Stratalize Finance Intelligence

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds behavioral context about public vs. org mode behavior, which is transparent and consistent with annotations.

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

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

Single, well-structured sentence conveys the essential information without redundancy. Every part is informative.

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

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

Given no parameters and no output schema, the description fully covers the tool's purpose and usage boundaries. No additional context is needed for an agent to correctly invoke it.

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 the description adds value by enumerating the possible output values (INITIAL, MINIMAL, EVOLVING, EMBEDDED), providing clarity beyond the empty schema.

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

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

The description clearly states it returns FS AI RMF adoption stages (INITIAL, MINIMAL, EVOLVING, EMBEDDED) and distinguishes between public mode and organizational scoped data, making the purpose specific and unambiguous.

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

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

Provides explicit guidance: 'public mode returns framework stages only' and advises using org MCP or dashboard for org-scoped data, clearly indicating when to use this tool 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?

With no annotations provided, the description carries the full burden. It mentions 'narrative fallbacks' and 'multi-platform belief synthesis', which are behavioral traits. However, it does not disclose potential limitations, error conditions, or whether the operation is read-only, leaving gaps 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 sentences that front-load the key purpose, provide an example, and add a summarizing tagline. Every sentence adds value without redundancy, making it highly efficient.

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

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

The description lists the output fields but does not explain their meaning or possible values. It also does not mention which platforms are synthesized or how the consensus is calculated. Given the lack of output schema and many sibling tools, more context would be beneficial for full completeness.

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

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

The input schema has two parameters (topic, category) with 0% description coverage. The description only elaborates on 'topic' via the example 'default themes cost optimization', which may relate to 'category' but is unclear. The 'category' parameter is not explained, failing to compensate for the schema's lack of descriptions.

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

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

The description clearly states it returns a JSON with specific fields (consensus_score, sentiment_mix, key_themes, platform_breakdown) for analysts researching a business topic or vendor. It distinguishes from siblings by focusing on AI consensus synthesis across platforms, as indicated by 'Multi-platform belief synthesis'.

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

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

The description does not provide any guidance on when to use this tool versus its many sibling tools, nor does it mention when not to use it or any prerequisites. It simply describes what it does without contextual 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?

With no annotations provided, the description must disclose behavioral traits. It only lists data contents, omitting read-only nature, authentication requirements, rate limits, or output format. Minimal transparency beyond data 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 a single, relatively concise sentence that front-loads the tool's purpose and lists key data types. However, it lacks structural elements like bullet points that would improve scannability.

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 what data is included but fails to explain how parameters affect output, what the response format is, or any limitations. Given the absence of an output schema and annotations, this is insufficient for proper tool 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 0% description coverage, so the description must explain parameter meanings. It does not mention the 'focus' or 'institution_type' parameters at all, leaving agents without guidance on how to select benchmarks or filter by institution 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 the tool provides AML regulatory benchmarks and enumerates specific data types (SAR filing rates, OFAC counts, enforcement fines, etc.). It identifies the target audience, but lacks an explicit verb like 'retrieve' or 'get'. It distinguishes itself from sibling benchmarks by being AML-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 does not provide guidance on when to use this tool vs. alternatives such as get_bank_regulatory_benchmark or other benchmark tools. It only specifies the target users, not usage context 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. It mentions the data source is a static table from ASCA and CMS 2024, implying it is not real-time, but does not clearly state limitations such as data staleness, rate limits, or error handling. Minimal behavioral disclosure.

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: core functionality, an illustrative example, and a contextual model statement. No wasted words, front-loaded with the most important information. Very 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?

No output schema exists, so the description should at least outline the expected return structure. It mentions 'cost_per_case_median plus revenue-mix percentages' but does not specify response keys, formatting, or how filters apply. The example gives a concrete number but omits the full structure. Adequate but with clear 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 has two string parameters (state, specialty) with 0% description coverage. The description only mentions 'by specialty' and gives an example 'orthopedics', but does not explain valid values for specialty or state. This leaves the agent without clear guidance on valid inputs, so minimal value added beyond the schema.

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

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

The description clearly states the tool returns JSON with cost_per_case_median and revenue-mix percentages for ASC administrators by specialty, based on a static ASCA + CMS 2024 table. An example with orthopedics ~$4,200 per case solidifies the purpose. This is very specific and distinguishes it from sibling benchmark tools.

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

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

The description implies usage for ASC cost benchmarking by specialty, but it does not explicitly state when to use this tool vs alternatives among the many siblings. No exclusions or when-not guidance is provided.

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

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

No annotations are provided, so the description carries the full burden. It mentions the data source (Audit Analytics public aggregate data) but does not disclose behavioral traits like rate limits, data freshness, or that it is a read-only operation. The description is minimal beyond purpose.

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 plus a usage note, all informative with no redundancy. Every sentence adds value, making it efficient 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?

Given the tool's simplicity (3 params, no output schema), the description is fairly complete, specifying the two output metrics and segmentation dimensions. It could mention revenue band ranges or data limitations, but overall is adequate for a 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?

Schema coverage is only 33% (one of three parameters described in schema). The description adds context by mentioning revenue band and auditor tier, which align with two parameters, but the industry parameter is not explained. It partially compensates but does not fully describe all 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 audit fee benchmarks, including total fees and fees as a percentage of revenue, segmented by revenue band and auditor tier. It also mentions the source and typical users, making it distinct from sibling benchmark tools.

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

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

The description indicates the tool is used by CFOs and audit committees in RFPs and fee negotiations, providing clear context. However, it does not explicitly state when to use this tool versus other benchmarks or provide 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?

With no annotations, the description must disclose behavioral traits. It states the output format (JSON) and data source (FDIC), implying read-only operation, but lacks details on rate limits, pagination, or potential costs.

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 plus a short phrase, concise and to the point. It front-loads the main function in the first sentence and adds context in the second.

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 sufficiently explains the returned data (assets, deposits, etc.), data source (FDIC), and audience. It covers the essential aspects for selection and invocation.

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

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

Schema coverage is 100% with one required parameter 'bank_name'. The description adds example values (JPMorgan, Wells Fargo), which aids understanding but does not provide additional semantics beyond the schema.

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

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

The description clearly states the tool returns JSON financial metrics (assets, deposits, capital ratios, loan quality) sourced from FDIC, specifically for bank credit analysts. It distinguishes from numerous sibling tools by focusing on bank balance-sheet benchmarking.

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

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

The description mentions the target users (bank credit analysts) and data source (FDIC), but does not provide explicit guidance on when to use this tool versus alternatives or when not to use it.

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

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

No annotations are provided, so the description bears full responsibility. It discloses the source (FDIC call report public aggregates) and specific metrics, but lacks details on update frequency, reliability, or any limitations beyond being aggregates.

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

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

Two sentences, front-loaded with key info (metrics and grouping), then source and audience. No fluff.

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

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

The description covers domain and source but does not mention output format or that it returns aggregate data. With no output schema, this gap reduces completeness for an agent.

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

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

Schema coverage is 0%, leaving parameters undocumented. The description mentions grouping by 'asset size tier' but does not explain the enum values or the bank_type parameter, relying entirely on the schema's enum names which are self-explanatory but not elaborated.

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

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

The description clearly states it provides bank regulatory capital and financial performance benchmarks (CET1, Tier 1 leverage, NIM, etc.) by asset size tier, distinguishing it from siblings like get_rwa_benchmark or get_aml_regulatory_benchmark.

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

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

The description implies usage for bank CFOs, risk officers, and analysts seeking regulatory benchmarks, but offers no explicit when-to-use or when-not-to-use guidance relative to many similar 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?

States it is a static composite model and not claim-level coding, but lacks details on data sources, update frequency, or limitations. With no annotations, the description carries full burden and 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?

Two sentences, front-loaded with key outputs and model type. No fluff.

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

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

Lists outputs and clarifies the model's scope. No output schema needed as return type is described qualitatively. For a risk screen, covers key aspects.

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 one parameter (level_4_5_percentage) has schema description; the other two are bare. Description does not explain how specialty or annual_claim_volume affect results. Low schema coverage (33%) requires compensation, which is missing.

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 E/M mix benchmarks, upcoding risk heuristics, OIG priority audit themes, and RAC watchlist. Uses specific verbs and differentiates from siblings like get_bank_financial_intelligence or get_cms_star_rating by focusing on billing coding risk.

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

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

No explicit guidance on when to use this tool vs alternatives among many sibling benchmarks. Does not provide when-not or alternative tool references.

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

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

With no annotations, the description carries the full burden. It discloses two behavioral traits: default momentum of ~1.8 for sparse history, and heuristic trend direction. However, it omits other aspects like permission requirements, data freshness, or idempotency. The disclosure is partial but valuable.

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. The first sentence lists return fields and notes the default behavior; the second gives context. Efficient but could be slightly tighter by removing redundant 'brand trajectory monitoring' if implicit.

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

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

For a single-parameter tool with no output schema and no annotations, the description touches on return structure, default behavior, and purpose. However, it lacks detail on data source, stability, or side effects, making it moderately complete but not comprehensive.

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

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

Schema description coverage is 0%, so the description must compensate. It implicitly references brand_name through 'brand trajectory' but does not explain the parameter's meaning, format, or any constraints. The agent must infer that brand_name is the input; no added value beyond the schema.

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

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

The description clearly states it returns JSON with specific fields (momentum_4week, trend_direction, week_over_week) and identifies the use case for marketers and brand trajectory monitoring. While the verb 'Returns' implies retrieval, it is sufficiently specific. It distinguishes from siblings by focusing on brand momentum, though not explicitly naming alternatives.

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. Mentions 'for marketers' but does not provide conditions, exclusions, or comparison to other tools. The description lacks any when-to-use 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?

With no annotations, description should detail behavioral traits. It states 'Returns JSON' and 'Static Stratalize go-to-market benchmark', implying read-only and no side effects, but does not explicitly confirm safety or mention rate limits or authentication needs.

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

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

Single sentence with a brief fragment, no redundancy. Every word adds value: specifies target audience, output format, key filters, optional parameter, and data pedigree. 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 no output schema, description covers expected output categories (ranges, guardrails, notes) and inputs thoroughly. Lacks details on output structure or error conditions, but sufficient for a standard 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?

Description adds meaning beyond schema: it explains industry and gtm_motion as filters, avg_contract_value_usd as optional for LTV:CAC calculation. This compensates for the missing schema description on gtm_motion, improving 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?

Description clearly states the tool returns JSON CAC payback ranges, LTV to CAC ratio guardrails, and channel efficiency notes. It specifies filtering by industry and gtm_motion, and mentions optional avg_contract_value_usd, making its purpose unambiguous among sibling benchmark tools.

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

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

No guidance on when to use this tool versus alternatives like get_saas_metrics_benchmark. Description lacks conditions, prerequisites, or exclusions, leaving the agent to infer usage from tool name alone.

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 does not disclose any behavioral traits such as read-only nature, side effects, rate limits, data freshness, or geographical coverage. The description is purely functional and lacks transparency beyond the basic purpose.

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: the first states the tool's purpose, the second provides source and user context. No redundant information, and every sentence adds value. Well-structured for quick comprehension.

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

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

The tool has moderate complexity with 3 parameters and no output schema or annotations. The description mentions data source and target users, but lacks details on output format, update frequency (quarterly only implied), regional coverage, or any limitations. It is partially complete but misses important contextual details.

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

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

The schema has 3 parameters, all with enums but no descriptions (0% schema description coverage). The description mentions 'by asset class, market tier, and geography' which maps loosely to the parameters but does not explain individual parameter semantics like what 'retail_strip' means or whether 'region' is required. Some context is added, but insufficient for full 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 identifies the tool as providing commercial real estate cap rate benchmarks by asset class, market tier, and geography. It specifies the data source and target users, making the purpose unambiguous and distinct from sibling benchmark tools.

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

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

The description mentions typical users (CRE acquisition teams, asset managers, CFOs) and use cases (property pricing, portfolio valuation), but does not explicitly state when to use this tool versus alternatives like get_cre_debt_benchmark or get_property_operating_benchmark. Usage context is clear but lacks exclusions or direct comparisons.

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 bears the full burden of disclosing behavioral traits. It states the tool returns JSON and uses a fallback mechanism, but it does not mention idempotency, rate limits, authentication requirements, or any side effects. The phrase 'Unprompted AI leader mentions' is vague and lacks clarity.

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

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

The description consists of two sentences. The first sentence is long and includes an example that could be omitted. It is somewhat cluttered but not excessively verbose. It could be more concise while retaining 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 (one parameter, no output schema), the description provides basic return value information: a list of leaders with mention_count, up to 15. It also explains the data sources. However, it does not fully specify the output structure or other potential fields, leaving some 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 input schema has one parameter ('category') with no description, resulting in 0% schema coverage. The description adds meaning by indicating that the category is used for querying and that the output is a category-level share-of-voice leaderboard. However, it does not specify the expected format or provide examples, so the added value is moderate.

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 indicates the tool returns a ranked list of AI leaders based on mention_count, with a max of 15 results, and specifies the data source (ai_citation_results or composite fallback). It distinguishes itself from sibling tools by focusing on AI leaders for a specific use case (brand strategists). However, the inclusion of an example ('like Salesforce 42 mentions') adds unnecessary detail.

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 brand strategists' but does not provide explicit guidance on when to use this tool versus alternatives, nor does it specify when not to use it. Given the large number of sibling tools, this lack of usage context is a significant gap.

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

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

With no annotations, the description explains the output format (JSON with score and evidence), provides an example default value, and notes it is a static AI disruption radar. This gives sufficient behavioral context without contradictions.

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

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

The description is three sentences, each adding distinct value: what it returns, an example, and a clarifying disclaimer. No redundant or vague statements.

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

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

For a tool with one parameter and no output schema, the description covers the return format, audience, and nature (static, not equity research). It is complete enough to use, though a brief parameter hint would strengthen it.

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 a single 'category' parameter with no description (0% coverage). The tool description does not elaborate on what values 'category' expects, leaving the agent to infer from context. It adds minimal 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 a disruption_risk_score (0 to 1) and evidence strings, targeting product strategists. It distinguishes itself by specifying it is not equity research and ties to citation-volume heuristics.

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 product strategists and explicitly states it is not equity research, giving clear context. However, it does not name specific sibling tools for alternative purposes, leaving some 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?

With no annotations, the description carries full burden. It discloses that the model is a static industry composite, not org-specific, and provides example output values. However, it does not discuss data source, update frequency, or error conditions.

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 the main purpose with example values, then clarify the model type and provide additional percentiles. No superfluous content.

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

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

The description explains the return structure and model type but omits details about input parameters (e.g., valid values for industry, company_size) and data limitations. Given no output schema, it covers the essentials but lacks completeness for a fully informed call.

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 describes category with a basic description; industry and company_size lack descriptions. The description adds that company_size is used for grouping and gives output context (median, percentiles). This compensates for partial 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 the tool returns median monthly spend with percentiles and sample size for a software category by company size, targeting finance teams. It distinguishes the tool from many sibling benchmarks by specifying the exact resource and output fields.

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

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

The description implies the tool is for industry-level benchmarks ('not org-specific spend') but does not explicitly compare to siblings like 'get_industry_spend_benchmark' or 'get_spend_by_company_size'. Usage context is hinted but not fully specified.

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 includes a disclaimer that the data is not legal advice, but it does not mention data freshness, rate limits, authentication requirements, or idempotency. The word 'Synced' hints at periodic updates but lacks detail. Behavioral transparency 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 relatively concise with three sentences. The first sentence conveys the main action, the second adds context, and the third is somewhat redundant. It is efficient but could be streamlined further.

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

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

Given the tool's simplicity (2 parameters, no nested objects, no output schema), the description covers the basic purpose and parameters. However, it lacks details on output format, error handling, and data freshness. It is minimally complete for a straightforward data retrieval tool.

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

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

Schema description coverage is 0%, so the description must compensate. It explains that 'company_name' is required and 'product' is optional, but it does not provide details on their formats, allowed values, or examples. The description adds minimal value beyond the schema field 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 specifies what the tool does: it returns a CFPB consumer complaint rollup JSON by company name with an optional product filter. It also mentions the content (synced complaint volume and issue themes) and includes a disclaimer. The purpose is distinct from sibling tools which focus on various benchmarks and financial 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 implies the tool is for risk teams seeking consumer finance complaint data, but it does not explicitly state when to use it versus alternatives. No comparisons to sibling tools or usage limitations are provided. The guidance is implied rather than explicit.

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

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

The description adds context about data source (DeFiLlama) and output metrics, but fails to disclose behavioral aspects like rate limits, data freshness, or read-only nature. With no annotations, more detail would be beneficial.

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

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

The description is a single, well-structured sentence that conveys all key points without redundancy. It is front-loaded with the main purpose and efficiently packs 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, the description adequately outlines the return values: rankings, changes, protocol counts, dominance, and comparison. However, it could be more explicit about the response format or whether pagination applies.

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

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

Schema coverage is 0%, yet the description does not explicitly explain the 'limit' or 'sort_by' parameters. It hints at sort options via 'rankings, 1D and 7D change' but lacks direct mapping, adding minimal value beyond the schema's enums and constraints.

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

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

The description clearly states the tool provides live TVL by blockchain from DeFiLlama, listing specific chains and metrics. It distinguishes itself from sibling tools like get_defi_yield_benchmark by focusing specifically on chain-level TVL.

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 phrase 'for x402 agent context' is vague and does not provide explicit when-to-use or when-not-to-use criteria.

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

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

No annotations are provided, so the description must fully disclose behavioral traits. It mentions data sources but does not state if it is read-only, side effects, authentication needs, or error 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 concise with two sentences, but the first sentence is a bit dense. No redundant information; front-loaded with key terms.

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?

Without an output schema, the description should explain the output format or structure. It only mentions sources and types, leaving the return value 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 description coverage is 0%, but enums are self-explanatory. The description adds some context by listing risk types (physical, transition) and data sources, though it does not explicitly map parameters to their meanings.

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

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

The description clearly states the tool returns climate financial risk benchmarks covering physical and transition risks, with specific examples. It distinguishes itself from numerous sibling tools by explicitly naming the domain and sources.

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

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

No guidance on when to use this tool vs. alternatives. The only hint is 'For ESG and risk agents,' but there is no explicit differentiation or conditional 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. Mentions 'No login' and 'Returns JSON', but lacks details on response structure, pagination, rate limits, or any side effects. Minimal behavioral disclosure beyond 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?

Single sentence with key points front-loaded. Fairly concise but includes arguably redundant phrase 'Example acute facility cost curve vs peers' which adds clutter. Overall efficient.

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

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

With no output schema, no annotations, and 0% schema coverage, the description provides a reasonable overview of what the tool does and its target audience. Lacks details on output format, error handling, and parameter constraints like bed_size range or state format. Adequate for a straightforward data lookup tool but incomplete.

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

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

Schema coverage is 0%. Description clarifies that parameters include bed_size and state (matching required params) and suggests hospital_type is optional. Adds context 'by bed_size and state' and 'CMS HCRIS-style', but does not explain parameter format, valid values, or defaults. Adequate but incomplete.

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

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

Clearly states it returns JSON CMS cost report benchmarks for hospital CFOs/ops, listing specific metrics (IT, labor, supply chain, cost per adjusted patient day) and context (no login, CMS HCRIS-style). Differentiates from siblings by the specific focus on CMS cost reports, though no explicit sibling comparison.

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

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

Implied usage: use when needing hospital cost report benchmarks. Mentions 'No login'. No explicit when-not or alternatives. Sibling tools like get_hospital_supply_chain_benchmark exist but not discussed.

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 only states it returns JSON and is aggregate, but lacks details on authorization needs, rate limits, pagination, or error behavior. This is minimal for a tool with no annotations.

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

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

Three concise sentences deliver the essential information without redundancy. Every sentence adds value, clearly stating purpose, parameters, and distinguishing from individual data.

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 data retrieval tool with no output schema, the description covers core purpose and parameters but omits return structure, data scope details, and error conditions. It is minimally adequate but not 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?

With 0% schema coverage, the description adds meaning by listing parameters as filters, but does not specify formats, constraints, or whether matches are exact or partial. It provides basic context but not comprehensive 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 returns CMS Open Payments aggregate JSON for compliance teams, specifies filtering by recipient_name with optional program_year and manufacturer_name, and distinguishes from individual attestations. It effectively differentiates from the many sibling 'get_' benchmark tools.

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

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

The description notes this is for aggregate payment rollups and not individual attestations, providing some context. However, it does not explicitly state when to use this tool versus other tools or any 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?

With no annotations provided, the description bears full responsibility. It states the tool returns static data and is free, indicating it is a read-only, non-destructive operation. However, it does not disclose potential auth requirements, rate limits, or details on parameter 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 brief—three sentences that convey the core purpose, static nature, and free availability. It is front-loaded with the main action. Minimal waste, though a bit more structure could improve 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 the tool has 3 optional parameters, no output schema, and no annotations, the description is insufficient. It describes the general content of the output but does not explain how parameters influence the response, the JSON structure, or any constraints. The agent cannot reliably predict the outcome.

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 3 parameters (state, hospital_name, current_star_rating) with 0% schema description coverage, yet the description provides no explanation of how these parameters affect the output. The agent receives no guidance on whether parameters filter the data or are ignored, severely limiting correct 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 the tool returns CMS Hospital Star methodology notes, domain weights, national distribution benchmarks, and an improvement checklist. It explicitly distinguishes itself as a static CMS-style composite, not live patient-specific data, differentiating it from sibling tools.

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

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

The description hints at appropriate usage by noting it is static and not live patient-specific data, implying it is for educational or benchmarking purposes. However, it does not explicitly state when to use this tool versus alternatives like get_hospital_care_compare_quality, nor does it specify prerequisites 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?

The description discloses data source (FRED), update frequency (daily), and content scope (changes, inflation signal). However, with no annotations, it fails to mention response format, pagination, or any limitations, leaving 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 compact with three sentences: first introduces the tool and examples, second mentions changes and signal, third gives source and frequency. It is front-loaded and contains 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 one optional parameter and no output schema, the description adequately covers purpose, data content, source, and frequency. Missing output structure details are minor given the tool's straightforward nature.

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

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

The description adds meaning to the 'category' parameter by listing commodities that map to each category (energy, metals, agriculture), compensating for the 0% schema description coverage and providing context beyond the enum values.

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

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

The description clearly states the tool provides live commodity price benchmarks for specific commodities (WTI crude, natural gas, gold, copper, wheat, soybeans) with weekly/monthly price changes and inflation pressure signal, differentiating it from sibling benchmark tools.

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

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

The description implies usage for traders and macro analysts but does not explicitly state when to use this tool over alternatives like get_inflation_benchmark or get_gas_benchmark. It provides context but lacks 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 provided, so description carries full burden. It states 'Synced public filing rollups', implying read-only data retrieval, but does not disclose side effects, permissions, or update frequency. Lacks explicit safety cues.

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

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

Two concise sentences with front-loaded purpose. Every sentence adds information, though the second sentence could be more specific.

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 core purpose and parameters but omits behavioral details, return format, and usage context. 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 coverage is 0%, but description only lists parameter names (company_name, job_title, state, fiscal_year) without explaining formats, constraints, or allowed values. Adds minimal value beyond the parameter names themselves.

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 DOL LCA and H-1B wage disclosure aggregates JSON, specifies required and optional filters (company_name, job_title, state, fiscal_year), and positions it as a tool for comp analysts. This distinguishes it from siblings like get_employer_h1b_wages and get_salary_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 usage for employer-sponsored wage transparency and comp analysts, but does not explicitly state when to use or when to avoid, nor does it mention alternatives among the many sibling tools.

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

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

No annotations are provided, so the description must fully disclose behavior. It mentions the output format (JSON) and that it may fall back to 'Microsoft or Google style rows', hinting at data source variability, but the phrase 'Switching narrative signal' is cryptic and unexplained. No details on potential side effects, rate limits, or authorization needs.

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

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

The description is two sentences, but the third fragment 'Switching narrative signal' is unclear and seems out of place, reducing conciseness. The main content is front-loaded but the vague closure detracts from overall 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 the simple schema (one string parameter) and no output schema, the description should provide enough context to understand the return structure. It mentions 'top_replacing_vendors' with mention counts and evidence snippets but does not specify if the result is an array or object, nor the shape of evidence_snippets. The fallback behavior is 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?

The input schema has 0% description coverage, so the description must compensate. Although the parameter name 'vendor_name' is self-explanatory, the description only implicitly refers to 'target_vendor' without describing the parameter's format, constraints, or relationship to the output. No added meaning beyond the schema.

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

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

The description clearly states it returns a JSON with 'top_replacing_vendors' including mention counts and evidence snippets for competitive teams tracking rip-and-replace of a target vendor. The verb 'Returns' and the specific resource are clear, and the context of 'competitive displacement' distinguishes it from similar tools like 'get_vendor_alternatives'.

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

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

The description implies usage for competitive intel teams tracking rip-and-replace scenarios, but does not explicitly state when to use this tool versus alternatives like 'get_vendor_alternatives'. No 'when not to use' 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?

Without annotations, the description does not disclose behavioral traits such as read-only nature (implied but not stated), rate limits, or data freshness. It only describes what the tool provides, not how it behaves.

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 dense sentences: first states outputs, second lists sources and audience. No redundancy, front-loaded with key purpose.

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

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

Given no output schema, the description adequately informs about return types (hard costs, soft cost ratios, etc.) and sources. It lacks details on output structure or any limitations, but is sufficient 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?

With 0% schema description coverage, the description adds meaning by linking 'building type' and 'region' to outputs, but does not explicitly describe all parameters (construction_class is missing). The enum values in the schema provide some clarity, but the description could do more.

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' with specific outputs: hard cost per SF, soft cost ratios, contingency standards, and material cost escalation signals. It names authoritative sources (NAHB, Turner, RSMeans) and target audience, making it distinct from siblings like 'get_cap_rate_benchmark'.

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

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

No guidance on when to use this tool vs. alternatives. It does not mention prerequisites, exclusions, or compare to other benchmark tools (e.g., for different asset types or geographies).

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 of behavioral disclosure. It mentions live data and a consumer signal (strong/moderate/weak), but does not disclose update frequency, historical range, error handling, or how the signal is computed. Key behavioral traits are missing.

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

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

The description is two sentences long, concise, and front-loaded with the purpose. It avoids extraneous detail, but the second sentence could be clearer. Overall, it earns its 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 the complexity of aggregating multiple indicators into a composite signal and the absence of an output schema, the description should explain the return format, frequency, and data range. It mentions 'strong/moderate/weak consumer signal' but not how it is represented (e.g., text, numeric score). Key contextual information is missing.

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

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

The schema has one parameter ('focus') with enum values ('sentiment', 'spending', 'saving', 'all'), and schema description coverage is 0%. The description lists data sources (sentiment, retail sales, PCE, saving rate) that partially map to enum options, but does not explicitly explain the parameter or how each focus value affects the output. The description adds minimal value beyond 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 live consumer sentiment benchmarks from FRED, listing specific indicators (University of Michigan sentiment, Conference Board confidence, etc.) and mentions outputting a consumer signal strength. It distinguishes the tool as aggregating multiple data points into a signal, but does not explicitly differentiate from sibling tools like 'get_inflation_benchmark' or 'get_labor_market_benchmark'.

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

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

The description implies the tool is used for assessing consumer health for GDP and equity analysis, but provides no explicit guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites.

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

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

No annotations are provided; the description reveals the data source (S&P Capital IQ, Damodaran) and return metrics but does not disclose other behaviors such as rate limits, authentication, or read-only nature. It adequately describes what the tool returns but lacks additional context.

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

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

The description is concise, two sentences long, and front-loaded with the core purpose. 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 no output schema and no annotations, the description covers the purpose and data source but omits details on return format or behavioral aspects. It is minimally adequate for a simple data query tool but could be improved.

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 with enums but 0% description coverage. The description mentions grouping by credit rating and industry but does not explain the parameter values or add meaning beyond the schema. Consequently, it fails to compensate for the lack of schema descriptions.

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

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

The description clearly states the tool provides corporate leverage and debt benchmarks (Net Debt/EBITDA, interest coverage, debt maturity profiles) by credit rating tier and industry. It also specifies the data source and use cases, distinguishing it from sibling benchmark tools.

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

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

The description targets CFOs and treasurers for refinancing, covenant setting, and credit rating management, but does not explicitly state when not to use this tool or direct to alternatives among the many sibling benchmark tools.

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

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

No annotations provided; description transparently lists output contents and sources but lacks details on data freshness, authentication, or rate limits. Adequate for a read-only benchmark tool.

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

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

Two concise sentences with no superfluous information. Front-loaded with core function, then source and audience. Efficient.

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

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

With no output schema, description covers output concept and filters. Could clarify return format (e.g., ranges vs single values) but sufficient for a benchmark tool with two 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?

Schema coverage 0%; description mentions 'by property type and lender type' but does not explain enum values or defaults. Schema enums are self-explanatory, but description adds minimal value beyond the schema.

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

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

Description clearly states it provides CRE debt benchmarks (DSCR, LTV, spread ranges) by property and lender type, with sources and target audience. Distinguishes from sibling tools like get_cap_rate_benchmark.

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

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

Implied usage for CRE CFOs and capital markets teams, but no explicit when-to-use or when-not-to-use, and no comparison to sibling tools like get_mortgage_market_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 exist, so the description carries full burden. It discloses that data is live, updates daily, and comes from FRED ICE BofA indices. It lists what the tool returns. This is adequate for a read-only benchmark tool, though it does not elaborate on rate limits or authentication.

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 concise sentences: first presents the tool's offerings, second notes update frequency, third states audience. No unnecessary words; key information is front-loaded.

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

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

Given the tool's simplicity (one optional parameter, no output schema), the description covers the essential aspects: data source, specific metrics, update cycle, and target users. It does not explain every term (e.g., distress signal) but assumes domain knowledge, which is reasonable.

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 'rating_tier' is described indirectly: the description mentions 'OAS by rating tier' and lists 'investment grade and high yield', which aligns with the enum values (ig, hy, bbb, all). The description adds meaning beyond the raw enum list by connecting it to the tool's outputs.

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 'credit spread benchmarks' from specific indices (FRED ICE BofA) and lists concrete data points (OAS by rating tier, TED spread, 2s10s Treasury spread, distress signal). It distinguishes itself from sibling benchmark tools by focusing on credit spreads.

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

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

The description targets credit analysts and fixed income PMs, implying the tool is for credit spread analysis. It does not explicitly state when not to use or list alternatives, but the specificity to credit spreads provides sufficient guidance among many sibling benchmarks.

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

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

No annotations are provided, so the description carries the full burden. It implies read-only behavior by describing a benchmark tool, but does not specify important details like data refresh frequency, historical range, or whether results are aggregated. Adequate but could add more 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 three sentences, front-loaded with the main purpose, and contains no unnecessary words. It efficiently conveys the tool's function, metrics, source, and target 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, the description sufficiently explains what data is returned and its use case. It lacks details on how parameters combine (e.g., charter_type filter) and return format, but for a benchmark tool, the provided context 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 schema has 2 parameters with enums and 0% description coverage. The description mentions 'by asset size' aligning with the required asset_size_tier parameter, but does not mention the optional charter_type. The enum values are self-explanatory, so the description adds some value but does not fully compensate for the lack of schema descriptions.

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

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

The description clearly states the tool provides credit union financial performance benchmarks and lists specific metrics (capital ratios, net interest margin, loan growth, delinquency rates) and source. It distinguishes from the many sibling benchmark tools by targeting credit unions and specifying NCUA data.

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

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

The description explicitly mentions the target user ('credit union CFOs preparing for NCUA exams and board reporting'), providing clear context for when to use. It does not explicitly state when not to use or suggest alternatives among siblings, but the context is sufficient.

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

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

No annotations are provided, so the description must disclose behavioral traits. It specifies that the correlation is '30-day rolling', mentions the source (DeFiLlama historical prices), and lists output components (correlation pairs, beta, dominance, diversification signal). However, it does not clarify if data is cached, real-time, or how often it updates, nor does it state any access restrictions. Given the lack of annotations, the description provides moderate context but missing details like latency or historical refresh.

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 focused sentence listing key outputs and source, followed by a target audience note. It is front-loaded with the primary function. No wasted words, though it could be slightly restructured to separate purpose from details.

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

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

Given the tool's complexity (correlation matrix, beta, dominance, diversification) and the absence of an output schema, the description should elaborate on output format, how each metric is computed, and interpretability. It omits important context like whether beta is based on daily returns, the definition of dominance, or how the diversification signal is derived. Agents lack sufficient information to confidently 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 one parameter 'period' with enum ['7d','30d','90d'] and 0% description coverage. The description only says '30-day rolling', which implies a default of 30d but does not explain the parameter or its valid values. An agent cannot infer that the 'period' parameter controls the calculation window. The description fails to add semantic meaning beyond what the schema provides.

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

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

The description explicitly states the tool returns a '30-day rolling correlation matrix for BTC, ETH, and SOL — Pearson correlation pairs, beta to BTC, dominance context, and portfolio diversification signal. Source: DeFiLlama historical prices.' This clearly differentiates it from sibling tools like 'get_chain_tvl_benchmark' or 'get_gas_benchmark', which cover different crypto metrics.

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

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

The description mentions 'For crypto portfolio agents' and 'portfolio diversification signal', implying usage for portfolio analysis. However, it provides no explicit guidance on when to use this tool versus alternatives (e.g., when is 7d vs 30d period appropriate, or when to prefer other crypto benchmarks).

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

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

No annotations provided. Description states 'Source: DeepDAO public data' implying read-only and no destructive actions, but does not confirm safety, authentication needs, or rate limits. Mentions median benchmarks but not response 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, front-loaded with purpose and key data. Efficient but could be structured (e.g., bullet points). 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?

No output schema; description gives median numbers but does not explain full response format (e.g., rows, fields). For a tool with multiple benchmarks, an agent needs to know output structure. Adequate but incomplete.

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

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

Schema coverage is 0%, but description hints at sorting via 'top DAOs by treasury size, stablecoin percentage' which maps to sort_by enum. However, does not explicitly explain parameters or their effects. 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?

Description clearly states 'DAO treasury benchmarks' and lists specific metrics (treasury size, stablecoin percentage, runway, governance token concentration), with concrete median values. It distinguishes from siblings like get_chain_tvl_benchmark or get_defi_yield_benchmark by specifying DAO treasury 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?

No explicit guidance on when to use this tool vs alternatives. Description does not mention prerequisites, context, or exclusions. Given many sibling tools, an agent needs clearer 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 response is Ed25519-signed via public MCP and requires no API key. It implies a read-only operation. It does not mention rate limits or error behavior, but the provided details add meaningful transparency beyond what annotations would typically convey.

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 states output, the second covers filters and API details. It is front-loaded and every word adds value. No unnecessary repetition.

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

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

Given no output schema, the description lists key output fields (APY bands, TVL, chain, pool id). It also mentions the source (DeFiLlama) and signing. This is sufficient for an agent to understand what to expect from the response.

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

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

Schema coverage is 100% with each parameter having a description and examples. The description merely restates that parameters are optional substrings, adding no new meaning. Baseline score of 3 is appropriate since the schema already provides sufficient 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 returns 'DeFi lending and stable yield benchmark' with specific metrics (APY bands, TVL, chain, pool id). It differentiates from sibling benchmark tools by focusing on DeFiLlama Yields and stablecoin pools. The verb is implicit but 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 explains optional filters (protocol/asset substring) and default behavior (stablecoin-marked pools). It notes the API is free and public. While it doesn't explicitly contrast with similar siblings like get_stablecoin_yield_benchmark, the context is sufficient for basic guidance.

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

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

With no annotations, the description must cover behavioral aspects. It names sources (NAHB, ULI, industry composite) but does not disclose data freshness, update frequency, or output format. It implies a read-only operation but lacks explicit safety cues.

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 key metrics and use cases. Every sentence adds value without redundancy, making it efficient for an AI agent to parse.

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

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

Despite moderate complexity (two parameters, no output schema), the description omits what the output looks like (e.g., single value vs. table) and any limitations. Users need more context to understand the tool's full capabilities.

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

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

Schema description coverage is 0%, so the description must compensate. It mentions 'by product type' hinting at the 'product_type' parameter, but does not describe 'market_tier' or provide any additional meaning beyond the enum values in the schema.

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

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

The description clearly defines the tool as providing development pro forma benchmarks, listing specific metrics (yield on cost, profit-on-cost, etc.) and data sources. It distinguishes itself from siblings like 'get_construction_cost_benchmark' by focusing on development 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?

The description explicitly states target users ('developers underwriting new projects and lenders sizing construction loans'), giving clear context for when to use this tool. However, it does not mention when not to use it or suggest 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 full burden. It discloses the data source and model (SEC EDGAR aggregate + Sloan accruals model) but does not explicitly state read-only behavior, rate limits, or data availability 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 concise at two sentences, front-loaded with the purpose and metrics, and every sentence adds value without fluff.

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

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

Given the tool has two parameters and no output schema, the description is adequate but incomplete: it does not describe the output format or provide examples, which would be helpful for an agent to understand what the tool returns.

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 mention the 'revenue_recognition_model' parameter at all. While it implies the 'sector' parameter is used for filtering, it adds little meaning beyond what the enum values provide, leaving the optional 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 earnings quality and financial statement risk benchmarks, listing specific metrics (accruals ratio, cash conversion, revenue recognition risk) and data source, distinguishing it from many sibling benchmark tools.

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

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

The description specifies the target users (CFOs, auditors, analysts) and context (assessing financial reporting risk before M&A or investment), providing clear guidance on when to use the tool, though it does not explicitly mention when not to use it.

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

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

No annotations are provided, so the description carries the full burden. It mentions the return format (JSON) and gives source attribution (KLAS/Kaufman Hall) but lacks details on idempotency, rate limits, or side effects.

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

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

The description is concise (two sentences plus example) and front-loaded with the main purpose. However, it includes minor redundancy ('for health IT finance' and 'EHR maintenance benchmark').

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 3 params, no output schema, and no annotations, the description provides the core return value and an example but does not fully specify input semantics (e.g., vendor_name) or processing details.

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

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

Schema coverage is 0%, so the description must compensate. It explains the purpose of 'bed_count' and 'annual_cost' for the optional gap calculation but does not describe the required 'vendor_name' 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 identifies the verb ('Returns'), the resource ('benchmark_cost_per_bed_median'), and the domain ('health IT finance'), with a concrete example. It effectively distinguishes from sibling benchmark tools.

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

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

The description implies usage for health IT finance benchmarking but does not explicitly state when to use this tool over alternatives, nor provide when-not-to-use guidance.

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

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

The description discloses the API key dependency, non-realtime nature, and fallback behavior ('empty or guidance per resolver'). However, 'Energy price strip dependency' is unclear without elaboration.

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

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

The description is short but uses jargon ('style JSON', 'per resolver') that may confuse. Could be more concise and clear.

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

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

Given no parameters and no output schema, the description provides basic context (API key, realtime status) but lacks details on return structure or usage examples.

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 no parameters, so description doesn't need to add parameter details. Baseline 4 applies since no parameters exist to explain.

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 tool returns 'EIA spot-price style JSON' for commodity strategists, clearly identifying the target audience and data type. The mention of API key configuration distinguishes it from sibling tools that don't require such setup.

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 siblings. The description implies it's for energy price data but doesn't contrast with other benchmarking tools or specify use cases.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description adds context by detailing the output (positions, targets, levels, confidence) and providing an example. This is sufficient and consistent with annotations.

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

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

The description is two sentences plus an example, all front-loaded with purpose. Every sentence adds value without redundancy 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?

With no output schema, the description fully explains what the tool returns (position, targets, invalidation levels, confidence scores) and gives a concrete example. The single parameter is adequately described, making the tool's functionality completely understandable for selection and invocation.

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

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

The input schema for 'asset' is already described well (symbol or 'all'), but the description adds concrete examples of valid assets (BTC, SPY, TLT, Gold) and implies the default is 'all', which goes beyond the schema's generic description.

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

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

The description clearly states it provides Elliott Wave positions, targets, invalidation levels, and confidence scores for specific assets (BTC, SPY, TLT, Gold). The example further clarifies the output. This distinctively differentiates it from sibling tools focused on other financial metrics.

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

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

The description indicates the tool is for traders and portfolio managers tracking technical market structure, providing clear usage context. However, it does not mention when not to use it or explicitly compare to siblings like 'get_market_structure_signal', missing some guidance on 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 carries full burden. It states the tool returns JSON and specific data categories, indicating a safe read operation with no side effects. However, it does not mention rate limits, authentication, or other behavioral aspects.

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 plus a short phrase. It front-loads the tool's purpose, target audience, and example, with zero wasted words.

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

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

Given no output schema, the description adequately explains what is returned (prevailing wage stats, certified job titles, state mix, compensation intelligence). It is complete enough for a simple tool with one parameter, though additional details about response format could be beneficial.

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

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

Only one parameter (employer_name) with 100% schema description coverage (examples provided). The tool description repeats the schema's purpose without adding new semantic information beyond what the schema already conveys.

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

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

The description clearly states the tool returns JSON prevailing wage stats, certified job titles, and state mix from DOL LCA filings by employer_name. It specifies target users (HR analytics and talent strategy teams) and gives an example, distinguishing it from the 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?

Usage context is implied ('for HR analytics and talent strategy teams') but no explicit when-to-use or when-not-to-use guidance is given. No alternatives are mentioned among the numerous 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?

With no annotations, the description must convey behavioral traits. It discloses data sources (EPA GHGRP, MSCI ESG methodology) and listed metrics, which adds context. However, it does not mention whether the tool is read-only, requires authentication, has rate limits, or what the return format is.

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 purpose and key details (metrics, sources, audience). It is efficient and avoids redundancy, though it could optionally add a brief usage note.

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

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

Given the lack of an output schema and the simplicity of the tool (2 enum parameters), the description provides moderate completeness by listing output metrics and sources. However, it does not describe the response structure or how to interpret the benchmarks, leaving some ambiguity.

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

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

The description mentions 'by sector' but does not explicitly map to the sector parameter enum values. It lists metrics that correspond to the focus parameter (carbon, social, governance, all) but does not explain the parameter or its options. With 0% schema coverage, the description should provide more parameter 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 provides ESG benchmarks by sector and lists specific metrics (carbon intensity, net zero commitments, SBTi alignment, etc.), which distinguishes it from many sibling tools focused on financial or market benchmarks. However, it does not explicitly differentiate from other ESG-related tools on the server.

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

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

The description implies the tool is for sustainability agents and ESG analysts, but provides no explicit guidance on when to use it versus alternative tools, no when-not-to-use conditions, and no prerequisites or context for invoking the 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?

No annotations are provided, so the description must disclose behavioral traits. It does not mention side effects, authentication needs, or rate limits. Terms like 'composite mode' and 'control library context' hint at behavior but remain undefined.

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, relatively short sentence, which is concise. However, it sacrifices clarity for brevity, using dense jargon that may confuse agents.

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 likely complexity (EU AI Act coverage, composite mode, nistFunction parameter) and the absence of an output schema, the description fails to explain what the tool returns, how to use it, or when it is appropriate. It is severely incomplete.

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

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

The schema description coverage is 0%, yet the description does not mention the single parameter 'nistFunction' or its enum values (GOVERN, MAP, MEASURE, MANAGE). The description adds zero value for understanding parameter usage.

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

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

The description specifies the domain (EU AI Act) and mentions 'coverage', 'composite mode', and 'implementation guidance', but it is vague about what exactly the tool returns. The resource is clear, but the modifiers are jargon-heavy and not well explained.

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 the many similar sibling tools. The phrase 'non-org-specific' offers slight context, but there are no explicit recommendations 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. It mentions the data source (USASpending) and return format (JSON), but does not disclose authentication requirements, rate limits, data freshness, error handling, or behavior when data is not found. Significant gaps remain.

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

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

The description is two sentences with no wasted words. The first sentence packs the essential information; the second sentence is slightly redundant but acceptable. Could be more tightly structured but is overall 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 five parameters, no output schema, and no annotations, the description should provide more detail on the output structure, pagination, sorting, or error states. It only specifies 'JSON' and 'obligation rollups,' which is insufficient for an agent to use reliably 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?

The description adds meaning by listing vendor_name as the primary filter and mentioning optional parameters (agency_name, naics_code, fiscal_year), partially compensating for the low schema coverage (20%). However, it omits the 'state' parameter entirely, leaving its purpose 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 it returns USASpending-synced obligation rollups JSON for government affairs teams, filtering by vendor_name and optionally agency_name, naics_code, fiscal_year. It identifies the specific resource and verb, distinguishing it among 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 provides context (for government affairs teams, USASpending-synced) but does not explicitly state when to use this tool versus alternatives like get_vendor_contract_intelligence. Usage guidance is implied but lacks exclusion or alternative references.

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 tool is illustrative, conditioned on the latest FRED fed funds print, and not market odds. This provides sufficient transparency for a read-only, educational tool.

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

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

The description is a single, well-structured sentence that front-loads the purpose, resource, scope, and key caveats without any wasted words.

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

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

Given no parameters, no output schema, and no annotations, the description fully covers the tool's purpose, audience, data source, and limitations, making it self-contained 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?

There are no parameters, and schema coverage is 100%. The description adds no parameter details, but the baseline for no parameters is 4.

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

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

The description clearly states that the tool returns 'illustrative cut, hike, hold probabilities' for the next three FOMC meetings, targeted at 'macro educators'. It differentiates itself from market-implied odds by stating 'explicitly not futures-implied market odds'.

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 defines the audience ('macro educators') and context ('scenario planning narrative only'), implying it is not for real trading. However, it does not explicitly state when not to use or compare with 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; the description adds some transparency by noting source (FRED) and update frequency (daily) but does not disclose behavioral traits like idempotency, authentication needs, 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?

Two concise sentences front-load key information (list of benchmarks, source, update frequency) with no unnecessary words.

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

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

For a simple 1-parameter tool with no output schema, the description is nearly complete. It covers available benchmarks and update cadence; only lacking detail on output format or sample response.

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

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

Schema description coverage is 0%. The description lists currency pairs but does not explain the only parameter (base_currency) or how it affects results, missing an opportunity to clarify parameter 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 explicitly states it provides live major currency pair benchmarks, listing specific pairs and metrics, which clearly differentiates it 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 use for currency benchmarks but does not provide explicit when-to-use or when-not-to-use guidance relative to 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 exist, so the description must fully disclose behavior. It states the tool returns live benchmarks, mentions sources (public RPCs), notes zero API key requirement, and lists output types. This is transparent, though rate limits or caching are not mentioned.

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

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

Two sentences, no filler. The first sentence conveys the core purpose and chains; the second adds detail on return values and sourcing. Every sentence adds value.

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

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

For a tool with one parameter and no output schema, the description covers purpose, chains, return types (Gwei, USD, congestion, x402), comparison (Base vs ETH), and data source. No gaps for an agent to understand invocation.

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

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

The single 'chain' parameter is an enum with values listed in the schema. The description mentions the chains (Ethereum, Base, Solana) but does not explain the 'all' option or provide additional context beyond what the enum implies. Schema coverage is 0%, so the description partially compensates.

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 it provides live gas price benchmarks for Ethereum, Base, and Solana, listing return details (Gwei, USD cost, congestion category, x402 context). This distinguishes it from sibling benchmark tools that cover different domains.

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

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

The description implies usage for obtaining gas price data but does not explicitly state when to use this tool versus alternatives like other benchmark tools. No guidance on context or exclusion is provided.

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

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

No annotations are provided, so the description carries the burden. It mentions 'live' data and 'subject to rate limits,' which is helpful, but does not elaborate on rate limit handling, caching, authentication, or what happens on failure. More detail would improve 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 concise with two sentences, no redundant words. It front-loads the core function and adds context in the second sentence. Structured well for quick scanning.

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

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

No output schema is provided, so the description should describe the return structure. It only says 'stats JSON' without listing typical fields. Also lacks information on authentication (implied public) and error handling. For a simple tool with one parameter, it is insufficiently 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 says 'org_or_company footprint query,' but does not specify the required format (e.g., exact name, handle) or provide examples. This adds minimal meaning beyond the raw parameter name.

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

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

The description clearly states the tool returns live GitHub organization and repository stats JSON for developer relations teams. It specifies the input as 'org_or_company' and mentions it is a public API with rate limits, making the purpose unambiguous.

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

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

The description indicates it is for developer relations teams and an open-source ecosystem snapshot, but does not explicitly state when not to use it or mention alternatives. However, among the sibling tools (all non-GitHub), this is the only GitHub-related one, so usage context is clear.

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

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

No annotations are provided, so the description must cover behavioral traits. It mentions the output (YTD returns, P/E ratios, risk signal) but fails to disclose any side effects, permissions required, rate limits, or data freshness. For a tool likely performing a read-only query, 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 two sentences with no filler. It front-loads the list of indices and then specifies the data points, efficiently conveying the tool's output.

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

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

Given the tool's simplicity (one optional param, no output schema), the description adequately explains what the tool returns. However, it lacks details on how the 'region' parameter filters results or the exact format of the output, which could be improved.

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

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

The input schema has one parameter 'region' with enum values, but the description provides no additional meaning or usage guidance for it. With 0% schema description coverage, the description should compensate but does not mention the parameter at all.

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 global equity index benchmarks, listing specific indices (S&P 500, Nasdaq, etc.) and the data points (YTD returns, P/E ratios, risk signal). This is a specific verb-resource combination that distinguishes it from sibling tools focused on other benchmarks like cost or regulatory metrics.

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

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

The description implies use for global equity benchmarks but does not explicitly state when to use this tool over alternatives. No exclusion criteria or alternative tool names are mentioned, leaving the agent to infer from the tool name and listed indices.

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

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

No annotations provided, so description carries full burden. It discloses the data is static and composite, not real-time or user-specific, and specifies the acute care context. However, it does not mention data freshness, update frequency, required permissions, or whether results are cached. It adds some behavioral context but not complete.

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 conveying key information about output and model type. No fluff, front-loaded with primary values. However, it could be improved by adding parameter information in a structured way.

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, description explains output fields (savings %, leakage %, risk threshold, top categories) and audience (materials managers). But it omits details about the category parameter, return format specifics, and usage context. Adequate but incomplete for a tool without annotations or output schema.

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

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

Input schema has one optional 'category' parameter with 0% schema description coverage. The description does not mention this parameter at all, leaving the agent with no information on what value to provide or how it influences the output. This is a critical gap for a tool with a 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?

Description clearly states it returns JSON with typical GPO savings, leakage, and risk thresholds for acute care GPO savings model. Distinguishes itself as a static HFMA/CMS composite rather than user invoices, which separates it from invoice-related tools. However, lacks clarity on how the optional 'category' parameter affects the output.

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 its many sibling benchmark tools (e.g., get_pharmacy_spend_benchmark, get_hospital_supply_chain_benchmark). The description only hints at the data source (HFMA/CMS) and that it is not invoice-based, but does not specify the exact use case or prerequisites.

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

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

No annotations provided, so the description must disclose behavior. It states 'Returns JSON' but fails to mention read-only nature, authentication requirements, or potential side effects. The phrase 'narrative scan' and 'recommendation audit' is vague.

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 but the first is a run-on that lists output fields without clear punctuation. It could be more concise and better structured.

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

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

Given no output schema and one parameter, the description should fully explain the tool's functionality and return structure. The mention of multiple outputs and defaults is insufficient; the relationship between outputs and the meaning of 'narrative scan' remains 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?

The single parameter 'category' is a string with no schema description. The description adds context by narrowing to healthcare IT vendor categories, but does not specify acceptable values or format, leaving ambiguity.

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

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

The description mentions specific outputs (top_recommended_vendors, ai_consensus blurb, sample_size) and a target audience (health IT strategists), but the phrasing is jumbled and fails to clearly distinguish from siblings like get_top_vendors_by_category or get_category_ai_leaders.

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

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

No guidance on when to use this tool versus alternatives. The mention of 'AI citations or Epic and Meditech-style defaults' hints at data sources but does not clarify selection criteria 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, but description discloses it returns a JSON payload and mentions embedded hints and data sources (CMS, Stratalize). However, it does not specify whether it is read-only, requires authentication, or has 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?

Three sentences, front-loaded with purpose. Efficient but could be slightly more concise. No redundancy.

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

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

With no output schema and 2 params, description covers purpose and data source but lacks details on output structure, units, or time period. Adequate but not fully complete for an agent to invoke without 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?

Input schema has 2 params with 0% coverage. Description adds context by listing example categories but does not fully explain vendor_name or expected format. Adds some meaning beyond schema but insufficient.

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

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

The description clearly states it returns JSON market-rate payload for healthcare vendor categories, with specific examples (EHR, staffing, food, waste, med-surg). It distinguishes from siblings like get_vendor_market_rate by focusing on healthcare.

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 any healthcare vendor category but does not explicitly state when to use this tool vs alternatives like get_vendor_market_rate. No when-not or exclusions 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 only states 'from synced public data' and mentions example fields. No disclosure of behavioral traits like data freshness, rate limits, or any side effects beyond being a read operation.

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

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

Three sentences with no fluff. Front-loaded purpose and then adds specific output details. 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?

Adequate for a simple lookup tool with one parameter and no output schema. But lacks details like example usage, query constraints, or expected response format, which could be improved.

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% for the single parameter 'hospital_name'. The description adds context about output fields but not about the parameter itself. 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?

Description clearly states it returns JSON quality scores with specific metrics like safety, readmissions, star rating, and identifies the target audience. While clear, it could better distinguish from siblings like get_cms_star_rating or get_cms_facility_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, no exclusions, and no context for when not to use it. The description lacks any usage-oriented language.

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

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

No annotations are provided, so the description carries full burden. It discloses the data source (CMS HCRIS resolver), a median fallback (~17%), and that output is JSON. This adds useful behavioral context beyond the 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?

Single sentence packs return format, metric, percentiles, audience, filters, data source, fallback, and peer comparison. Dense but clear and front-loaded with key purpose.

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

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

Given no output schema, the description explains returned percentiles and benchmark context. It covers the main use case adequately for a benchmark tool with clear filters.

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 mentions filtering by bed_size and state, clarifying both parameters. With only two params, this adequately explains their roles.

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

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

The description clearly specifies the verb 'returns' and the resource 'supply cost percent of operating expense p25/p50/p75' for hospital CFOs, with filters by bed_size and state. It distinguishes from sibling tools by targeting hospital supply chain benchmarks.

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

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

The description implies usage for hospital supply cost benchmarking but does not explicitly state when to use this tool versus alternatives or provide exclusions. No guidance on when not to use it.

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

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

No annotations exist, so description must carry the transparency burden. It adds behavioral context: live data from FRED/Census, leading indicator nature, target audience. However, it omits update frequency, data freshness, or any 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 succinct sentences: first defines what it does, second adds contextual value (leading indicator, audience). No wasted 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, so description should clarify return format or example outputs—it does not. Parameter count is low, but missing guidance on how parameters affect results. Could be more complete given the tool's niche and many siblings.

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 parameter descriptions). The description does not explain the two parameters (region, structure_type) beyond implying 'by market tier'. It misses an opportunity to clarify how parameters filter or what the enum values represent, leaving the agent to infer from names alone.

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

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

The description clearly states the tool provides 'Live housing supply indicators — starts, permits, completions, and absorption' from specific sources (FRED and Census), and identifies its use as a leading indicator. This distinguishes it from numerous siblings focusing on other economic or financial benchmarks.

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

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

The description implies usage for forecasting housing prices ('leading indicator 6-12 months ahead'), but does not explicitly state when to use this tool over alternatives or provide exclusion criteria. Among many real estate siblings, no comparison is made.

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

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

No annotations are provided, so the description must fully disclose behavioral traits. It mentions the dataset is annual and free, but does not explain what the tool returns (e.g., rent amounts per bedroom count), potential limitations (e.g., only FMR, not actual market rents), or any authentication or rate limiting. For a read-only data retrieval tool, more transparency on output and constraints is needed.

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) and front-loaded with the core purpose. The first sentence immediately defines what the tool does, and the second adds context with use cases and source. No redundant or unnecessary words.

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

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

Given the tool's simplicity (2 required/optional parameters) and no output schema, the description adequately covers the what and why. However, it lacks details on the return structure (e.g., rent amounts per bedroom count), how data is formatted, or error handling. For a tool with no output schema, more guidance on what the agent will receive would improve completeness.

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

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

The input schema has 2 parameters with 50% description coverage (only metro_area has a schema description). The tool description mentions 'bedroom count' in the purpose, adding some meaning beyond the schema (which lacks a description for bedroom_count). However, it does not clarify the format or range for metro_area beyond examples, nor does it specify that bedroom_count is optional with a 0-4 range (already in schema). Baseline 3 is appropriate given the partial 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 the tool provides HUD Fair Market Rents by metro area and bedroom count. It lists specific use cases (affordable housing underwriting, Section 8 compliance, LIHTC calculations, housing authority budgeting) and identifies the source (HUD annual FMR dataset). This precisely distinguishes 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 explicitly states several use cases, giving clear context for when to use the tool. However, it does not provide guidance on when not to use it or mention alternatives (e.g., get_rental_market_benchmark). The context is strong but lacks explicit 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?

With no annotations provided, the description carries the full burden. It discloses the tool is read-only ('returns'), static, and not live, which adequately conveys behavioral traits for a simple data retrieval tool. No contradictions, but could mention any constraints like data frequency or update schedule.

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 three concise sentences, each serving a clear purpose: what it returns, the specific content, and its academic positioning. No fluff, front-loaded with key differentiators. 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 parameters or output schema, the description is fairly complete. It specifies the return type (JSON static composites), content focus (GDP, inflation), and use case (academic briefing). Missing details like exact time periods covered or update frequency, but overall adequate for a simple snapshot 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?

Input schema has zero parameters, and schema coverage is 100% (trivially). The description adds value beyond the schema by explaining the content (global GDP, inflation, academic macro briefing card) and nature (static, curated), which sets proper expectations for the 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?

Description clearly states the tool returns static IMF WEO-style macro composites JSON, specifying it is curated tables, not live API pulls or country desks. It identifies the specific resource (Global GDP and inflation reference snapshot) and distinguishes from siblings by emphasizing 'static' nature, making purpose specific and unique among many 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?

Description provides context by stating it returns static curated data, not live IMF pulls, which implies when to use (for static reference) and when not (for live data). However, it does not explicitly name alternative tools or provide direct when-to-use vs when-not-to-use guidance, leaving some inference to the agent.

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

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

With no annotations, the description carries the full burden. It mentions the data is a 'static Stratalize industry composite, not transactional GL data', hinting at non-real-time nature, but does not disclose if the tool is read-only, required permissions, rate limits, or other behavioral traits.

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

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

The description is relatively short but uses disjointed phrasing and jargon like 'canned category_breakdown' and 'Software stack spend curve'. It could be more concise and structured, but it is not overly verbose.

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

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

Given the simple tool with two parameters and no output schema, the description should provide more complete context. It mentions output fields but not their structure, and parameter details are absent. The description is insufficient for full 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?

The input schema has two parameters (industry, company_size) with 0% description coverage. The tool description does not explain what values are valid or how to use these parameters, leaving the agent without guidance on required inputs.

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 fields like median_total_spend_monthly and category_breakdown, indicating it provides industry spend benchmarks. However, the phrasing is somewhat cryptic and could be more straightforward.

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

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

The description offers no guidance on when to use this tool versus other similar benchmark tools, such as get_category_spend_benchmark or get_asc_cost_benchmark. No alternatives or context for appropriate use 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?

With no annotations, the description must convey behavioral traits. It states 'Returns JSON' and gives a source, but does not disclose whether the operation is read-only, idempotent, or any side effects. It adequately implies a safe query but lacks explicit 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 two sentences with no wasted words. It front-loads the core purpose and includes a concrete example, 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, the description fairly describes the return structure (bands, ranges, flags) and cites a source (Stratalize). It could elaborate on JSON structure or interpretation, but overall it is sufficiently complete for a benchmark tool.

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

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

Schema coverage is 100% with descriptions for both parameters ('Industry vertical', 'Employee headcount for banding'). The description adds meaning by linking them to 'spend bands, category ranges, and outlier flags' and mentioning 'workforce-scaled vendor benchmark', going slightly 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 industry spend bands, category ranges, and outlier flags by employee_count for CFOs sizing SaaS and ops stacks.' It specifies the target audience and the specific resource, distinguishing it from sibling tools like 'get_industry_spend_benchmark'.

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

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

The description provides context for usage (CFOs sizing SaaS/ops stacks) and an example (healthcare vs manufacturing tier curves). However, it does not explicitly state when to avoid using this tool or mention alternatives, which could be useful given the extensive sibling list.

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

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

No annotations are provided, so the description must carry the full burden. It does not disclose behavioral traits such as data freshness, rate limits, or API dependencies. While it describes the output, it lacks transparency about the source call (e.g., FRED API call, possible failures).

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

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

The description is a single well-structured sentence that front-loads the core purpose and lists key outputs. Every part adds value, with no redundant or filler 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 simple schema (one optional parameter) and no output schema, the description adequately explains what data is returned (specific indicators and policy implications). It could mention data frequency or update cadence, but is still quite complete for its 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 schema has one optional enum parameter 'measure' with schema coverage 0%. The description compensates by listing the values (CPI, core CPI, PCE, core PCE, breakeven, components, all) and what they include, adding meaning beyond 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 it provides 'live inflation benchmarks from FRED' and lists specific components (CPI, core CPI, PCE, etc.) and outputs (Fed target gap, anchoring signal, policy implication). It is specific, uses a strong verb-resource combination, and distinguishes from siblings like get_fomc_rate_probability.

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 inflation benchmarks for macro analysis, but does not explicitly state when to use this tool versus alternatives like get_fomc_rate_probability or other macro tools in the sibling list. No exclusions or prerequisites 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 carries full burden. It lacks disclosure of behavioral traits such as data freshness, whether the operation is read-only, or any potential side effects. The description only states the source but no further behavioral 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 two sentences with no wasted words. It front-loads the key purpose and provides source and audience efficiently.

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

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

Given the lack of output schema and annotations, the description is adequate but incomplete. It covers purpose and source but does not describe the output format or any constraints (e.g., data range, update frequency).

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, and the description does not explain the parameters. It implies filtering by line of business but does not mention 'company_size' or clarify that 'line_of_business' is required. The description adds little meaning beyond the schema's enum values.

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

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

The description clearly states the tool returns insurance financial performance benchmarks with specific metrics (combined ratio, loss ratio, etc.) and identifies the source (NAIC annual statistical report). It distinguishes itself from sibling tools by being specifically for insurance underwriting 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 mentions target users (CFOs, actuaries, analysts) and context (reviewing underwriting performance), but does not provide explicit guidance on when to use this tool versus alternatives or when not to use it.

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

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

With no annotations, the description must disclose behavioral traits. It mentions returning JSON and using 'citation heuristics', but fails to state whether the tool is read-only, has side effects, rate limits, or authentication needs. The behavior is opaque and the description does not compensate for missing annotations.

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

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

The description is three sentences but packed with unclear jargon like 'ten-platform style narrative'. It is not concise; the phrases do not effectively communicate purpose. A clearer, more straightforward description would be shorter and more useful.

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 the return value in detail. It mentions 'growth_signal stable or growth, top_brands, evidence lines' but does not define these fields or how they relate to the input. The context for usage is incomplete.

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

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

The only parameter 'category' has no description in the schema (0% coverage) and the description adds no clarification. The term 'VC software category' is hinted but not defined. The description does not help an agent understand what values to provide.

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

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

The description uses jargon like 'citation heuristics', 'Snowflake style defaults', and 'ten-platform style narrative' without clearly stating what the tool does. It mentions returning 'growth_signal' and 'top_brands' for growth investors, but the purpose remains vague and hard to distinguish from siblings 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 indicates it's for growth investors but provides no explicit guidance on when to use this tool versus alternatives like 'get_category_ai_leaders' or 'get_category_spend_benchmark'. No when-not-to-use or exclusion criteria 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, and the description does not disclose behavioral traits such as data freshness, rate limits, authentication needs, or side effects. It only lists indicators.

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 critical information, no redundant words. Lists indicators efficiently.

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

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

For a simple tool with one optional parameter and no output schema, the description is mostly complete, though it does not describe the output format. The list of indicators and signal provides sufficient 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% but the description lists indicators that map to the enum values of the 'focus' parameter, partially compensating. However, it does not explicitly explain the parameter's role.

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

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

The description clearly states the tool provides live labor market benchmarks from FRED, listing specific indicators and mentioning a computed signal. It is well-differentiated from sibling tools which cover other benchmarks (e.g., inflation, yield curve).

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 labor market analysis but does not explicitly specify when to use this tool vs. alternatives, nor does it provide exclusions or prerequisites.

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

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

With no annotations, the description carries full burden. It discloses the live FRED feed dependency and the fallback behavior when the API key is missing. This is good transparency for a data retrieval tool, though it could elaborate on what 'empty snapshot fields' means or any 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 two sentences plus a fragment, which is reasonably concise. However, the final fragment 'Rates and inflation panel.' is somewhat redundant and could be integrated into the first sentence. Overall, it is well-structured and front-loaded.

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

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

Given that there is no output schema and only one parameter, the description should explain both what the parameter does and what the output looks like. It covers the output broadly but omits parameter documentation, leaving the tool incomplete for an agent. The lack of an output schema places extra burden on the description, which it does not fully meet.

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 for the single parameter 'signal_type'. However, the description fails to mention the parameter at all, leaving the agent to guess valid values or how to specify which data block to retrieve. This is a critical gap.

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

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

The description states it returns JSON blocks for Fed funds, Treasury yields, CPI, PCE, and employment hooks, which clearly indicates it provides macroeconomic data. It distinguishes from siblings by specifying these data types and the FRED dependency. However, it could be more explicit about the action it performs (e.g., 'retrieves macro market signals').

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

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

The description mentions the condition that FRED_API_KEY must be set for live data, otherwise empty fields are returned. This provides some guidance, but it does not advise on when to use this tool versus alternatives like get_fomc_rate_probability or get_inflation_benchmark, nor does it specify when not to use it.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the description does not need to reiterate safety. The description adds limited behavioral context (e.g., 'current' playbook) but lacks details on data source or update frequency.

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

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

The description is two sentences, front-loaded with key content (list of playbook components), and an example. 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 zero-parameter read-only tool with no output schema, the description is fairly complete, listing what the playbook contains and the intended users. It could mention data freshness or format, but it's adequate.

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

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

The input schema has zero parameters, so baseline is 4. The description does not need to explain parameters and does not add any 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 it provides a current macro trading playbook with specific elements like active regime label, positioning themes, risk triggers, and tactical opportunities. This distinguishes it from sibling tools which are mostly benchmarking tools.

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

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

The description specifies the target audience (traders, portfolio managers, macro allocators), indicating who should use it. However, it does not provide explicit when-to-use guidance or compare with alternative tools.

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

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

The description discloses the data source (Damodaran dataset and public deal aggregates), which adds behavioral context beyond the simple get operation, but lacks detail on response format, pagination, or authorization requirements. Since no annotations are provided, more transparency would be beneficial.

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), front-loaded with the core purpose, and includes source and use case without unnecessary words. Every sentence adds value.

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

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

Given no output schema, the description partially compensates by listing the return metrics (EV/EBITDA, EV/Revenue, control premiums), but it does not describe the output structure or format, leaving ambiguity about what exactly the agent receives.

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

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

The description adds meaning by stating the tool filters 'by industry and deal size,' mapping directly to the two parameters, but does not elaborate on the enum values or provide additional semantics beyond what the schema provides. With 0% schema description coverage, more parameter-specific detail would be 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?

The description clearly states the tool returns M&A transaction multiples (EV/EBITDA, EV/Revenue, control premiums) by industry and deal size, effectively distinguishing it from sibling benchmark tools that focus on other domains like public market multiples or PE returns.

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

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

The description specifies target users (corp dev, PE deal teams, M&A advisors, CFOs) and a use case (preparing fairness opinions), providing clear context for when to use the tool, though it does not explicitly exclude alternatives or give when-not guidance.

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