easily distinguishable from its many siblings.

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?

Implied usage for GDP and equity analysis via 'consumer signal', but no explicit when-to-use or when-not-to-use guidance. Given many sibling tools, some comparative guidance would help.

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

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

With no annotations provided, the description carries full burden. It identifies data sources but does not disclose behavioral traits such as whether the tool is read-only, caching behavior, rate limits, or the exact format/detail level of the output (e.g., averages vs. distributions).

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, to the point, with all essential information front-loaded. No extraneous words or redundant 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 no output schema and no annotations, the description covers the main metrics but lacks details on return format (e.g., table, percentiles, single value vs. range). The schema only has enums, and no descriptions exist. More 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 description coverage is 0%, so the description must compensate. It mentions filtering 'by credit rating tier and industry', which maps to the two parameters, but does not explain the enum values (e.g., what 'unrated' means) or provide additional context beyond the schema names. Baseline 3 due to schema 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?

Description clearly states it provides corporate leverage and debt benchmarks (Net Debt/EBITDA, interest coverage, debt maturity profiles) by credit rating tier and industry, distinguishing it from sibling benchmark tools focused on other sectors or asset classes.

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 use cases (refinancing, covenant setting, credit rating management) and user personas (CFOs, treasurers), implying appropriate contexts. Does not explicitly list alternatives or when not to use, but the specific metrics and filters provide clear 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?

No annotations are provided, so the description carries the full burden. It discloses the metrics returned and data sources but does not mention details like refresh frequency, data range, authentication needs, or any side effects. For a read-only tool, this is adequate but not thorough.

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 composed of three concise sentences: the first states the tool's output and filters, the second cites data sources, and the third identifies the target audience. No unnecessary words, and information is front-loaded.

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

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

Despite lacking an output schema, the description covers the tool's purpose, input dimensions, data sources, and audience. It does not specify temporal context (e.g., current quarter vs. historical) or return format, but for a simple benchmark tool, the provided context is largely sufficient.

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

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

The description mentions filtering by property type and lender type, aligning with the two schema parameters, but does not explain the enum values fully (e.g., debt_fund, bridge are omitted). With 0% schema description coverage, the description should provide more detail on parameter meanings and usage to compensate.

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 CRE debt benchmarks (DSCR minimums, LTV maximums, spread ranges) filtered by property type and lender type, with named data sources and a specific target audience. This differentiates it from sibling tools like get_cap_rate_benchmark or get_corporate_debt_benchmark, making the purpose highly specific and distinct.

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 implicitly guides usage by stating 'For CRE CFOs and capital markets teams structuring financings,' but does not explicitly mention when to use it versus alternatives or when not to use it. However, the context is clear enough for an AI agent to infer appropriate use cases.

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

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

No annotations provided, so description carries full burden. It discloses 'live' and 'updates daily' but lacks deeper behavioral traits like data source latency, rate limits, or error handling. Basic transparency but could be improved.

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

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

Three short, front-loaded sentences: first defines content, second states update frequency, third specifies audience. No wasted words; structure is clear and efficient.

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

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

For a simple tool with one optional parameter and no output schema, the description covers data content and use case. Lacks note on return format, but given simplicity, it is nearly complete.

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

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

Schema coverage is 0% with one enum parameter. Description adds context by mentioning 'OAS by rating tier', linking to parameter purpose. However, it does not explicitly explain each enum value (all, ig, hy, bbb), leaving some ambiguity.

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

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

Description clearly states the tool provides live credit spread benchmarks from FRED ICE BofA indices, listing specific metrics (OAS, TED spread, 2s10s spread, distress signal). It is specific and distinguishes from other benchmark tools by focusing on credit spreads.

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 sets context with update frequency (daily) and target audience (credit analysts, fixed income PMs), implying its use case. However, it does not explicitly state when to avoid or mention alternatives among siblings.

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

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

With no annotations, the description discloses the data source (NCUA quarterly call report) and the general nature (benchmark read). However, it does not detail update frequency, output format, or any limitations, leaving some behavioral aspects implicit.

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, efficiently stating what the tool provides, the data source, and the intended audience. Every word adds value, and it is front-loaded with key 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 two parameters and no output schema, the description covers the essential purpose, source, and audience. However, it misses explanation for the optional charter_type parameter and does not specify output format, which is needed for full 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 only mentions 'by asset size', partially explaining the required asset_size_tier parameter. It omits any explanation of charter_type (optional with enum values). Since schema description coverage is 0%, the description should compensate but falls short.

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 credit union financial benchmarks (capital ratios, net interest margin, etc.) by asset size from NCUA data. However, it does not explicitly differentiate from sibling tools like get_bank_financial_intelligence, relying on the name and context to imply uniqueness.

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 the target audience ('credit union CFOs preparing for NCUA exams and board reporting'), providing clear context for when to use it. But it lacks explicit guidance on when not to use or 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?

No annotations provided, but the description discloses key behaviors: rolling window, Pearson correlation, beta, dominance context, and data source. Could be improved by stating read-only nature or limitations (e.g., only three assets).

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

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

Two sentences, no wasted words. Front-loaded with the main functionality, then source and use case. Excellent conciseness.

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

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

For a simple tool with one parameter and no output schema, the description covers the main outputs (correlation matrix, beta, dominance, signal) and data source. It could mention output format or data structure, but 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?

Schema coverage is 0% and description does not explain the 'period' parameter, despite allowing '7d', '30d', and '90d'. It implies a 30-day default, but does not clarify that the parameter sets the rolling window length. This 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 computes a 30-day rolling correlation matrix for BTC, ETH, and SOL, with specific metrics (Pearson, beta, dominance, diversification). This is distinct from many sibling tools that are about financial benchmarks, not crypto correlation.

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

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

Mentions 'For crypto portfolio agents' and 'Source: DeFiLlama historical prices', implying use case. However, it lacks explicit alternatives or when-not-to-use guidance compared to other tools on the server.

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

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

No annotations provided, so description must carry behavioral context. It does not mention that the tool is read-only, any required authentication, rate limits, data freshness, or how results are filtered beyond the parameters. Only states source (DeepDAO public data).

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

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

The description is short (three sentences) and front-loaded with the main purpose. It provides median benchmark values for context, adding value without unnecessary detail.

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 simple parameters, the description explains output content but not structure (e.g., format of top DAOs vs median benchmarks). Lacks behavioral details like pagination or default sort, leaving ambiguity.

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

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

Schema description coverage is 0% (no property descriptions), and the description does not explain the two parameters (sort_by, min_treasury_usd). The agent gains no additional meaning beyond the parameter names and enum values.

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 DAO treasury benchmarks including specific metrics like treasury size, stablecoin percentage, runway, and governance token concentration. It distinguishes from sibling benchmark tools by focusing specifically on DAO treasuries.

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. Given many sibling benchmark tools (e.g., get_chain_tvl_benchmark, get_defi_yield_benchmark), the description fails to differentiate usage scenarios.

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

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

With no annotations provided, the description carries the full burden. It discloses free public API, no key required, and Ed25519-signed responses, which adds value beyond the schema. It does not mention rate limits or caching, but the key behavioral traits are covered.

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

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

The description is three sentences with no wasted words. It front-loads the core purpose and data, then adds filter details and behavioral info efficiently.

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 (2 optional params) and no output schema, the description adequately covers input defaults, return data (APY bands, TVL, chain, pool id), and response signing. It is complete enough for an agent to use correctly, though pagination or limits are not mentioned.

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%, giving a baseline of 3. The description adds examples (e.g., 'USDC, DAI, ETH' for asset, 'aave-v3, compound-v3' for protocol) and clarifies that filters are substring matches, providing additional 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 it provides DeFi lending and stable yield benchmarks from DeFiLlama, listing specific data points (APY bands, TVL, chain, pool id). This distinguishes it from sibling benchmark tools, which cover different 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 explains that optional protocol and asset filters are available and that unfiltered returns stablecoin-marked pools. It implies usage for DeFi yield data but does not explicitly contrast with sibling tools like get_stablecoin_yield_benchmark, though the context is clear.

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

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

No annotations are provided, so the description carries full burden. It mentions sources (NAHB, ULI, industry composite) which adds transparency, but does not disclose update frequency, limitations, or whether the data is a snapshot. Given the lack of annotations, this is adequate but not comprehensive.

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

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

Two sentences front-load the key metrics and usage context, with zero wasted words. Every sentence adds value: first lists outputs, second specifies users and sources.

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

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

The tool is simple (2 params, no output schema) and the description covers domain, users, and sources. However, it does not explain the return format or parameter choices, leaving some gaps that could cause ambiguity for an 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?

Schema description coverage is 0%, so the description must fully explain parameters. It mentions 'by product type' but does not describe the allowed values or the optional market_tier parameter. The description adds minimal meaning beyond the schema itself.

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 development pro forma benchmarks including yield on cost, profit-on-cost, construction-to-perm spread, and return hurdles by product type. This is a specific verb+resource combination that distinguishes it from sibling benchmark tools like cap rate or construction cost 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?

The description explicitly says 'For developers underwriting new projects and lenders sizing construction loans,' which provides clear context for when to use this tool. It does not explicitly exclude alternative tools, but the context is sufficient 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?

No annotations provided, so description must convey behavioral traits. It describes methodology (SEC EDGAR aggregate, Sloan accruals model) which adds transparency. Lacks explicit safety profile (e.g., read-only hint) but implies non-destructive data retrieval. Could mention data freshness or 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?

Two sentences with no redundancy. Front-loaded with core purpose, metrics, and source. Every word adds value.

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

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

Given no output schema and 2 parameters, the description covers the required parameter but is incomplete on the optional parameter. Explains source and intended audience but omits output format or potential error conditions. Adequate but not comprehensive.

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%. Description lists metrics but fails to mention the optional parameter 'revenue_recognition_model' and does not explain how parameters affect output. Only 'sector' is implied. Users would need to infer parameter usage from 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?

Clear verb+resource: 'get_earnings_quality_benchmark' with specific metrics (accruals ratio, cash conversion, revenue recognition risk) and source. Differentiates from many sibling benchmark tools by focusing on earnings quality.

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 target users (CFOs, auditors, analysts) and use case (financial reporting risk before M&A/investment). However, no guidance on when not to use or comparison to alternatives among siblings.

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?

Without annotations, the description conveys that the tool returns JSON data, is read-only, and provides an optional gap computation. It does not detail authentication or rate limits, but the example value adds useful context. The behavioral traits are adequately disclosed for a simple benchmark tool.

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

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

The description is concise with two sentences: the first explains core function and optional behavior, the second provides a concrete example. No extraneous words; every sentence adds value.

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

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

For a simple tool with 3 parameters and no output schema, the description covers the return value, optional parameters, and provides a benchmark example. It could more explicitly explain the role of vendor_name, but overall it is largely complete for an agent to 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?

The description adds meaning to bed_count and annual_cost by explaining their role in computing gap_vs_benchmark. However, the required vendor_name parameter is only indirectly referenced via an example (Epic) and not explicitly explained. Given 0% schema description coverage, the description partially compensates but leaves vendor_name ambiguous.

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 benchmark_cost_per_bed_median and optionally gap_vs_benchmark when bed_count and annual_cost are provided, specifically for health IT finance. It distinguishes itself from many sibling benchmark tools by focusing on EHR cost per bed.

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

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

The description indicates the tool is for health IT finance benchmarking, with clear context on when to provide bed_count and annual_cost for the gap calculation. However, it does not explicitly state when not to use it or mention alternatives among siblings.

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

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

With no annotations, the description discloses dependency on an API key, fallback behavior (empty/guidance), non-realtime nature, and dependency on energy price strip. It lacks details on authentication, rate limits, or error handling.

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

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

Two concise sentences with no wasted words. The key points are front-loaded, making it efficient for an agent to parse.

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

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

Given no parameters and no output schema, the description adequately explains preconditions, output type, and limitations. It is sufficient for a simple 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 zero parameters, so the description does not need to explain them. Baseline score of 4 is appropriate as there is no additional meaning to add.

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 EIA spot-price style JSON for commodity strategists, distinguishing it from general energy data tools. However, it does not explicitly differentiate from the many sibling tools that might also return energy-related 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?

It provides context on when to use (when EIA API key is configured) and not to use (not realtime exchange prints). However, it does not mention alternative tools among the siblings for other energy data needs.

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 behavioral traits beyond the annotations (readOnlyHint, destructiveHint) by detailing what the tool returns (positions, targets, invalidation levels, confidence scores) and providing a concrete example. No contradictions with annotations.

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

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

The description is two sentences, front-loaded with the tool's purpose and key outputs. Every sentence adds value without redundancy, making it highly efficient.

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

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

For a tool with one optional parameter and no output schema, the description fully covers the input (asset symbol or 'all'), output (positions, targets, invalidation, confidence), and supported assets. No gaps remain.

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 baseline is 3. The description adds value by providing an example ('Gold Wave 5 target $2,750') that illustrates expected values and output format, enhancing understanding beyond the schema's parameter description.

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

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

The description clearly states the tool provides Elliott Wave position, targets, invalidation levels, and confidence scores for specific assets (BTC, SPY, TLT, Gold). It uses a specific verb (get) and resource (Elliott Waves), distinguishing it from sibling tools that cover different financial metrics.

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 identifies the target audience (traders and portfolio managers) and context (tracking technical market structure). While it does not list alternatives or when not to use it, the context is sufficiently clear for the intended use case.

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

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

No annotations are provided, so the description carries the full burden. It indicates a read operation by stating 'Returns', but does not explicitly confirm idempotency, safety, or rate limits. The output format (JSON) is mentioned, but behavioral traits beyond that are lacking.

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 a concise phrase, front-loaded with purpose. Every word adds value: data type, audience, example, and core concept. No unnecessary repetition.

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 the essential aspects: data returned (stats, job titles, state mix), format (JSON), and target users. Given the simple single-parameter input and lack of output schema, it is sufficiently complete for a straightforward query tool. No major gaps.

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

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

The single parameter employer_name is well-described in the schema with examples, and the description adds context (querying DOL LCA filings) that enriches understanding. With 100% schema coverage, the baseline is 3, but the contextual addition justifies a 4.

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

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

The description clearly states the tool returns JSON prevailing wage stats, certified job titles, and state mix, targeting HR analytics and talent strategy teams. It specifies the data source (DOL LCA filings by employer_name) and gives an example, distinguishing it from sibling tools that focus on other 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?

The description implies use for H-1B compensation intelligence and for HR/talent teams, but does not explicitly state when not to use or compare to alternatives. No exclusions or when-to-use guidance beyond the target audience.

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

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

No annotations provided, so description must cover behavioral traits. It mentions data sources and metrics but lacks details on operation mode (e.g., synchronous, data freshness), pagination, or any side effects. Only indicates it is a read operation.

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

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

Three efficient sentences, front-loaded with purpose, followed by sources and audience. No redundancy.

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

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

Given the tool's simplicity (2 enum parameters, no required, no output schema), the description is largely complete for an agent to infer it returns ESG benchmark data filtered by sector and focus. However, it lacks output format details, which would be beneficial.

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 description should explain parameters. It mentions 'by sector' and lists metrics that map to the 'focus' parameter, but does not explicitly define each enum value's output or how parameters interact. Insufficient detail for an agent to understand parameter semantics.

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

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

Description clearly states it provides ESG benchmarks by sector, listing specific metrics (carbon intensity, net zero commitments, etc.) and data sources. It distinguishes itself from sibling benchmark tools by its ESG focus and target audience.

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 audience is implied ('For sustainability agents and ESG analysts'), but no explicit guidance on when to use this tool versus other benchmark tools 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?

No annotations are provided, so the description carries full burden. It fails to state whether the tool is read-only, requires authorization, has rate limits, or any side effects. The description only describes content, not behavior.

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

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

The description is a single sentence with no redundancy. Every word adds meaning. 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?

Lacking output schema and parameter explanation, the description does not adequately inform an agent about return values or usage details. For a tool covering a complex regulatory framework, more context (e.g., what 'coverage' means in practice) is needed.

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

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

The single parameter 'nistFunction' is undocumented. Schema coverage is 0%, and the description does not explain its role or how enum values affect results. The description adds no value beyond the schema's enum list.

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 provides EU AI Act coverage with composite mode and implementation guidance. It is specific enough to distinguish from siblings like 'get_uk_fca_coverage' or 'get_category_ai_leaders', but the jargon ('composite mode', 'control library context') could be clearer.

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

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

The description offers no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, disclaimers, or context where this tool is most appropriate. Siblings cover various benchmarks, but without explicit when-to-use statements, an agent lacks selection criteria.

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

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

With no annotations provided, the description must fully disclose behavior. It states the output is JSON and that the tool is synced from USASpending, but it does not mention side effects, authentication requirements, rate limits, data freshness, or whether pagination is needed. This is insufficient for an agent to predict behavior safely.

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 core purpose and key parameters. The second sentence adds synonymous phrases but does not add significant new information. It is well-structured for quick parsing.

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

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

Given the lack of annotations, output schema, and low parameter documentation, the description should cover more context such as output structure, limitations, or usage examples. It only provides minimal context, leaving potential users uncertain about how to effectively invoke the tool or 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 only 20% (only 'state' has a description). The description lists some parameters (vendor_name, agency_name, naics_code, fiscal_year) but does not explain their semantics or expected formats beyond names. For a tool with low schema coverage, the description should compensate with detailed parameter guidance, 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 states the tool returns USASpending-synced obligation rollups JSON for federal contract intelligence, specifying the key parameter (vendor_name) and optional filters. It distinguishes the tool's purpose from numerous sibling tools focused on benchmarks or other intelligence.

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 context that the tool is for government affairs teams and federal vendor spend intelligence, implying use cases. However, it does not explicitly state when to prefer this tool over similar siblings like get_vendor_contract_intelligence or get_vendor_market_rate, nor does it provide exclusion criteria.

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

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

With no annotations provided, the description carries the full burden. It discloses that the output is illustrative, conditioned on latest FRED fed funds print, and solely for scenario planning. This covers key behavioral traits, though it omits details like data freshness or error handling.

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-loading the core function and then adding essential context. No filler words; every sentence adds value.

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

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

Given zero parameters and no output schema, the description fully covers purpose, usage, and output format. It leaves no ambiguity about what the tool returns and for what audience.

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 no parameters, so schema coverage is 100%. The description does not need to add parameter meaning, as there are none. It correctly handles the absence by focusing on the output and 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?

The description clearly states it returns illustrative cut, hike, hold probabilities for the next three FOMC meetings, distinguishing it from market-odds tools. The verb 'returns' with specific resource 'probabilities' and scope 'next three meetings' is precise and differentiates it from sibling tools.

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

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

The description specifies it is for macro educators and scenario planning, and explicitly notes it is not futures-implied market odds. This provides clear context for when to use (educational) and when not to (market expectations), but does not name specific alternative tools for market odds, which would elevate it to 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?

No annotations exist, so the description carries the burden. It adds value by stating data source (FRED), update frequency (daily), and scope (live, with historical rate changes). This compensates for missing 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?

Single sentence, front-loaded with key info, no wasted words. Effectively conveys what the tool does in a compact form.

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 param and no output schema, the description covers source, update frequency, and specific outputs. Missing parameter explanation is a minor gap, but overall sufficient for an agent to understand basic functionality.

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 single optional parameter 'base_currency' with enum values is not explained in the description. The listed currency pairs are all USD-based, but it's unclear how the parameter affects results. Schema description coverage is 0%, and the description adds no semantic 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?

Description clearly specifies 'Live major currency pair benchmarks' and lists exact pairs and derived metrics, distinguishing it from other benchmark tools. The verb 'get' and resource 'fx_rate_benchmark' are precise.

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

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

No explicit when-to-use or when-not-to-use guidance is provided. Usage is implied by the resource name and description, but no alternative tools or exclusions are mentioned among many sibling benchmarks.

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

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

No annotations are provided, so the description carries the full burden. It reveals data source (public chain RPCs) and auth requirements (none), but does not mention caching, latency, or potential rate limits. Still, it offers significant 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 concise with three sentences. It front-loads the key purpose and then adds details and advantages. 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?

For a simple benchmark tool with no output schema, the description provides a good overview of inputs and outputs (Gwei, USD cost, etc.), source, and auth. It could mention whether data is real-time but is otherwise complete.

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

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

The schema has one parameter 'chain' with enum values. The description adds meaning by specifying the supported chains (Ethereum, Base, Solana) and the context of what the parameter controls, even though it doesn't repeat the schema. It compensates for the 0% schema coverage by explaining the effect.

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 gas price benchmarks for Ethereum, Base, and Solana, specifying data points like Gwei, USD cost, congestion category, and x402 context. It is distinct from sibling tools which cover other benchmarks (e.g., get_chain_tvl_benchmark, get_defi_yield_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 when to use (for gas prices) and highlights that no API key is required, making it easy to use. However, it does not explicitly state when not to use or provide alternatives among siblings, though the context is clear.

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

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

With no annotations provided, the description must fully disclose behavior. It mentions 'live' data and being 'subject to rate limits,' which is useful. However, it does not specify whether the tool is read-only, authentication requirements, or error handling scenarios. These gaps reduce transparency slightly.

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 a single sentence plus a fragment. It front-loads the main action and includes key details (live, rate limits, target audience). It could be slightly more structured, but every part 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 covers the core purpose and parameter hint. However, it lacks information about the response format, error handling, or authentication, which would be needed for complete understanding. It is adequate but not comprehensive.

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 on the parameter. The description adds the phrase 'org_or_company footprint query,' which gives some semantic meaning beyond the schema's raw string type. However, it lacks details on acceptable formats, examples, or constraints, providing only minimal additional 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: returning live GitHub organization and repository stats JSON for developer relations teams via public API. It distinguishes itself from the many sibling tools (which are mostly financial/industry benchmarks) by focusing on GitHub ecosystem data, making its purpose very specific.

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

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

The description mentions it is 'for developer relations teams' and an 'open-source ecosystem snapshot,' implying usage context. However, it does not explicitly state when to use this tool over alternatives, nor does it describe when not to use it or any prerequisites. Given the large sibling list, more explicit guidance would be helpful.

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

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

No annotations are provided, so the description bears the full burden. It lists outputs (YTD returns, P/E, signal) but lacks details on data source, update frequency, or any behavioral traits like rate limits or pagination.

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

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

The description is a single, efficient sentence that front-loads the purpose and lists indices and metrics. It is concise with no wasted words, though it could benefit from better structure (e.g., separating indices from metrics).

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 optional param, no output schema), the description is adequate for basic understanding. However, the missing parameter semantics create a notable gap that limits 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 input schema has one parameter 'region' with enum values but no descriptions. The tool description provides zero additional information about the parameter or how it affects results, despite low schema coverage (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 explicitly states it provides global equity index benchmarks, lists specific indices (S&P 500, Nasdaq, etc.), and mentions key metrics (YTD returns, P/E ratios, risk signal), clearly distinguishing it from sibling tools that focus on other benchmark types.

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

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

The description clearly indicates the tool is for global equity index data, making its usage context obvious among many sibling tools. However, it does not explicitly state when not to use it or mention alternatives.

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

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

With no annotations, the description carries full transparency burden. It discloses the output values, static nature, and that it is a composite model not based on invoices. This gives the agent a good sense of behavior, though update frequency is not mentioned.

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 efficiently convey the tool's purpose, output values, target audience, and distinguishing features. No unnecessary words.

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

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

For a simple tool with one optional parameter and no output schema, the description covers the output and nature well but fails to explain the 'category' parameter, leaving its usage ambiguous. Adequate but incomplete.

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

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

The input schema has one undocumented parameter 'category' (0% schema description coverage). The description does not mention this parameter at all, leaving the agent uninformed about what values are valid or how it affects 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 it returns JSON with benchmark metrics (typical_gpo_savings_pct, leakage, risk threshold, top categories) for materials managers in group purchasing. It specifies it is a static HFMA plus CMS-style composite model for acute care, distinguishing it from invoice-based tools.

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

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

The description implies usage for materials managers benchmarking group purchasing and warns 'not your invoices', but lacks explicit guidance on when to use this tool versus siblings like get_hospital_supply_chain_benchmark or get_healthcare_vendor_market_rate.

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?

Discloses it uses AI citations or defaults from Epic/Meditech and returns JSON, but lacks information on read-only nature, permissions, data freshness, or side effects. With no annotations, more transparency is expected.

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 but somewhat fragmented and repetitive (e.g., 'Healthcare vendor narrative scan' and 'Category recommendation audit'). It could be more streamlined.

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 some return fields (vendors, AI consensus, sample size) but is vague about the full output structure. It is adequate for a simple tool but could be more precise.

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 parameter descriptions. The description mentions 'health IT strategists' but does not explain the 'category' parameter or its expected values, failing to add meaning beyond the schema.

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

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

The description clearly states it returns top recommended vendors, AI consensus, and sample size for health IT strategists, distinguishing it from generic category tools. However, it could be more explicitly differentiated from siblings like get_top_vendors_by_category and get_category_ai_leaders.

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 implies usage for health IT strategists but does not specify when to use this tool versus alternatives, nor does it provide exclusions or prerequisites.

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

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

With no annotations provided, the description carries the full burden. It does not disclose behavioral details like data freshness, authentication requirements, or side effects. It only states it returns a payload, which is basic.

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

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

The description is concise, at 2-3 sentences, with no redundant information. It front-loads the key action and resource.

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 absence of annotations, output schema, and only 2 parameters, the description is minimal. It explains the return type but not structure, error handling, or data source context, leaving gaps.

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

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

Schema description coverage is 0%. The description adds value by clarifying the 'category' parameter with examples (EHR, staffing, food, waste, med-surg) and mentioning embedded hints, but does not elaborate on 'vendor_name' or provide format details.

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 JSON structured market-rate payload for healthcare vendor categories, with specific examples (EHR, staffing, food, waste, med-surg). This distinguishes it from general benchmark tools in the sibling list.

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

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

The description implies usage for healthcare vendor market rates but does not provide explicit guidance on when to use this tool versus alternatives, such as other benchmark tools in the sibling list.

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

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

No annotations provided, so description carries full burden. It indicates conditional fields ('when present') and data source ('synced public data'), but does not disclose response size, latency, or access restrictions. Adequate but not thorough.

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

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

Single sentence with a trailing tagline. No redundant words, but the structure could be improved by separating purpose from technical details. Still 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?

With no output schema, the description lacks details on return structure, fields always present, or pagination. Mentions 'example mortality and HCAHPS-linked fields' but is vague. Adequate for a simple query tool.

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

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

Schema coverage is 100% for the single parameter hospital_name. The description only reiterates the parameter's role without adding constraints like case sensitivity or partial matching. No added value beyond schema.

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

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

Description clearly states verb 'Returns', specific resource 'CMS Hospital Compare quality scores', and target audience 'clinical ops leaders'. Mentions exact parameters and data source, distinguishing it from sibling tools like get_cms_star_rating.

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. While it mentions 'clinical ops leaders', it does not explain when to prefer this over sibling tools like get_cms_facility_benchmark or get_value_based_care_performance.

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

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

With no annotations provided, the description carries the full burden. It discloses a key behavioral trait: a ~17% median fallback when data is unavailable. However, it does not mention authentication, rate limits, or error handling. The disclosure about the fallback adds value, but overall transparency is adequate but not exhaustive.

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

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

The description is two sentences with no filler. It front-loads the core purpose and includes critical details (metric, audience, source, fallback) in a compact format. Every piece 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 complexity (healthcare benchmarking with percentiles and a fallback), the description covers the essential aspects: what is returned (p25/p50/p75), the data source, the fallback, and the audience. Without an output schema, it explains the return structure sufficiently. Minor omission: no example or format for state or bed_size values.

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. It mentions parameters only in passing ('by bed_size and state'), providing no details on valid values, units, or formats. For example, bed_size is a number but the expected range (e.g., number of beds) is unspecified. This minimal addition does not 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 states the tool returns JSON supply cost percentiles (p25, p50, p75) of operating expense for hospitals, filtered by bed_size and state, sourced from CMS HCRIS. This specific verb+resource combination differentiates it from the many other benchmark tools in the sibling list.

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

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

The description does not provide any guidance on when to use this tool versus alternatives. It mentions the target audience (hospital CFOs) and parameters, but fails to specify exclusions or conditions that would help an agent choose this over sibling benchmarks like get_asc_cost_benchmark or get_cms_facility_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?

No annotations are provided, so the description carries full burden. It indicates the tool provides live data from FRED and Census, but does not disclose update frequency, data limitations, authentication requirements, or whether it returns time series or single values. Behavioral aspects beyond purpose are minimal.

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

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

The description is three sentences, front-loaded with the core purpose, and each sentence adds value (what, source, use case). No redundant or unnecessary information.

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

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

The tool is simple with two enum parameters and no output schema. The description covers purpose and audience adequately but does not describe the output format or any additional context like whether it supports historical queries. Given the lack of output schema, some details are missing.

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

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

Schema description coverage is 0% and the description adds little meaning beyond the schema. It mentions 'by market tier' which may relate to structure_type or region, but does not explain what each parameter does or how to use them. The enums are not elaborated.

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 housing supply indicators (starts, permits, completions, absorption) from FRED and Census, and specifies it as a leading indicator for housing prices. It also lists target users (developers, lenders, investors, policy analysts), 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 mentions the tool is a leading indicator for housing prices and for specific users, but does not explicitly state when to use it vs alternatives or when not to use it. Usage is implied by the domain, but no explicit guidance is provided.

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

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

No annotations provided, so description carries full burden. It mentions source and that data is free, but does not disclose behavioral traits such as read-only nature, rate limits, authentication requirements, or error conditions. This is insufficient for a tool with no 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?

Three sentences, front-loading the core purpose and use cases. No redundant words. Every sentence contributes essential 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 two parameters and no output schema, the description gives usage contexts and data source. Missing details about output format, data freshness, and geographic scope, but overall adequate for a straightforward data retrieval tool.

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

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

Schema coverage is 50% (only metro_area has a description). The description adds 'by metro area and bedroom count' but does not clarify that bedroom_count is optional or its valid range (0-4). It adds minimal value beyond what the schema already provides.

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

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

Description clearly states it provides HUD Fair Market Rents by metro area and bedroom count, with explicit use cases (affordable housing underwriting, Section 8 compliance, LIHTC calculations, housing authority budgeting). Distinguishes itself from sibling tools which are 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?

Explicitly lists contexts where this tool should be used (underwriting, compliance, calculations, budgeting). Does not mention when not to use or provide alternatives, but the use cases are specific enough to guide the agent.

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?

Without annotations, the description carries the burden of behavioral disclosure. It states the tool returns static, curated data, indicating no side effects or mutations. The description clearly marks it as a snapshot, so an agent can infer read-only behavior. No contradictions.

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

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

Two sentences with no wasted words: the first defines what the tool does and what it is not, the second specifies the content and use case. Information is front-loaded 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 no output schema, the description sufficiently outlines the return type (JSON) and content (GDP, inflation). For a simple snapshot tool, this is adequate. However, explicit field names or structure 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 input schema has zero parameters, so description cannot add parameter-specific meaning. Baseline for 0-parameter tools is 4. The description adds context about the output (JSON, static composites) which is sufficient.

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 static IMF World Economic Outlook-style macro composites (GDP, inflation) in JSON format, and explicitly distinguishes it from live IMF API pulls or country desks. The target audience (economists) and context (academic briefing) are specified, ensuring unambiguous purpose.

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

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

The description provides usage context by noting this tool offers curated, static data rather than live API pulls, implying when not to use it. However, it lacks explicit comparisons to sibling tools like get_inflation_benchmark, so guidance on when to use this over alternatives is indirect.

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

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

With no annotations, the description carries full burden. It clarifies the data source (static composite, not transactional GL data) and mentions example medians. However, it lacks disclosure about required permissions, error handling, or performance traits.

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

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

The description is a single run-on sentence with technical jargon and lack of clear structure. It attempts to convey multiple pieces of information but is not well-organized.

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

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

For a tool with two parameters and no output schema, the description is incomplete. It partially describes the return value structure but does not explain parameter usage, valid inputs, or other possible fields in the JSON response.

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 0% description coverage for its two parameters (industry, company_size). The description does not explain valid values, formats, or how to use company_size. The mention of examples does not map to 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 the tool returns JSON with median_total_spend_monthly and category_breakdown, indicating it provides industry spend benchmarks. It specifies it's a static composite, not transactional data, and gives examples. However, it does not explicitly differentiate from many 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?

No guidance is provided on when to use this tool versus alternatives or when not to use it. The tool is implied for CFOs, but there is no explicit context or exclusion criteria.

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

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

No annotations are provided, so the description carries full burden. It discloses the output format (JSON) and content (spend bands, category ranges, outlier flags), but does not mention authentication, rate limits, or error handling. It implies read-only behavior but lacks explicit safety guarantees.

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 the action and result. Every sentence adds information without redundancy. Highly efficient.

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

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

Even without an output schema, the description adequately describes the return structure (JSON with spend bands, ranges, outlier flags) and gives an example. It covers the main use case but could be more explicit about output fields.

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 100% of parameters with descriptions. The description adds value by explaining that parameters drive the output (e.g., 'by employee_count' and example industries healthcare vs manufacturing), providing context beyond the schema.

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

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

The description clearly states the tool returns JSON industry spend bands, category ranges, and outlier flags by employee count, targeting CFOs for SaaS and ops stacks. It distinguishes itself from numerous sibling tools by specifying the exact resource and use case.

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

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

The description gives clear context for when to use this tool (sizing SaaS and ops stacks, with an example of industry tier curves). It does not explicitly state when not to use or list alternatives, but the specificity implies a narrow use case.

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

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

With no annotations, the description bears full burden. It mentions 'live' data and lists derived signals, but omits details like update frequency, data freshness guarantees, or any caveats about the computed metrics (Fed target gap, anchoring 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: two sentences with no redundancy. It front-loads the source and key outputs, making it easy for an agent to parse quickly.

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 provides sufficient context on the data returned. However, it lacks details on output format, time range, or whether data is point-in-time or historical series.

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 compensates by enumerating available data categories (CPI, PCE, breakeven, components) corresponding to the enum values. This adds meaning beyond the raw enum strings, though it does not explicitly describe the parameter.

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 inflation benchmarks from FRED, listing specific metrics like CPI, core CPI, PCE, etc., and additional derived signals. It differentiates from sibling tools by focusing exclusively on inflation data from a specific source.

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 implicitly indicates use for inflation-related queries by naming specific series. It does not explicitly contrast with alternatives, but the context of sibling tools (e.g., get_fomc_rate_probability) suggests it is tailored for inflation benchmarks.

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

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

No annotations are provided, so the description bears full burden. It states the tool provides benchmarks but does not disclose behavioral traits such as read-only nature, data freshness, regional scope, or any limitations. Minimal behavioral context 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 two sentences that front-load the core purpose and target audience. No extraneous content.

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

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

The description provides source and data points but lacks details on output format, time period, or aggregation level. Without an output schema, more completeness would be helpful, but it is acceptable for a simple benchmark lookup 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 should compensate for parameter meaning. It mentions 'by line of business,' which relates to the 'line_of_business' parameter, but does not address 'company_size.' The enum values are self-explanatory, but the description adds limited value beyond the schema.

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

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

The description clearly identifies the tool as providing insurance financial performance benchmarks (combined ratio, loss ratio, expense ratio, reserve adequacy) by line of business, with source and target audience. It distinguishes itself from sibling tools which focus on other industries (e.g., get_aml_regulatory_benchmark, get_cap_rate_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 mentions the tool is for insurance CFOs, actuaries, and analysts reviewing underwriting performance, implying its use case. However, it does not explicitly state when to use versus alternatives or exclude certain scenarios.

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

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

No annotations provided. The description mentions 'citation heuristics' and 'Snowflake style defaults when empty', hinting at fallback behavior but not elaborating. Overall, moderate disclosure of behavioral traits.

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

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

The description is short but fragmented, with multiple disjointed phrases. It could be rewritten more coherently without losing 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?

With no output schema and one parameter, the description should explain output structure and behavior more fully. It mentions some output fields but is incomplete and lacks clarity.

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%, and the description does not explain the 'category' parameter. It implies the input is a category via context but adds no semantic value beyond the schema.

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

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

The description mentions output fields (growth_signal, top_brands) and targets growth investors, but uses jargon like 'Ten-platform style narrative' and fragmented phrases, making the core purpose somewhat vague.

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 or when not to use this tool versus alternatives. The context 'for growth investors' is implied but not actionable.

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

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

No annotations provided, so the description must fully disclose behavioral traits. It mentions the data source (FRED) and a signal classification, but omits details such as mutability, rate limits, data freshness, or whether the tool is read-only. The behavioral profile is incomplete.

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

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

Two concise sentences front-load the key information: the metrics list and the signal/audience. No fluff, every word adds value. Structure is optimal for quick comprehension.

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 listing many metrics, the description does not explain the output format, how results are structured, or the role of the focus parameter. Given no output schema, the agent lacks information to predict the tool's response. Sibling tool comparisons are not addressed. Incomplete 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 schema has one parameter (focus) with enum values but no descriptions. Schema description coverage is 0%, yet the description does not explain what each focus value returns or how it filters the metrics. The description adds no meaning beyond the enum choices, failing to compensate 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 provides live labor market benchmarks from FRED, listing specific metrics like unemployment, JOLTS, wage growth. It targets macro agents and portfolio managers, making the purpose very specific and distinguishable from siblings like get_inflation_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?

No explicit guidance on when to use this tool versus alternatives. The description implies use for macro analysis but does not mention when-not-to-use or compare with sibling tools that cover related domains (e.g., inflation, consumer sentiment).

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

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

With no annotations, the description carries full burden. It discloses the live FRED feed dependency and conditional return of empty fields. However, it lacks details on error handling, rate limits, or performance characteristics.

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 short (two sentences plus a fragment) but the final 'Rates and inflation panel' adds little value. Information could be better structured, but it avoids verbosity.

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

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

Given only one parameter, no output schema, and many siblings, the description should clarify possible signal_type values and the structure of returned JSON. It fails to do so, leaving major gaps 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 single parameter signal_type has 0% schema description coverage and no explanation in the description. The agent has no idea what values (e.g., 'fed_funds', 'cpi') are valid, making the tool largely unusable without external knowledge.

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 lists specific data types (Fed funds, Treasury yields, CPI/PCE, employment) and targets macro analysts, providing a clear verb+resource. However, it does not differentiate from sibling tools like get_inflation_benchmark or get_yield_curve_benchmark, which may overlap.

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 the API key dependency and fallback behavior, indicating a condition for use. But it offers no guidance on when to choose this tool over similar siblings, leaving the agent without exclusion criteria.

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

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

Annotations already indicate read-only and non-destructive. Description adds content but no behavioral details (e.g., frequency, real-time or snapshot, size limits). Acceptable given annotations.

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

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

Two concise sentences plus example and audience. No redundant information, well-structured.

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

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

Complete for a no-parameter, no-output-schema tool. Description fully explains return content with example.

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 correctly omits param details. Baseline 4 for 0-param tool.

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

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

Description clearly states it returns current macro trading playbook with specific components (regime label, themes, risk triggers, opportunities). Example and audience further clarify purpose. Distinguishes 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?

Implied usage for macro trading context and targets traders/PMs/allocators, but no explicit when-to-use or when-not-to-use compared to siblings like get_macro_market_signal or get_fomc_rate_probability.

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

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

No annotations provided, so description must fully convey behavioral traits. It mentions data source (Damodaran dataset and public aggregates) but lacks details on recency, update frequency, output format, or any potential limitations. Key behavioral aspects are missing.

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 the tool's output, second adds context (source, users). No redundant text, front-loaded with purpose.

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

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

For a simple 2-parameter tool with no output schema, the description covers purpose, source, and users. However, it does not describe the output structure or behavior when the optional parameter is omitted, leaving some ambiguity.

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

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

Schema has 0% description coverage, so description must compensate. It mentions the two dimensions (industry and deal size) but does not list enum values or explain what each tier means. It adds value by specifying the metrics returned, which goes beyond parameter names.

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 M&A transaction multiples (EV/EBITDA, EV/Revenue, control premiums) by industry and deal size. It distinguishes from sibling benchmarks by specifying the exact metrics and dataset source.

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 target users (corp dev, PE, M&A advisors, CFOs) but does not provide explicit guidance on when to use this tool vs alternatives, nor does it specify when not to use it or mention sibling tools.

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