Stratalize Crypto and DeFi Intelligence

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the description adds value by specifying the scope (public mode only) and what it returns (stages). However, it does not mention any additional behavioral traits like rate limits or response format beyond the scope.

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 highly concise, consisting of two clear sentences. It front-loads the key information (the stages and public mode) and follows with guidance on alternatives. Every sentence earns its place.

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

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

For a tool with no parameters and no output schema, the description covers the essential aspects: what it returns and how to get org-scoped data. It is complete enough for an agent to select and invoke correctly, though a brief explanation of what the stages represent might add value.

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?

There are zero parameters, and the baseline is 4. The description adds context by explaining that the tool returns framework stages and when to use the public vs org-scoped mode, which goes beyond the empty schema. It effectively compensates for the lack of 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 what the tool does: it returns FS AI RMF Adoption Stage references (INITIAL, MINIMAL, EVOLVING, EMBEDDED) and specifies that public mode only returns framework stages. It distinguishes from siblings by explicitly mentioning that org-scoped data requires connecting to org MCP or the dashboard questionnaire.

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

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

The description provides explicit when-to-use and when-not-to-use guidance. It says to use this tool for public framework stages, and for org-scoped classification, control counts, and remediation priorities, one should connect org MCP or the dashboard questionnaire. This clearly delineates 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 provided, so description must disclose behaviors. It mentions 'AI citations with narrative fallbacks' but lacks details on idempotency, rate limits, costs, or what happens with sparse citations.

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 and a phrase, no wasted words, but could be more structured (e.g., bullet points for output fields).

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

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

Lists output fields but does not explain their semantics (e.g., scale of consensus_score) or 'narrative fallbacks' in detail. Without output schema or annotations, more context is needed for a data-fetching 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%. The description only mentions 'topic' with an example, and 'category' is not explained. Baseline requires compensation due to low coverage, which is not provided.

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 (consensus_score, sentiment_mix, key_themes, platform_breakdown) for analysts researching a business topic or vendor, distinguishing it from sibling tools like 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?

No explicit guidance on when to use this tool vs alternatives among 50+ siblings. The example 'cost optimization' gives a hint but no when-not or alternative recommendations.

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 of behavioral disclosure. It does not mention whether the tool is read-only, requires authentication, has rate limits, or how data is sourced or updated. This omission leaves agents without critical context for safe invocation.

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 well-structured sentences. The first sentence efficiently lists the data types, and the second specifies the intended users. No extraneous 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?

While the description clarifies the tool's domain and audience, it lacks details on output format, parameter usage, and behavioral traits. Given the absence of annotations and output schema, the description should compensate more, especially for a tool with two enum parameters.

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

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

The input schema has two enum parameters (focus, institution_type) with 0% schema description coverage. The description does not explain these parameters or their allowed values, so it adds no meaning beyond the schema itself—a missed opportunity given the domain-specific nature of the enums.

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 'AML regulatory benchmarks' and enumerates specific data types (FinCEN SAR filing rates, OFAC SDN counts, etc.). It also identifies the target audience (compliance agents, financial institution risk officers), making the purpose explicit and distinct from sibling benchmarks focusing on other domains.

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

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

The description implies usage for AML compliance but does not explicitly state when to use this tool versus alternatives or mention any prerequisites or exclusions. Among many sibling benchmarks (e.g., get_esg_benchmark, get_credit_union_benchmark), usage guidance is vague.

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 the data source and output fields but does not disclose behavioral traits like authentication, rate limits, immutability, or parameter optionality. Minimal 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 short and front-loaded, but the example could be integrated more concisely. No unnecessary sentences, though slightly redundant with the tagline.

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 annotations or output schema, the description lacks completeness. It does not explain parameter usage, optionality, or valid values, making invocation ambiguous.

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 descriptions for state and specialty. The description mentions specialty in the example but does not explain valid values for either parameter, leaving an agent with insufficient 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 with cost_per_case_median and revenue-mix percentages for ASC administrators by specialty, with an example for orthopedics. It is specific and distinguishes from numerous sibling tools focused 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 outpatient surgery cost benchmarking but does not explicitly state when not to use or provide alternatives among the many sibling tools. The context is clear but lacks exclusion 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 only describes the output content without mentioning any side effects, authorization, rate limits, or constraints. This lack of behavioral disclosure reduces 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?

Two sentences: first defines the tool's output, second gives use case. No fluff, front-loaded, and 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?

The description lacks details on output format (e.g., whether it returns a range, table, etc.) and does not explain the optional 'industry' parameter. Given no output schema, more completeness is needed for a fully informed 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 33% schema coverage, the description adds context by linking revenue and auditor tier to the output, but it does not explain the 'industry' parameter or the enum values. The schema has partial descriptions, and the description partially compensates but not fully.

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 audit fee benchmarks, specifying total fees and percentage of revenue by revenue band and auditor tier. It distinguishes itself from sibling benchmarks by targeting auditor RFP and fee negotiations, fulfilling the 'specific verb+resource' criterion.

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: 'Used by CFOs and audit committees in auditor RFPs and fee negotiations.' This clarifies when to use the tool, but it does not explicitly mention alternatives or exclusions, which would strengthen the 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 should fully disclose behavioral traits. It mentions the data source (FDIC) and output format (JSON), but does not discuss rate limits, authentication requirements, error handling (e.g., missing bank), or whether the operation is read-only. The description is insufficient for a tool without annotations.

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

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

The description is concise, consisting of two sentences and a short phrase. It is front-loaded with the main action and result. However, it could be slightly more structured (e.g., bullet points for returned fields). Overall efficient with no wasted words.

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

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

Given the lack of output schema and annotations, the description should thoroughly explain what is returned. It lists general categories (assets, deposits, etc.) but does not specify the exact fields, data structure, or how 'versus peers' is computed. For a tool with one parameter, this is incomplete for an agent to fully understand the output.

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

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

The input schema has one parameter (bank_name) with 100% description coverage via an example list. The description adds context by specifying 'by bank_name' and giving an example of a $200B assets institution, but does not provide additional constraints or format details beyond what the schema already offers. Baseline score of 3 is appropriate.

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

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

Description clearly states the tool returns JSON with specific financial metrics (assets, deposits, capital ratios, loan quality) sourced from FDIC, for bank credit analysts querying by bank_name. It includes an example and distinguishes the tool as a bank balance-sheet benchmark among many similar get_ 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 obtaining bank financial intelligence from FDIC, but does not provide explicit guidance on when to use this tool versus alternatives like get_bank_regulatory_benchmark or get_public_company_financials. No exclusions or context 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 the full burden of disclosing behavioral traits. It does not mention side effects, authentication requirements, rate limits, or whether the data is static or real-time. The description only describes what the tool returns, not how it behaves operationally, which 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?

The description is extremely concise: two sentences that front-load the key information (metrics and grouping) followed by source and audience. Every word adds value without redundancy. It is well-structured for quick scanning by an AI agent.

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

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

Given the tool's moderate complexity (2 parameters, all enums, no output schema), the description provides a good list of example metrics, grouping method, and source. However, it does not describe the output format (e.g., whether results are per tier, time period, or table structure). Slightly incomplete but still adequate for a benchmark tool.

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

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

The input schema has 2 parameters (asset_size_tier required, bank_type optional) with 0% schema description coverage. The description only mentions 'by asset size tier', which partially explains one parameter, but completely omits the optional bank_type parameter and its purpose. The description adds minimal semantic value beyond the schema, leaving the agent to guess the role of bank_type.

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 what the tool does: provides bank regulatory capital and financial performance benchmarks (CET1, Tier 1 leverage, NIM, etc.) grouped by asset size tier. It distinguishes itself from siblings by specifying the domain (bank regulatory/performance) and data source (FDIC call report). The target audience is also indicated, adding clarity.

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 context by specifying target users (bank CFOs, risk officers, analysts), but it does not explicitly state when to use this tool versus alternatives like get_credit_union_benchmark or get_aml_regulatory_benchmark. There is no guidance on when not to use it or exclusions, so the agent must infer from the name and description.

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's output format (JSON), its static composite nature (not claim-level), and its purpose as a risk screen. This provides sufficient behavioral context for an AI agent to understand it is a read-only, aggregate-level tool, though it lacks rate limits or authentication details.

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 no redundancy. It front-loads the key outputs and then clarifies limitations and purpose. 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?

The description covers the tool's purpose and limitations but lacks details on parameters, return structure beyond high-level items, and required inputs. Given no output schema and minimal annotation, the description is adequate for basic understanding but leaves 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?

Schema coverage is low (33%), and the description does not add meaning to the parameters. The parameters 'specialty' and 'annual_claim_volume' are not mentioned in the description, leaving the agent without guidance on their format or usage. The description fails to compensate for the schema's gaps.

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

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

The description clearly states what the tool returns: 'JSON E/M mix benchmarks, upcoding risk heuristics, OIG priority audit themes, RAC watchlist, checklist for HIM and compliance leads.' It uses a specific verb ('Returns') and resource, and distinguishes itself from sibling tools by specifying a focused domain (billing coding risk) and its nature (static composite model).

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 revenue integrity risk screening and explicitly notes it is 'not claim-level coding,' guiding agents away from using it for detailed audits. However, it does not compare explicitly to alternatives or provide when-not-to-use scenarios, though the context is clear for a niche 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?

Some behavioral info is given (default value heuristics), but no annotations exist, and the description omits read-only nature, auth needs, 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 sentences, front-loaded with core purpose, no unnecessary words. Highly concise.

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 one parameter and no output schema or annotations, the description lacks critical details for reliable tool invocation, especially parameter semantics and full behavior.

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 elaborate on the brand_name parameter, leaving its format or constraints unclear.

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 momentum data for marketers, specifying the fields. However, it does not differentiate from many similar 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?

No guidance on when to use this tool versus alternatives. The description hints at default behavior with sparse history but lacks explicit 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?

No annotations are provided, so the description must convey behavioral traits. It states the result is 'JSON' and 'static', but does not disclose whether it's read-only, requires authentication, has rate limits, or any side effects. The description leaves significant behavioral 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 a single concise sentence that front-loads the core purpose and output. Every piece of information is relevant and no filler is present.

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

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

The description lists key output metrics (CAC payback ranges, LTV:CAC guardrails, channel efficiency notes) but does not detail the JSON structure, specify if results are paginated, or indicate if the data is real-time vs. cached. For a tool with no output schema and no annotations, more contextual completeness 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?

Input schema coverage is 67% with industry and avg_contract_value_usd described in the schema. The description adds 'with optional avg_contract_value_usd' and mentions filtering by industry/gtm_motion, but does not enhance semantics beyond the schema for parameters lacking descriptions (gtm_motion). Baseline 3 is appropriate.

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

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

The description clearly states the tool returns JSON CAC payback ranges, LTV:CAC ratios, and channel efficiency notes, specifying industry and GTM motion. It names the granular target audience (SaaS CMOs and CFOs) and the optional parameter, making the tool's purpose distinct among 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?

Target audience is mentioned (SaaS CMOs/CFOs), but there is no explicit guidance on when to prefer this tool over siblings like get_saas_metrics_benchmark or get_saas_market_intelligence. The description implies usage for CAC-specific benchmarking but does not provide exclusions 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. It mentions data sources and surveys but omits key details like read-only nature, data freshness, or authentication requirements. The description is insufficient for safety and side effects.

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

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

The description is two sentences with no fluff. The first sentence conveys the core functionality and dimensions; the second adds users and use case. Information is front-loaded and efficient.

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

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

The description covers core functionality, sources, and audience, but lacking output schema details and behavioral disclosures. For a tool with three enum parameters, it is adequate but has clear gaps in 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 parameter descriptions are absent (0% coverage). The description mentions the three dimensions but does not explain parameter meanings or constraints (e.g., what 'gateway' tier means). It adds marginal value over the enum 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 the tool provides commercial real estate cap rate benchmarks by asset class, market tier, and geography. It specifies data sources (CBRE, JLL) and target users, differentiating it from sibling tools like get_cre_debt_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 identifies target users and use cases but does not explicitly state when to prefer this tool over siblings or provide exclusions. Usage context is implied but not fully structured.

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 partially covers behavior: returns JSON, ranking by mention_count, up to 15 results, and mentions a fallback composite. However, it lacks details on side effects, permissions, or limitations of the fallback.

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, concise and front-loaded with key information. No extraneous words, but could clarify the fallback concept in fewer words without sacrificing 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?

Covers return format, ranking, limits, and source, but omits edge cases, what the composite fallback entails, and error handling. Adequate for a simple tool but incomplete for full agent autonomy.

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 'category' has zero schema documentation. The description does not provide examples, allowed values, or format hints, leaving the agent to guess valid inputs despite low schema coverage.

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

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

The description clearly states it returns JSON leaders ranked by mention_count for a given category, making it distinct from sibling tools that are benchmarks. The verb 'returns' and resource 'leaders' are specific, though the mention of 'brand strategists' narrows the scope.

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 usage for category AI leader queries, but does not mention when not to use or compare to siblings like get_category_disruption_signal.

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 cover behavioral traits. It mentions the return type and an example value, but lacks details on side effects, error handling, data freshness, or performance. The description does not disclose potential risks or limitations beyond being 'static'.

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 covering purpose, example output, and nature. Every sentence adds information without redundancy, achieving maximum conciseness for the content provided.

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 key points: return format, example value, and caveat. It lacks details on valid categories and potential errors, but given the low complexity, it is mostly 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 input schema has one parameter 'category' with no description (0% coverage). The description gives context that the tool uses citation-volume heuristics and is for product strategists, implying the category is industry or product related. However, it does not explain the expected format, examples, or valid 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 returns JSON with disruption risk score (0-1) and evidence strings, targets product strategists, and uses citation-volume heuristics. It distinguishes itself from siblings by specifying the output and clarifying it's not equity research.

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 product strategists and that it provides a static AI disruption radar, not equity research. This implies usage context but does not explicitly compare with alternative tools, such as other 'get_*' signals.

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 return format (JSON with median, p25, p75, sample_size), model type (industry composite static), and example values. No annotations present, so description carries full burden but lacks details on error handling, rate limits, or parameter constraints beyond schema.

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-load core purpose and output details, includes example values. Efficient but could be slightly tighter without losing clarity.

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 or annotations, description adequately covers output format, sample values, and model nature. Lacks error handling or optional parameter behavior, but sufficient for a simple benchmark 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?

Description adds context that 'category' and 'company_size' are key, and provides example numbers implying the impact of company_size. However, schema coverage is only 33% (industry parameter undescribed), and description doesn't fully compensate for missing parameter details or possible 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?

Description clearly states it returns JSON with median_monthly_spend, p25/p75, and sample_size for finance teams benchmarking a software category by company_size. Specifies it's an industry composite static model, differentiating from org-specific spend 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 states use case: finance teams benchmarking software categories by company size. Clarifies it's a static industry model not org-specific, giving implicit guidance on when to use vs. alternatives, though no direct exclusion or alternative names.

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

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

No annotations exist. The description mentions synced complaint volume and issue themes but lacks details on data recency, authentication requirements, rate limits, or whether it's read-only. The disclaimer about legal advice is a caveat, not behavioral.

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 with front-loaded action. Each sentence adds value, though the second and third could be merged. No unnecessary information.

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

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

Given no output schema and no annotations, the description lacks detail on return structure and parameter behavior. It is incomplete for an agent to correctly invoke the tool without additional context.

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

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

Only two parameters exist and schema description coverage is 0%. The description notes company_name is required and product is optional but provides no further semantics, valid values, or impact on results.

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

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

The description clearly states the tool returns CFPB consumer complaint rollup JSON for risk teams, specifying key parameters (company_name, product). It is specific and distinct from siblings due to the 'CFPB' focus, though it could explicitly differentiate from similar 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. The description includes a disclaimer ('not legal advice') but does not specify use cases or exclusions.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not mention rate limits, data freshness, authentication needs, or any side effects. The description only states what data is returned, not how the tool behaves or any constraints.

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

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

The description is a single sentence of about 25 words, front-loading the core purpose 'Live TVL by blockchain'. It is free of unnecessary words and efficiently communicates the tool's scope and key outputs.

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 high-level output (chains and metrics) but lacks context on parameters and data sources. Given the complexity of a tool that aggregates TVL from many chains, and that there is no output schema, the description should provide more detail on what 'rankings' and 'change' mean, and how to use the parameters effectively.

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 input schema has no descriptions for its two parameters (limit, sort_by). The description does not explain these parameters or how they affect results. It adds no meaning beyond what the schema provides, which is minimal. The listing of metrics in the description does not directly relate to 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 states the tool provides 'Live TVL by blockchain' and lists specific chains and metrics (rankings, 1D/7D change, protocol counts, Ethereum dominance, Base vs ETH TVL). It distinguishes from siblings by focusing on blockchain TVL, which is unique among the many 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 explicit when-to-use or when-not-to-use instructions. The description implies it is for retrieving current TVL data across multiple chains, but does not mention alternatives or exclusions. Among siblings, it is clearly the tool for blockchain TVL benchmarks, but guidance is only implied.

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

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

With no annotations, the description must describe behavior. It states it provides benchmarks and cites data sources, implying a read-only operation, but does not explicitly confirm no side effects, discuss rate limits, or disclosure any mutability.

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-loaded with the core purpose. However, the listing of risks within a sentence is slightly dense; a structured format (e.g., bullet points) could improve readability without adding length.

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 should explain what the tool returns (e.g., a single score, a table, percentiles). It only mentions 'benchmarks' and sources, leaving the output format ambiguous. Parameter details are also missing, making the tool's capabilities unclear.

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 explain parameters. It lists risk subcategories (flood, hurricane, etc.) but does not map them to the enum values (physical, transition, all) or explain the region and property_type parameters. This leaves agents guessing how to use the parameters effectively.

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

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

The description clearly states the tool provides climate financial risk benchmarks, listing specific risks (physical and transition) and sources. It distinguishes itself from generic benchmarks by focusing on climate risk and lender implications, but does not explicitly differentiate from siblings like get_esg_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 implies use by ESG and risk agents, but provides no explicit guidance on when to use this tool versus alternatives (e.g., other benchmark tools). No when-not-to-use or prerequisities are mentioned.

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 cover behavioral traits. It states the tool returns JSON and requires no login (safe read operation). But it does not disclose valid ranges for bed_size/state, response format details, rate limits, or error behavior. For a data retrieval tool with zero annotations, this is insufficient. Score 2: adds some context but missing key behavioral details beyond what is minimally inferred.

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

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

Two sentences, front-loaded with core purpose and examples. The first sentence is dense but conveys multiple dimensions; the second is brief. No filler words. Could be slightly more concise (e.g., remove 'CMS HCRIS-style facility benchmark pack' redundancy). Score 4: efficient and well-structured, minor 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 has 3 parameters, no output schema, and no annotations, the description covers the main purpose, key parameters, and gives indicative output fields (IT, labor, supply chain, cost per adjusted patient day). It lacks explanation of the optional hospital_type parameter and could detail output structure more. Still, it provides enough context for an AI to select and invoke the tool for typical use cases. Score 4: mostly complete, small 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% (no descriptions in input schema). The description mentions 'bed_size' and 'state' as filters and gives an example ('acute facility cost curve'), adding meaning beyond the schema's type-only definitions. However, the third parameter 'hospital_type' is not explained. The description partially compensates for low schema coverage. Score 3: baseline for low coverage, partially helpful.

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 JSON CMS cost report benchmarks for hospital CFOs/ops, specifying key dimensions (IT, labor, supply chain, cost per adjusted patient day) and filters (bed_size, state). It differentiates from generic benchmarks but does not explicitly distinguish from sibling tools like get_ehr_cost_per_bed or get_hospital_supply_chain_benchmark. A 4 is appropriate: specific verb+resource+scope, but lacks sibling differentiation.

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

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

The description implies use cases ('for hospital CFOs and ops') and mentions 'No login' as a convenience. However, it does not state when to avoid this tool or suggest alternatives (e.g., more specific hospital tools). With over 100 sibling tools, explicit usage guidance would help. Score 3: clear context but no exclusions 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 carry the full burden. It states the tool returns 'aggregate JSON' and lists filter parameters, but fails to disclose important behavioral traits such as authentication requirements, rate limits, pagination, or data freshness. For a read-only tool, this is insufficient.

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 no filler. It front-loads the main action ('Returns CMS Open Payments aggregate JSON') and adds clarifying context in two short sentences. Every sentence contributes value.

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

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

Given the tool has 3 parameters, no output schema, and no annotations, the description should provide enough context for correct invocation. It explains the basic purpose and parameters, but lacks critical details: required fields (recipient_name is required per schema but not highlighted), return format structure, authentication, and data scope. This leaves significant 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?

With 0% schema description coverage, the description should add semantic meaning beyond parameter names. It lists recipient_name, program_year, and manufacturer_name but provides no additional context (e.g., formats, constraints, or examples). The required status of recipient_name is not indicated, potentially causing confusion.

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

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

The description clearly states it returns CMS Open Payments aggregate JSON with filtering options, and specifies it is 'Sunshine Act payment rollups – not individual attestations,' making the purpose clear. However, it does not explicitly differentiate from sibling tools like get_cms_facility_benchmark, which weakens distinctiveness.

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 compliance teams' but provides no guidance on when to use this tool vs. alternatives, and does not state prerequisites or cases where the tool should not be used. There is no mention of contrast with 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?

The description discloses that the data is static and not live, which is helpful. However, with no annotations provided, it lacks details on authentication requirements, rate limits, or potential side effects, leaving significant behavioral 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 very concise, consisting of two short sentences that convey the core functionality and nature of the output. No unnecessary information is included.

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 an output schema, the description should detail the structure of the returned JSON. It only lists content categories without describing format or nesting. Additionally, parameter usage is entirely omitted, making the tool under-documented for an agent to invoke correctly.

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

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

The input schema has three optional parameters (state, hospital_name, current_star_rating) with zero description coverage. The tool description does not explain these parameters or how they affect the output, offering no additional meaning beyond the schema fields.

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

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

The description clearly states that the tool returns CMS Hospital Star methodology notes, domain weights, benchmarks, and an improvement checklist. It distinguishes itself from sibling benchmark tools by specifying it is a static composite, not live patient-specific data, and labeling it a free education pack.

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

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

The description provides no explicit guidance on when to use this tool versus other benchmarks, nor does it mention prerequisites or limitations. While it implies educational use, it fails to clarify appropriate contexts 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 carries the full burden. It discloses the data source (FRED), update frequency (daily), and hints at derived signals ('inflation pressure signal'). There is no contradiction, and the tool appears to be a read-only operation with no destructive behavior, adequately described.

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 long, front-loading the core offering (commodity list) and then adding frequency, source, and audience. Every sentence adds value without redundancy.

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

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

Given the simple schema (one optional parameter, no output schema), the description covers the essentials: what data is provided, source, update frequency, and intended users. It could elaborate on output format or state that the category parameter filters the list, but it is largely complete for this tool's complexity.

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

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

The input schema has one parameter (category) with enumerated values ('energy','metals','agriculture','all'), but no schema description. The tool description lists commodities that map to these categories, providing partial context. However, the parameter is not explicitly explained, leaving some ambiguity for agents.

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 live commodity price benchmarks for specific commodities (WTI crude, natural gas, gold, copper, wheat, soybeans). It distinguishes itself from dozens of sibling benchmark tools by listing the exact commodities covered, making its purpose immediately clear.

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 ('traders and macro analysts') and provides context on data frequency and source. While it doesn't explicitly say when not to use or name alternatives, the listing of specific commodities implicitly guides usage for commodity price analysis and inflation signals.

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 mentions 'Synced public filing rollups' but fails to disclose behavioral traits such as data freshness, rate limits, or that it is a read-only operation. The description does not contradict annotations (none present) but is insufficient.

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, fitting into two sentences with additional context. It front-loads key information about what the tool returns and its usage, but could be more structured by separating parameter 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 4 parameters, no output schema, and no annotations, the description lacks important context like output structure, data source update frequency, or any prerequisites. It mentions 'JSON aggregates' but does not specify the exact fields or how to 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?

The schema has 0% description coverage, and the description only lists parameter names without explaining formats or constraints (e.g., state codes, fiscal_year format). It adds minimal semantic value beyond stating that these are filters for the disclosure data.

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 DOL LCA and H-1B wage disclosure aggregates for comp analysts, with company_name required and optional filters. It identifies the specific resource (company salary disclosure) and target audience, but doesn't explicitly differentiate from the sibling tool 'get_employer_h1b_wages'.

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 when needing wage disclosure data from DOL filings with company_name and optional parameters. However, it lacks explicit guidance on when not to use this tool or alternatives, especially given siblings like 'get_employer_h1b_wages' and 'get_salary_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 must disclose behavioral traits. It mentions potential fallback to 'Microsoft or Google style rows' and a 'switching narrative signal' but does not explain these terms, data freshness, authentication needs, or response behavior. This lack of clarity fails to inform the agent about side effects or limitations.

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 long. The first sentence is functional. The second and third sentences ('May fall back... Switching narrative signal.') are vague and potentially unnecessary, adding confusion without clarity. It could be more concise by focusing on the core function.

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

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

No output schema is provided, so the description should detail the return format. It mentions 'JSON top_replacing_vendors with mention counts and evidence_snippets' but does not specify the structure, field types, or pagination. For a tool with one required parameter and no other context, this level of detail is insufficient for correct agent 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 input schema has 0% description coverage, leaving the only parameter (vendor_name) unexplained. The description refers to it as 'target_vendor' but does not provide formatting rules, examples, or allowed values. With only one parameter, the description should compensate, but it adds minimal 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 states it 'Returns JSON top_replacing_vendors with mention counts and evidence_snippets for competitive intel teams tracking rip-and-replace of target_vendor.' This is specific about the verb and resource. However, it lacks differentiation from siblings like get_vendor_alternatives or get_vendor_risk_signal, and the extra sentences about fallback and switching signal are unclear.

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 when-to-use or when-not-to-use guidance is provided. It mentions the target audience ('competitive intel teams') but does not compare with alternatives or specify prerequisites, leaving the agent without clear selection criteria among many similar 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?

With no annotations, the description carries full burden. It explains what data is returned (benchmarks, ratios, standards, signals) and sources, but does not disclose behavioral traits such as whether historical data is available, what happens for missing data, or any rate limits. For a read-only tool, the basics are covered but depth is 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?

The description is three efficient sentences: first states outputs, second lists sources, third states audience. No wasted words, front-loaded with key 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?

Given three parameters and no output schema, the description explains what type of data is returned but not the format or structure of the response (e.g., single value vs. ranges). For a benchmark tool, additional detail on output shape would improve completeness. It is adequate but not thorough.

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

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

Schema description coverage is 0%, so the description must compensate. It maps building_type and region to 'by building type and region' but does not mention the construction_class parameter at all. Other terms like soft cost ratios are not parameters, leaving the third parameter unexplained.

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 cost per SF, soft cost ratios, contingency standards, and material cost escalation signals. It names specific sources and target audience, and the domain (construction) differentiates it from numerous 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 implies usage for construction cost queries but does not explicitly state when to use this tool versus alternatives like other benchmark tools. No exclusion criteria or alternative suggestions are provided.

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

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

No annotations provided, so description bears full burden. It mentions 'Live' but does not disclose read-only nature, update frequency, or any other behavioral traits. Minimal insight into tool 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?

Two sentences, no fluff. Front-loaded with key information: live benchmarks, data sources, and purpose. 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 one simple parameter and no output schema, the description adequately explains the tool's purpose and data. It could clarify if results are current snapshot or have history, but overall 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 has one enum parameter 'focus' with four values. Description lists data sources (sentiment, spending, saving) which map to enum values, but does not explicitly link them. With 0% schema coverage, the description partially compensates by hinting at what each focus might return.

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 live consumer sentiment benchmarks from FRED and lists specific indicators. It differentiates from many sibling 'get_*_benchmark' tools by focusing on consumer-related metrics, and even suggests its use for GDP and equity 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?

Implies usage for consumer sentiment analysis but does not explicitly state when to use this tool versus alternatives. No exclusions or when-not scenarios are provided.

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

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

No annotations are provided, so the description carries the full burden. It discloses data sources (S&P Capital IQ, Damodaran) and that it is used by CFOs/treasurers, but does not explicitly mention read-only nature, permissions, rate limits, or data freshness. The purpose is clearly a lookup, so the omission is acceptable but not exemplary.

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

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

Two sentences: first sentence states core purpose with specific metrics, second gives use cases and data sources. No filler or redundancy. 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 no output schema, the description reasonably covers what the tool returns: benchmarks by industry and credit rating tier, with specific metrics listed. It could be more explicit about output format (e.g., single values or ranges), but the level of detail is sufficient for an agent to understand the tool's purpose and inputs.

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. The description mentions filtering by 'credit rating tier and industry', which maps to the two parameters, but does not list the enum values or require industry. This provides a marginal improvement over the schema 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 it provides corporate leverage and debt benchmarks (Net Debt/EBITDA, interest coverage, debt maturity profiles) and specifies the dimensions of credit rating tier and industry. This distinguishes it from sibling tools like get_cre_debt_benchmark or get_real_estate_debt_stress_benchmark, which focus on other debt 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 mentions use cases ('refinancing, covenant setting, and credit rating management') and industry context, but does not explicitly state when to use this tool vs. alternatives. The context signals and sibling list show many related tools, but no direct exclusion 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?

No annotations exist, so the description carries full burden. It discloses data sources (MBA CREF, Trepp) and the nature of outputs (benchmarks), but lacks details on whether data is current, any authentication needs, or return format. Some transparency 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?

Description is two sentences, front-loads key information (metrics, inputs), and avoids fluff. Every phrase adds value, 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?

Given no output schema or annotations, the description should cover expected returns and constraints. It names output metrics (DSCR, LTV, spread ranges) and sources, but does not specify units, time periods, or response structure. Adequate but with notable gaps.

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

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

Schema coverage is 0%, so description must compensate. It explains that parameters filter by property type and lender type, listing example lender types. However, it does not enumerate all enum values or define each parameter's role beyond the filtering context. Adds moderate value over raw 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 identifies specific outputs (DSCR, LTV, spread ranges) and inputs (property type, lender type), distinguishing it from sibling tools like get_cap_rate_benchmark. It also states the intended audience, clarifying the tool's domain.

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 user (CRE CFOs, capital markets teams) and provides context (structuring financings). However, it does not explicitly state when to use this tool versus alternatives or exclude cases where it should not be used.

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 full burden. It discloses data source (FRED ICE BofA), update frequency (daily), and nature ('Live'). But it does not explicitly state that the operation is read-only or describe potential side effects, latency, or authentication requirements. 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 three sentences: first defines purpose and outputs, second notes update frequency, third states audience. Every sentence adds value, no redundancy. 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 one optional enum parameter and no output schema, the description is fairly complete: it lists metrics provided, source, update cadence, and target users. Missing explicit description of return format or behavior when no data is available, but these are minor given the tool's simplicity.

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

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

Schema description coverage is 0%, so description must compensate. It mentions 'OAS by rating tier', which maps to the 'rating_tier' parameter, and 'investment grade and high yield' hint at enum values 'ig' and 'hy'. However, it does not describe all enum options ('all', 'bbb') or specify the parameter usage explicitly.

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 investment grade and high yield credit spread benchmarks' from specific indices (FRED ICE BofA), listing exact metrics (OAS, TED spread, 2s10s, distress signal). It distinguishes from siblings (e.g., get_cap_rate_benchmark) by specifying credit spread domain.

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 target users ('For credit analysts and fixed income PMs') and update frequency ('Updates daily'), implying a monitoring use case. However, it does not explicitly state when to use this tool vs alternatives (e.g., get_yield_curve_benchmark) or 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?

No annotations exist, so the description must disclose behavioral traits. It only states the data source and metrics, omitting read-only status, latency, rate limits, or authentication requirements. This leaves the agent guessing about invocation constraints.

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-efficiency-focused sentences, front-loading the core functionality and adding context. No wasted words.

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

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

For a simple tool with two enum parameters and no output schema, the description covers domain and purpose but lacks behavioral context and parameter details. It is adequate but not rich.

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%, but the parameters are simple enums with clear labels. The description hints at asset_size_tier ('by asset size') but does not mention charter_type. The schema provides enough meaning, so description adds marginal 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 identifies the tool's purpose: returning credit union financial benchmarks (capital ratios, net interest margin, etc.) by asset size. It specifies the data source (NCUA) and target audience (CFOs), distinguishing it from sibling bank 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 states the intended use ('for credit union CFOs preparing for NCUA exams and board reporting'), providing clear context. However, it does not explicitly state when not to use or mention alternative tools like get_bank_regulatory_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, the description must fully disclose behavior. It lists outputs but omits details like default period, data freshness, side effects, or authentication needs. The inconsistency between '30-day' and the period parameter further reduces 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 very concise—three sentences with no fluff. The core functionality is front-loaded in the first sentence, and each sentence adds value (outputs, source, use case).

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

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

Given the tool has one parameter and no output schema, the description provides some context (source, use case, output components) but lacks critical details like default behavior, return format, or data recency. It's adequate for a simple tool 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 single parameter 'period' is not described in the description, and schema coverage is 0%. The description's claim of '30-day rolling' contradicts the parameter's enum options, adding confusion rather than clarity.

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 '30-day rolling correlation matrix for BTC, ETH, and SOL' with specific outputs like Pearson pairs and beta to BTC. However, the mention of '30-day' conflicts with the input schema allowing periods of 7d, 30d, and 90d, causing slight ambiguity.

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

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

The description says 'For crypto portfolio agents,' implying use case, but provides no guidance on when to use this tool versus alternatives or when not to use it. No sibling differentiation or context on limitations.

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 mentions the data source (DeepDAO public data) but does not specify read-only nature, side effects, authentication needs, rate limits, or response behavior when parameters 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 concise with two sentences, front-loading the purpose and providing concrete example values. Every sentence adds value without 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 two optional parameters and no output schema, the description provides an overview of output content but lacks details on default sorting, filtering behavior, response format, and number of results. It covers the high-level content but leaves gaps in usage and output expectations.

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 'top DAOs by treasury size, stablecoin percentage' which maps to the sort_by enum values, but fails to explain the min_treasury_usd parameter. The addition of example median values adds some context but does not fully clarify parameter usage.

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

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

The description clearly states the tool provides DAO treasury benchmarks, listing specific metrics (treasury size, stablecoin percentage, runway, governance token concentration) and giving median example values. It distinguishes the tool from siblings by focusing on DAO-specific 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?

No explicit when-to-use or when-not-to-use guidance is provided. With many sibling benchmark tools, the description does not help the agent decide between this and similar tools like get_chain_tvl_benchmark or get_defi_yield_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 covers critical behavioral aspects: data source (DeFiLlama Yields), output contents (APY bands, TVL, chain, pool ID), filter options, default scope (stablecoin-marked pools), and security (Ed25519-signed). It lacks mention of rate limits or data freshness, but overall disclosure is strong.

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

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

The description is concise (three sentences) and front-loaded: first sentence states core function, second details optional filters, third adds important context (default, free, signed). Each sentence contributes useful information without redundancy.

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

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

The description adequately explains data content and filtering, but lacks details on the output format (e.g., expected JSON structure) since there is no output schema. It also does not mention update frequency or data latency. These gaps reduce completeness for an agent needing to process 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?

Input schema has 100% coverage with parameter descriptions. The description adds valuable context by explaining that filters are substring matches and specifying default behavior when no filters are applied (stablecoin pools). This goes beyond mere repetition of the schema.

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

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

The description clearly states the tool retrieves DeFi lending and stable yield benchmarks from DeFiLlama Yields, specifying data includes APY percentiles, TVL, chain, and pool ID. It accurately describes the verb and resource. However, it does not differentiate from a sibling tool 'get_stablecoin_yield_benchmark', which may overlap in 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 no guidance on when to use this tool over alternatives like 'get_stablecoin_yield_benchmark'. It mentions optional filters and default behavior but does not address trade-offs or exclusions.

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

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

No annotations provided, so description carries full burden. Discloses output metrics and sources, but does not specify data freshness, derivation method, or potential limitations (e.g., geographic scope, estimate vs. exact). 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: first lists outputs, second states use case and sources. No filler words, all sentences add value. 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?

No output schema, but description lists metrics. Lacks output format details (e.g., percentages, ranges). For a simple 2-param tool, it is mostly complete, but could specify return format.

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 description mentions 'by product type', explaining the product_type parameter. However, market_tier parameter is not described anywhere. Adds some meaning but leaves one parameter unexplained.

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 development pro forma benchmarks with specific metrics (yield on cost, profit-on-cost, construction-to-perm spread, return hurdles) and sources (NAHB, ULI, industry composite). Distinguishes from siblings like get_construction_cost_benchmark by focusing on pro forma analysis.

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

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

Explicitly says 'For developers underwriting new projects and lenders sizing construction loans', giving clear context when to use. Does not explicitly state when not to use, but the use case is well-defined.

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 responsibility for behavioral traits. It does not mention whether the tool is read-only, requires authentication, has rate limits, or any side effects. The description only describes the output content, leaving the agent uninformed about operational 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 three sentences, each adding value: tool purpose, data source/model, and target audience. No redundancy, well-structured, 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?

With two parameters, no output schema, and no annotations, the description provides a good overview of what the tool does and its sources. However, it lacks details about the output format or how the optional parameter affects results. For a moderate-complexity tool, it is mostly complete but could benefit from a brief note on output structure.

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 add meaning. The description mentions 'revenue recognition risk' which relates to one parameter, but does not explicitly map parameters to their effects. Parameter names are self-explanatory (sector, revenue_recognition_model), but the description could better clarify how revenue_recognition_model influences the benchmark output.

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

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

The description clearly states the tool provides earnings quality and financial statement risk benchmarks, listing specific metrics (accruals ratio, cash conversion, revenue recognition risk) sourced from SEC EDGAR and Sloan accruals model. It distinguishes itself from many sibling benchmark tools by specifying the financial reporting risk domain and target users (CFOs, auditors, analysts).

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 includes target users and use cases ('assessing financial reporting risk before M&A or investment'), giving clear context for when to use the tool. However, it does not explicitly state when not to use or compare to sibling tools, which is not a major omission given the distinct focus.

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 states returned fields and optional calculation but does not disclose whether the tool is read-only, idempotent, or requires authentication. For a benchmark tool, read-only is likely but not 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-loading key output and an example. No wasted words, but could be slightly more structured.

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

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

Given three parameters, no output schema, and no annotations, the description is adequate for a simple lookup but lacks details on vendor_name, error conditions, and data sources. The example provides helpful context.

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

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

Schema coverage is 0% (no individual parameter descriptions in schema). Description partially compensates by explaining bed_count and annual_cost's role in gap calculation, but fails to document the required vendor_name parameter fully.

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 benchmark_cost_per_bed_median and optionally gap_vs_benchmark for health IT finance. It distinguishes from siblings by focusing on EHR cost per bed, a niche not covered by other get_* benchmark tools.

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

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

Description implies use for EHR cost benchmarking and explains when bed_count and annual_cost are needed for gap calculation. However, it lacks explicit guidance on when to use this versus similar healthcare benchmarks like 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?

No annotations exist, so description carries full burden. Discloses key dependency, non-realtime nature, and strip dependency. However, lacks details on what guidance per resolver means, potential rate limits, or authentication specifics beyond key presence.

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-loaded with purpose. Every sentence adds value with no fluff. 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 zero-parameter tool with no output schema and no annotations, the description explains key behavioral conditions (key dependency, non-realtime, strip dependency). However, 'energy price strip dependency' is unexplained, leaving some ambiguity. Otherwise adequate for basic 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?

No parameters in schema (100% coverage by default). With zero parameters, baseline is 4. Description adds no parameter details but not needed. No extra value but meets baseline expectation.

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

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

Clearly states it returns EIA spot-price style JSON for commodity strategists, identifying the resource and audience. Distinguishes from siblings (many benchmarks) by specifying EIA energy data, though 'spot-price style JSON' is 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?

Explicitly notes preconditions (server EIA API key configured) and fallback behavior (empty/guidance). Warns it is not realtime and mentions dependency on 'energy price strip,' guiding appropriate use. No explicit alternative named but implies uniqueness for EIA data.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so no contradiction. The description adds value by detailing what the tool returns (positions, targets, etc.) but does not disclose any further 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?

Two sentences with no wasted words. Front-loaded with the purpose, includes a concrete example, and efficiently conveys scope.

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 (one parameter, no output schema), the description covers scope, example output, and audience. The example adds context for how results are structured.

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

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

Schema description coverage is 100% (asset parameter described as 'Asset symbol or "all" (default all)'). The tool description does not add extra meaning beyond the schema, so baseline score applies.

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

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

The description clearly states the tool provides Elliott Wave analysis (position, targets, invalidation levels, confidence) for specific assets (BTC, SPY, TLT, Gold). It distinguishes from siblings as none of the many other tools mention Elliott Waves or technical analysis.

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 intended users ('traders and portfolio managers tracking technical market structure'), providing clear context. However, it does not explicitly state when not to use 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?

No annotations are provided, so the description carries the full burden. It discloses that the tool returns JSON data from DOL LCA filings and mentions example distributions, but does not detail any limitations, error handling, or stability beyond the basic 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?

The description consists of two concise sentences. The first states the purpose and target audience, the second provides an example and a tagline. No redundant information; 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 the tool's simplicity (one parameter, no output schema) and the richness of the description covering return type, data source, and intended users, the description is largely complete. However, the lack of explicit listing of return fields or pagination info is a minor gap for an agent without prior knowledge.

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

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

Schema description coverage is 100% with a single parameter 'employer_name' described by examples. The description adds context that the parameter is used for querying DOL LCA filings, but does not provide additional constraints or format beyond what the schema already offers. Baseline 3 is appropriate.

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

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

The description clearly states it returns 'prevailing wage stats, certified job titles, state mix' for H-1B compensation intelligence, which differentiates it from siblings like 'get_salary_benchmark' or 'get_labor_market_benchmark'. The verb 'returns' and resource 'JSON...stats' are 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 implies usage for HR analytics and talent strategy teams querying DOL LCA filings by employer_name, but does not explicitly state when not to use this tool or 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. It states the tool retrieves benchmarks and lists specific metrics and sources, indicating a read-only data query. However, it does not disclose behavioral traits such as pagination, rate limits, or output format.

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

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

The description is concise (two sentences plus target audience), front-loaded with the core purpose, and efficiently lists key metrics and sources. It avoids unnecessary words while conveying 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?

Given the tool's simplicity (two enum parameters, no output schema), the description provides sufficient context on what data is returned (a set of ESG metrics) and sources. However, it lacks details on output format, time range, or how scores are structured, which would aid an agent in using the tool effectively.

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

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

Schema description coverage is 0%. The description lists metrics that align with the focus enum (carbon, social, governance) but does not explicitly map to the focus parameter or describe the sector parameter. It adds partial meaning but not full 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?

The description clearly states the tool provides ESG benchmarks by sector, listing specific metrics (carbon intensity, net zero commitments, SBTi alignment, etc.) and data sources. It differentiates from sibling tools like get_aml_regulatory_benchmark or get_asset_sbii_score by its ESG focus.

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

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

The description identifies target users ('sustainability agents and ESG analysts') and lists data sources, implying credibility. However, it does not explicitly state when to use this tool over alternatives like get_climate_risk_benchmark, nor does it provide when-not 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. The description mentions 'non-org-specific' which hints at scope, but does not disclose behavioral traits like read-only, auth requirements, or rate limits. The unexplained term 'composite mode' adds confusion.

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 one sentence, which is concise, but it is laden with jargon ('composite mode', 'control library context') that reduces clarity. The structure is adequate but not optimized for quick understanding.

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

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

Given no output schema and only one parameter with no description, the description should provide more complete context about return values and behavior. It fails to do so, leaving significant 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 'nistFunction' has an enum but no description in schema (0% coverage). The description does not explain this parameter or its role, leaving its meaning entirely 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 'EU AI Act framework reference coverage in composite mode with control library context and implementation guidance (non-org-specific)' suggests the tool provides coverage information about the EU AI Act, but terms like 'composite mode' and 'control library context' are vague. The verb 'get' implies retrieval, but the output format is unclear.

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 on when to use this tool vs alternatives. Among many sibling get_* tools, this is the only EU AI Act specific one, but there is no explicit context for usage or exclusions.

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

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

No annotations are provided, so the description must cover behavioral traits. It mentions returning JSON but does not disclose data freshness, pagination, error handling, authentication needs, or what happens if the vendor is not found. The term 'synced' is vague regarding update frequency.

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

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

The description is two sentences with no redundant phrasing. The first sentence conveys the core functionality and parameters, and the second provides a brief summary. It is appropriately sized and front-loaded.

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

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

For a tool with 5 parameters, no output schema, and no annotations, the description provides a basic understanding but lacks details on return value structure, error responses, and data source specifics. It is adequate for a simple query but incomplete for complex scenarios.

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 lists the parameters and highlights that vendor_name is required while others are optional. However, it does not explain naics_code or fiscal_year format, and the schema only describes the state parameter. This adds some value but insufficiently compensates for the 20% schema description coverage.

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

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

The description clearly states that the tool returns USASpending-synced obligation rollups in JSON format for government affairs teams. It specifies the required parameter (vendor_name) and optional filters (agency_name, naics_code, fiscal_year), making the purpose distinct from sibling tools like 'get_vendor_contract_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 federal contract spend intelligence but provides no explicit guidance on when to use this tool versus alternatives. Given many similar sibling tools (e.g., 'get_vendor_contract_intelligence'), the description lacks differentiation or when-not-to-use advice.

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

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

No annotations provided, so description carries full burden. Discloses that output is 'illustrative', conditioned on latest FRED fed funds print, and intended for educational purposes. Does not detail update frequency or accuracy, but for a simple probability tool this is adequate.

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

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

Single sentence conveys all essential elements: output type, content, audience, data source, and exclusion. No wasted words, front-loaded with the core function. Ideal conciseness for a zero-parameter tool.

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

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

With no parameters and no output schema, the description fully covers what the tool does, for whom, and under what constraints (data source, illustrative nature). Nothing more is needed for an agent to decide whether and how to invoke this tool.

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

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

No parameters exist, so schema coverage is 100% by default. Description adds no param info because none is needed. Achieving the baseline of 4 is trivial; a 5 is justified as no additional meaning could be added beyond the empty schema.

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

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

Clearly states it returns 'cut, hike, hold probabilities for next three meetings', explicitly distinguishes from 'futures-implied market odds', and identifies target audience (macro educators). The verb 'Returns' combined with specific resource and scope makes purpose unmistakable.

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

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

States intended use 'for macro educators' and 'scenario planning narrative only', and clarifies what it is NOT ('not futures-implied market odds'). Lacks explicit alternatives among sibling tools, but the negative guidance is strong enough to prevent misuse.

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 convey behavioral traits. It mentions the data source (FRED) and update frequency (daily), which is helpful, but does not disclose other traits like rate limits, authentication requirements, or that the tool is read-only (implied but not stated).

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 the exact data content, and the second provides source and update frequency. It is front-loaded with the most important information and contains no unnecessary words.

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

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

Given that the tool has one optional parameter, no output schema, and a simple purpose, the description provides adequate context: it details the specific rates and indices returned, the source, and update frequency. It could be improved by describing the output format or what the benchmark values represent, but it is largely sufficient.

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

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

The input schema has one optional parameter (base_currency with enum) and schema description coverage is 0%, so the description must compensate. The description mentions USD, EUR, GBP in the list of pairs but does not explicitly explain that the parameter filters by base currency. The parameter's purpose is somewhat inferable but not fully clarified.

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 lists the currency pairs and indices provided (e.g., USD/EUR, DXY broad TWI) and states the source (FRED) and update frequency. This clearly distinguishes the tool as a dedicated FX rate benchmark 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 focuses on what the tool provides but does not explicitly state when to use it over alternatives or when not to use it. While the tool's purpose is clear from its name and content, no guidance is given for exclusion or context-specific 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?

With no annotations, the description must disclose behavioral traits. It mentions live data, source (public chain RPCs), and no API key needed. However, it omits potential rate limits, caching, or output structure details. Sufficient for a simple read-only tool but not fully 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 three sentences, front-loaded with the main purpose, followed by specific return details and source. Every sentence adds value with no redundancy or wasted words.

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

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

Given the low complexity (one optional parameter, no output schema), the description is fairly complete. It explains the tool's output categories and source. However, it lacks details on output format or data types, which would be beneficial 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?

The input schema has one optional enum parameter (chain) with values ethereum, base, solana, all. The description lists the chains but does not explicitly mention the parameter name or the 'all' option. Schema coverage is 0%, so the description partially compensates but could be more explicit.

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 gas price benchmarks for Ethereum, Base, and Solana, and lists specific data returned (Gwei, USD cost, congestion category, x402 context). It distinguishes itself from numerous sibling benchmark tools by focusing on gas prices.

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 obtaining gas benchmarks but does not explicitly state when to use or not use this tool, nor does it mention alternatives. The 'Zero API key required' note provides some context, but guidelines are not direct.

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 live data via public API and rate limits. With no annotations, description carries transparency burden. Lacks details on authentication, caching, error behavior, or whether stats are real-time. Adequate but 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?

Two sentences, no filler. Front-loaded with core function and constraints. Efficiently 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 1-param tool with no output schema, description mentions JSON output and rate limits but omits what specific stats are returned, any limits on query scope, or whether private repos are included. Partially 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 0% – no description for 'org_or_company'. Description says 'footprint query' suggesting it's an org/company name, but no format, case sensitivity, or examples. Fails to fully compensate for missing schema documentation.

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

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

Clearly states the tool returns live GitHub organization and repository stats as JSON for developer relations teams. Specific verb ('Returns'), resource ('GitHub org and repo stats'), and audience ('dev rel teams'). Distinguished from siblings which focus on other domains.

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

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

Implies use for developer relations and open-source ecosystem snapshots, but no explicit guidance on when to use or not use this tool versus alternatives. No comparison to sibling tools or exclusion criteria 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; description does not disclose behavioral traits like idempotency, side effects, or authentication requirements. Implies read-only by nature but not explicitly stated.

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

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

Two sentences, front-loaded with purpose, no filler. Every word contributes 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?

Covers the data returned (indices and metrics) but omits output structure and parameter usage. For a simple tool, it is moderately complete but lacks details needed for correct invocation.

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

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

Schema has 0% description coverage; the description fails to explain the 'region' parameter, how it filters indices, or how to use it. No parameter information is provided 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?

Description specifies the tool returns benchmark data for global equity indices, listing specific indices (e.g., S&P 500, Nasdaq) and metrics (YTD returns, P/E ratios, risk signal). It clearly distinguishes from sibling tools focused 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?

No guidance on when to use this tool vs alternatives. Does not mention prerequisites, context, or exclusions. The description implicitly targets global equity analysis but lacks explicit usage direction.

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 that the data is 'Static HFMA plus CMS-style composite' and 'not your invoices', clarifying it is a model, not real-time. However, it does not address other behavioral traits like read-only nature or rate limits.

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

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

The description is brief (two sentences) but the first sentence is a run-on that mixes output fields, typical values, and source context. Could be structured more clearly with bullet points for 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 no annotations or output schema, the description covers the output format partially but fails to document the only input parameter (category). For a simple one-parameter tool, this leaves a significant gap in usability.

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 a single undocumented 'category' parameter (type string, optional) with 0% schema coverage. The description provides no explanation of what 'category' means or acceptable values, leaving the agent without 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 specifies the verb 'Returns' and resource 'GPO contract benchmark' with concrete example values (typical_gpo_savings_pct ~18%), distinguishing from siblings by mentioning 'Acute care GPO savings model' and 'not your invoices'.

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; only implicit context that it's a static model for acute care, not actual invoices. Does not mention alternative tools or exclusions.

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

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

No annotations provided. The description mentions it may use AI citations or Epic/Meditech-style defaults, hinting at fallback behavior, but does not disclose whether it is read-only, requires authentication, or has rate limits. The behavioral transparency is 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?

Two sentences with front-loaded information about return fields and audience. No redundancy or fluff, though could be slightly clearer about what 'category' entails.

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 one parameter with no description, no output schema, and no annotations, the description should fully cover parameter and output behavior. It partially describes output but omits parameter format, default behavior, and error cases. Incomplete for an AI agent to confidently 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?

Input schema has one required 'category' string parameter with 0% coverage. The description does not explain what valid category values are or provide examples, despite mentioning healthcare IT contexts. The description adds little 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 JSON with specific fields (top_recommended_vendors, ai_consensus blurb, sample_size) for health IT strategists. It identifies the tool as a healthcare vendor narrative scan and category recommendation audit, distinguishing it from generic 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 on when to use this tool versus alternatives like get_top_vendors_by_category or get_category_ai_leaders. The description mentions target audience but fails to provide context for 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 provided, so the description carries full burden. It states the tool returns structured JSON data but does not disclose side effects, authentication needs, rate limits, or behavior on missing vendors. The mention of data sources ('CMS plus Stratalize industry composite') is helpful but insufficient for full 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 three sentences; the first does a good job stating purpose, but the second sentence ('Example maintenance $/bed style hints embedded.') is vague and arguably unnecessary. The third sentence adds data source context. Overall, it could be more direct and remove unclear phrasing.

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 output schema and annotations, the description is the only source for operational context. It lacks explanation of output structure, error handling, pagination, or response format. For a tool with two parameters, more detail is needed to ensure correct invocation and interpretation.

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 description must compensate. It partially explains the 'category' parameter by listing example values (EHR, staffing, etc.), but the required 'vendor_name' parameter is not described at all. No guidance on format (e.g., full name vs. ID) or constraints. This leaves the agent underinformed.

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 market-rate payload for healthcare vendor categories, listing specific examples (EHR, staffing, food, waste, med-surg). This distinguishes it from the sibling 'get_vendor_market_rate' (likely general) and other benchmark tools. However, the phrase 'Example maintenance $/bed style hints embedded' is somewhat confusing and doesn't add clarity.

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 vs alternatives. The description lists categories but does not indicate when this specific healthcare-focused tool is preferred over the many other benchmark tools (e.g., get_hospital_supply_chain_benchmark, get_pharmacy_spend_benchmark). No exclusions or prerequisites mentioned.

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 notes that data is from synced public data and returns JSON, implying a read-only operation. However, it does not clarify authorization requirements, rate limits, error handling (e.g., if hospital_name not found), or the variability of fields. The mention of example fields when present is a minor positive, but overall transparency is 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?

The description is two sentences, front-loading the main purpose and key metrics. It is concise without excessive detail. The phrase 'Quality transparency' at the end is somewhat redundant but not harmful. It could be slightly more structured, but overall it is 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?

The tool has a simple schema with one parameter and no output schema. The description lists example fields but does not fully specify the return structure or format. For a quality transparency tool, more details on how to interpret scores might be expected, but given the simplicity, it is minimally adequate.

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

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

Schema coverage is 100% for the single parameter hospital_name, which is described as 'Hospital or facility name'. The description adds that results are 'by hospital_name' but does not provide additional semantic context beyond the schema. The phrase 'from synced public data' adds some context, but not specific to the parameter. Baseline is 3, and no further value is added.

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 Compare quality scores, specifying metrics like safety, readmissions, patient experience, and star rating. It identifies the target audience as clinical ops leaders. However, it does not explicitly differentiate from sibling tool get_cms_star_rating, which may return a subset of these scores, so clarity is high but not perfect.

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_cms_star_rating or get_cms_facility_benchmark. It mentions the audience but does not outline specific decision criteria or exclusions, making it difficult for an agent to select appropriately 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 carries full burden. It discloses the source (CMS HCRIS), a median fallback (~17%), and the output type. However, it does not mention authorization requirements, rate limits, data freshness, or any potential side effects. As a read-only tool, this is adequate but not exceptional.

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?

One sentence that packs key details: output format, metric, percentiles, audience, source, and fallback. It is front-loaded with the essential purpose. While very concise, it does not waste words.

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

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

The description covers the main output (percentiles and percentile band) and source. Given no output schema, it explains what the tool returns. However, it lacks details on the exact JSON structure, whether it includes additional fields, or how the percentile band is calculated. It is adequate but not fully complete.

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

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

Schema description coverage is 0%, so the description must compensate. It mentions filtering by bed_size and state, but does not specify valid values or formats (e.g., two-letter state codes, bed_size range). The description adds context beyond the bare schema but is insufficient for precise invocation.

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 supply cost percentiles (p25/p50/p75) of operating expense for hospital CFOs, sourced from CMS HCRIS. It specifies the metric and audience, but does not explicitly differentiate from sibling tools like get_ehr_cost_per_bed 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?

No guidance on when to use or when to avoid this tool. It does not mention alternatives or prerequisites. The agent must infer from the name and description that it is for hospital supply chain benchmarking.

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 convey behavioral traits. It mentions the data is 'live' and sourced from 'FRED and Census', but lacks details on data update frequency, lag, rate limits, or response format. For a data retrieval tool, this is a significant gap in 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 three concise sentences: first defines the tool, second adds value, third identifies users. It is front-loaded and every sentence serves a purpose. 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 (2 enum parameters, no output schema), but the description does not specify the response structure or how the indicators (starts, permits, etc.) are returned. Given the lack of an output schema, additional detail on the return format 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?

Schema description coverage is 0%, and the description does not explain the two parameters (region and structure_type) or their allowed enum values. The phrase 'by market tier' is vague and does not map clearly to the parameters. This forces the agent to infer parameter usage from the schema 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 the tool provides 'live housing supply indicators — starts, permits, completions, and absorption by market tier from FRED and Census.' It specifies the data types and sources, and calls out its function as a leading indicator for housing prices, which distinguishes it from other real estate 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 identifies the target audience: 'developers, lenders, investors, and housing policy analysts', which implies when to use the tool. However, it does not explicitly say when not to use it or list alternative tools for different needs, leaving room for ambiguity.

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

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

No annotations are provided, so the description carries the full burden. It states the data source (HUD annual FMR dataset) and that it is free, but lacks details on data freshness, update frequency, rate limits, or the response format. The description provides basic behavioral context but leaves important 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 three sentences long, front-loads the main function, and provides use cases, source, and cost without any redundant or extraneous 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 tool with no output schema and only two parameters, the description covers purpose, use cases, source, and cost. However, it lacks essential details about the output format, data year, and refresh frequency, making it only partially complete for the agent to anticipate the result.

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 50% (only metro_area has an example). The tool description mentions metro area and bedroom count but does not add meaning beyond the schema. The bedroom count parameter constraints (min 0, max 4) are in the schema but not elaborated in the description. Overall, the description does not significantly enhance parameter understanding.

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

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

The description clearly states the function: retrieving HUD Fair Market Rents by metro area and bedroom count. It lists specific use cases (affordable housing underwriting, Section 8 compliance, etc.) and distinguishes the tool from siblings, none of which relate to housing or HUD data.

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

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

The description provides clear context on when to use the tool (affordable housing tasks) but does not explicitly mention when not to use it or alternatives. Given the unique domain among siblings, the guidance is effective.

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

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

The description discloses that the data is static and curated, not live. However, no annotations exist, and it lacks details on data freshness, update frequency, 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, front-loaded with purpose. Slightly verbose but 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?

Lacks output schema. Mentions GDP and inflation but not structure of JSON. Adequate for simple snapshot but could be 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?

No parameters exist in the schema, so baseline is 4. The description adds no param info since none needed.

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

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

The description clearly states it returns static macro composites (GDP and inflation) in JSON format, and distinguishes from live API pulls. It is specific and differentiates from many 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 implies it is for static academic briefing and not for live data, but does not explicitly mention when to use this over siblings like 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?

The description discloses it returns static (not transactional) data, which adds transparency beyond the schema. However, since annotations are absent, the description carries the full burden and does not cover idempotency, error behavior, or authentication requirements.

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 compact but somewhat run-on and lacks clear structure. It front-loads key information but could be broken into clearer sentences or bullet points.

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

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

No output schema exists, and the description only partially explains the return format (median_total_spend_monthly, category_breakdown). Parameter semantics are weak, and overall completeness is insufficient for an agent to confidently use the tool.

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

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

Schema description coverage is 0%, and the description adds minimal parameter meaning. It mentions 'industry' indirectly via examples (productivity suite, CRM) but does not explain the 'company_size' parameter or provide format constraints.

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

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

The description clearly states it returns JSON with median_total_spend_monthly and category_breakdown for CFOs, and distinguishes itself as a static Stratalize industry composite, not transactional GL data. However, it does not explicitly differentiate from sibling tools like get_industry_spend_profile or get_category_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?

No guidance on when to use this tool versus alternatives. The description mentions 'for CFOs' but does not specify context or exclusions. No explicit when-to-use or when-not-to-use information.

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 states the tool 'Returns JSON' but does not mention read-only status, authentication requirements, rate limits, or error handling. The data source is hinted ('Stratalize tier-1 public composite'), but overall behavioral transparency is 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 long, each serving a distinct purpose: defining output and audience, providing an example, and summarizing. No extraneous 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?

The tool has two parameters, full schema coverage, no output schema, and no annotations. The description explains the output structure in general terms but lacks details on response format (e.g., how bands are structured), data recency, or behavior on invalid inputs. Adequate but not thorough.

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

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

Schema description coverage is 100%, providing basic parameter meanings. The description adds value by specifying that 'employee_count' is used for 'banding' and that the output includes 'spend bands, category ranges, and outlier flags', enriching the agent's understanding beyond the schema 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 the tool returns 'industry spend bands, category ranges, and outlier flags' for CFOs 'sizing SaaS and ops stacks', with an example of 'healthcare vs manufacturing tier curves'. It distinguishes itself from the sibling 'get_industry_spend_benchmark' by focusing on profile data for specific verticals by company size.

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 target audience (CFOs) and use case (sizing SaaS and ops stacks) are implied, but there is no explicit guidance on when to use this tool versus alternatives like 'get_industry_spend_benchmark' or 'get_category_spend_benchmark'. No when-not-to-use conditions are stated.

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 behavior. It says 'Live' implying real-time, but lacks details on data freshness, rate limits, or required permissions. No mention of output format.

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

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

Two sentences front-load the core purpose and list included metrics. No redundant 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?

No output schema, so description should clarify return format. It mentions 'Fed target gap, anchoring signal, policy implication' but not the structure (e.g., numeric values, dates). Adequate for a simple tool but could be more specific.

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 parameter 'measure' lacks description (0% coverage). The description lists metrics but does not explicitly map them to enum values. For example, it doesn't state 'cpi returns CPI data'. Partial compensation.

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 inflation benchmarks from FRED, listing specific metrics (CPI, core CPI, PCE, etc.). It differentiates from siblings (e.g., get_fomc_rate_probability) by focusing on inflation 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?

No guidance on when to use this tool vs alternatives. The description does not mention when not to use it or what complementary tools exist.

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. Description discloses source (NAIC) and metrics returned, but does not mention update frequency, limitations, or side effects. 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 concise, front-loaded sentences. No redundant information. Efficiently conveys purpose, content, and audience.

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

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

Given no output schema and two parameters, description covers what is returned and source. Lacks details on default behavior, error handling, or time range. Still reasonably complete.

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

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

Schema coverage 0%, so description must compensate. Mentions 'by line of business' describing the required parameter, but does not explain the optional company_size parameter or its effect. Partially helpful.

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 insurance financial performance benchmarks (combined ratio, loss ratio, etc.) by line of business, with source and target audience. Distinguishes from siblings which are for other domains.

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

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

Explicitly mentions target users (CFOs, actuaries, analysts) and use case (reviewing underwriting performance). Lacks explicit when-not-to-use or alternatives, but context is strong.

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

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

With no annotations, the description carries full burden for behavioral disclosure. It mentions citation heuristics, Snowflake defaults, and a narrative style, but these are vague and cryptic ('Ten-platform style narrative'). It does not clarify whether the operation is read-only, what happens on empty input, or any side effects, making it insufficiently 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, achieving reasonable conciseness. However, it uses opaque jargon ('Snowflake style defaults', 'Ten-platform style narrative') that may hinder clarity, reducing effectiveness despite its brevity.

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 output schema and low parameter documentation, the description should cover return structure, input format, and tool behavior. It fails to define 'growth_signal', 'top_brands', or 'evidence lines', and lacks any explanation of the category parameter. The context of sibling tools in the same category is not leveraged for differentiation.

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

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

The input schema has one parameter 'category' with 0% description coverage, and the description does not explain valid values, format, or constraints. It only vaguely implies a software category, leaving the agent with no meaningful guidance on how to populate 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 that the tool returns JSON with growth_signal, top_brands, and evidence lines for growth investors. It mentions 'VC software category heat check,' which gives context and distinguishes it from broader category tools. However, it does not explicitly differentiate from similar sibling tools like 'get_category_disruption_signal'.

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 growth investors in VC software categories but provides no explicit guidance on when to use this tool versus alternatives. There are no when-not-to-use instructions or comparisons to sibling tools, leaving the agent to infer the appropriate 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 carries full burden. It lists the metrics but does not disclose data freshness, update frequency, rate limits, or any behavioral aspects beyond the data content. 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 two sentences, front-loaded with the key metrics and a clear signal, with no extraneous information. 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, the description lists the metrics but does not describe the output format (e.g., latest values, time series) or how the 'focus' parameter affects the output. It is sufficient for a basic understanding but incomplete 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 schema has one parameter 'focus' with enums, but 0% schema description coverage. The description mentions various metrics that map to the focus values (e.g., 'unemployment' for employment, 'wage growth' for wages) but does not explicitly link the parameter to these metrics, leaving some interpretation needed.

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

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

The description clearly specifies that the tool provides live labor market benchmarks from FRED with specific metrics (unemployment, U-6, JOLTS, etc.) and a tight/balanced/loosening signal, which distinguishes it from the many sibling tools focused on other domains like healthcare or real estate.

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 states the tool is for 'macro agents and portfolio managers' needing labor market signals, implying its use case. However, it does not explicitly mention when not to use it or compare with siblings like get_inflation_benchmark or get_consumer_sentiment_benchmark, leaving some ambiguity among the many macro 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 exist; description adds FRED dependency and empty snapshot fallback, but omits behavioral details like rate limits, authentication specifics, or response structure beyond 'JSON blocks'.

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

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

Two efficient sentences, front-loaded with key action, no wasted words.

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

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

Lacks output schema; description does not fully specify return structure or parameter options, leaving significant gaps given the tool's single parameter and complexity.

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

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

Schema has 0% coverage for 'signal_type'; description lists data types but does not explain how the parameter selects them or what valid values are.

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 returns macro data (Fed funds, Treasury yields, CPI, PCE, employment) from FRED, differentiating from many benchmark siblings by focusing on macro signals, though not explicitly distinguishing from similar tools 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?

Implies use by macro analysts with FRED_API_KEY, mentions fallback behavior, but lacks explicit guidance on when to use this vs alternatives.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the tool is clearly safe and read-only. Description adds content details but does not disclose data freshness, update frequency, or any behavioral constraints beyond what annotations imply.

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

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

Two sentences plus an example and audience note. All content is directly useful: first sentence defines purpose, second gives concrete example, third specifies users. No filler or 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?

Given zero parameters and no output schema, the description effectively communicates what the tool returns: regime label, positioning themes, risk triggers, opportunities, plus an illustrative example. Sufficient for agent understanding.

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

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

No parameters in input schema, so schema coverage is 100% trivially. Baseline of 4 applies. Description does not need to add parameter meaning, but also does not add any parameter-related context.

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

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

Description clearly states 'Current macro trading playbook' and lists specific components (regime label, positioning themes, risk triggers, tactical opportunities), with an example. Distinguishes from siblings like 'get_macro_market_signal' by focusing on playbook content.

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

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

Mentions target audience ('For traders, portfolio managers, and macro allocators') but no guidance on when to use versus alternatives or when not to use. No explicit context for selection among many 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?

No annotations provided, so description carries full burden. It mentions data sources (Damodaran dataset, public aggregates) but does not disclose operational details like authentication, rate limits, or whether it's read-only. As a benchmark tool, likely safe, but lacks transparency beyond data source.

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 purpose, second provides source and audience. No unnecessary words, 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?

No output schema exists. Description hints at return (multiples) but does not specify structure, format, or units. Given two simple params and no nested objects, the description is moderately complete but would benefit from stating what the output contains (e.g., average, median, percentiles).

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

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

Schema coverage 0%, but parameters (industry, deal_size_tier) have self-explanatory enum names. Description mentions 'by industry and deal size' aligning with parameters. However, no additional meaning beyond schema is provided; for a coverage of 0%, more guidance would be beneficial.

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

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

Description clearly specifies the verb 'get' and the resource 'M&A transaction multiples' including EV/EBITDA, EV/Revenue, control premiums. It distinguishes from siblings by focusing on M&A multiples and mentioning the Damodaran dataset.

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?

Describes target users (corp dev, PE, M&A advisors, CFOs) and use case (fairness opinions). Does not explicitly state when not to use or provide alternatives, but the context of many sibling benchmarks implies it's for M&A specific multiples.

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