Stratalize Healthcare Intelligence

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds 'public mode' context but does not elaborate on behavior beyond what annotations provide, such as response format or limitations.

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

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

Two concise sentences effectively convey purpose and usage constraints. No wasted words.

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

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

Given no parameters or output schema, the description fully explains what the tool does and provides guidance for org-scoped needs, making it complete.

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

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

No parameters exist, and schema coverage is 100%. The description adds no parameter info, which is acceptable given baseline 3 per guidelines.

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 Stage reference with specific stages (INITIAL, MINIMAL, EVOLVING, EMBEDDED). It uses 'returns framework stages' as a verb+resource, distinguishing it from sibling get_* tools.

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

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

The description explicitly says 'public mode returns framework stages only' and directs users to 'Connect org MCP or the dashboard questionnaire for org-scoped classification' indicating when not to use this tool and alternatives.

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

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

With no annotations, the description carries the burden. It mentions 'narrative fallbacks' and lists output fields, but does not disclose behaviors like caching, rate limits, or failure modes (empty results). Adequate but not thorough.

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

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

Three sentences, but 'Example default themes cost optimization.' is a fragment and 'Multi-platform belief synthesis.' is redundant. Could be more structured, e.g., separating output details from usage notes.

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 describe return structure in detail. It lists fields but lacks definitions of 'sentiment_mix' or 'platform_breakdown'. For a research tool with potentially complex output, this is insufficient.

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

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

Schema description coverage is 0%, and the description does not explain the 'category' parameter or its relationship to 'default themes'. The example 'cost optimization' hints at usage but adds no formal meaning beyond the schema.

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

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

The description clearly states the tool returns a specific JSON object (consensus_score, sentiment_mix, etc.) for researching a business topic or vendor, distinguishing it from sibling benchmark tools. The verb 'returns' and resource 'AI consensus' are specific.

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

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

It says 'for analysts researching a business topic or vendor' and mentions 'multi-platform belief synthesis', implying use for opinion analysis rather than hard benchmarks. However, no explicit when-not-to-use or alternatives are given, leaving usage guidance implicit.

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 authentication requirements, rate limits, or what happens when data is unavailable. It only describes the content.

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 concisely lists the data included, the second states the target audience. No unnecessary words or 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 should provide more context about return format, parameter usage, or edge cases. It only describes the content, leaving the agent to infer everything else.

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

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

The description does not mention the two parameters ('focus' and 'institution_type') at all. With 0% schema description coverage, the description fails to add any 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 provides AML regulatory benchmarks, listing specific data types (FinCEN SAR filing rates, OFAC SDN counts, etc.). It distinguishes from sibling tools like 'get_bank_regulatory_benchmark' by focusing on AML-specific 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 target users ('compliance agents and financial institution risk officers') but does not explicitly state when to use this tool versus alternatives or provide any when-not-to-use guidance.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, indicating safe read behavior. The description adds valuable context by explaining the output (medians and percentages) and giving a concrete example, providing behavioral insights beyond what annotations 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 three sentences long, front-loaded with use cases, and includes a concrete example. Every sentence adds value, and the structure is efficient and clear.

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

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

Given no output schema, the description adequately explains the return values (medians and percentages) and provides an example. However, the lack of parameter descriptions limits completeness. For a simple two-parameter tool, the description is acceptable but could be more 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?

Input schema has two parameters (state, specialty) with 0% description coverage and no enums. The description does not explain expected formats, allowed values, or constraints. The example mentions 'Orthopedic' as a specialty but does not clarify if enums exist or how to format state/specialty 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 is used for benchmarking ASC financial performance, evaluating acquisitions, or preparing board reports. It specifies the return type (cost per case medians and revenue mix percentages by specialty) and provides a concrete example, making the purpose highly specific and distinct from sibling tools.

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

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

The description explicitly lists three use cases (benchmarking, acquisition evaluation, board report) and provides an example scenario. However, it does not mention when not to use this tool or suggest alternatives, which keeps it from a perfect score.

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 partly fulfills the transparency burden by indicating the data source and output nature (benchmarks by revenue band and auditor tier). However, it does not disclose exact return format, authentication needs, or rate limits, which is acceptable for a read-only lookup tool but not comprehensive.

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

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

The description is extremely concise: two sentences without wasted words. It efficiently conveys the tool's purpose, key dimensions, source, and use case. Every sentence earns its place.

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

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

Given the presence of three parameters and no output schema, the description provides a general sense of the output (benchmarks) but lacks specifics on exact return fields, revenue band definitions, and how industry is used. It is minimally complete but could be more informative.

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

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

Schema description coverage is low (33%), but the description adds meaning by explaining that annual_revenue_usd maps to revenue bands and auditor_tier corresponds to Big 4/national/regional. However, the industry parameter is not mentioned, leaving its role unclear. Thus, the description adds some but not full semantic value.

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

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

The description clearly states the tool provides audit fee benchmarks, specifying the metrics (total fees and fees as % of revenue) and dimensions (revenue band and auditor tier). It distinguishes from other benchmark tools by focusing on audit fees, making its 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 mentions the intended audience (CFOs, audit committees) and use case (RFPs, fee negotiations), but lacks explicit guidance on when to use this tool versus alternatives or when not to use it. Usage is implied but not clearly delineated.

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 correctly indicates a read-only operation ('returns') and specifies the data type (JSON). While it doesn't explicitly state it's safe/non-destructive, the return semantics are clear given the information provided.

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-loading the core purpose with specific examples. Every sentence adds value: the first states the function, the second gives a concrete example, and the third summarizes. No redundancy or unnecessary text.

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

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

With one parameter and no output schema, the description provides essential context: target users (bank credit analysts), data source (FDIC), and output contents (assets, deposits, etc.). It lacks details on pagination, errors, or data freshness, but is sufficient for a straightforward read 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% (single parameter 'bank_name' with example values). The description does not add additional meaning beyond the schema's examples, such as formatting or constraints. The baseline for high coverage is 3.

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 data for bank credit analysts, specifically FDIC-sourced intel by bank_name, with examples like assets and capital ratios. It distinguishes itself from many sibling tools that cover different domains (e.g., AI, AML, 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 bank financial analysis but does not explicitly state when to use this tool over alternatives, nor does it mention when not to use it. The context of 'bank credit analysts' provides a clear audience but no exclusions 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?

No annotations are provided, and the description does not disclose any behavioral traits such as idempotency, rate limits, authentication requirements, or side effects. It only states what the tool returns, not how it behaves, leaving the agent with incomplete information for safe invocation.

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

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

The description is two sentences long, with the first sentence front-loading the essential information (what the tool does, key metrics, grouping). The second sentence adds source and audience. There is no fluff, and every sentence contributes meaning.

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

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

Given the lack of output schema, the description should hint at the return format (e.g., table of percentages). It lists the metrics but omits output structure, response size, or pagination. For a benchmark tool with 2 parameters, it is moderately complete but could be improved by describing the output.

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

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

Schema description coverage is 0%, so the description must compensate. It explains that benchmarks are grouped by 'asset size tier' but does not describe the meaning of individual enum values or the optional 'bank_type' parameter. It adds some value (e.g., listing the metrics) but falls short of fully clarifying the parameters.

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

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

The description clearly lists specific regulatory capital and financial performance benchmarks (CET1, Tier 1 leverage, NIM, etc.) and states they are grouped by asset size tier. It also identifies the source (FDIC) and target audience. This provides a precise, actionable purpose that distinguishes it from generic benchmark tools.

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

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

The description mentions the target audience (CFOs, risk officers, analysts) but does not provide explicit guidance on when to use this tool versus alternatives. There is no 'when-to-use' or 'when-not-to-use' advice, leaving the agent to infer usage context from the tool's purpose 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 provided, so description must disclose behavior. It states it's a 'static composite model — not claim-level coding', which partially reveals its nature (non-destructive, aggregated). However, it lacks details on authentication, rate limits, or side effects. Given no annotations, this is acceptable but not fully transparent.

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

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

Two concise sentences that front-load the key outputs and immediately clarify the tool's scope. No redundant or irrelevant content. Every sentence adds value.

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

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

The description lists outputs but does not explain how parameters filter or influence the results. With no output schema and moderate complexity (multiple risk components), the description should provide more context on usage scenarios and result interpretation. Incomplete for a risk screening 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 only 33% (only level_4_5_percentage has a description). The tool description does not explain any parameters or their impact on output. For a 3-parameter tool, this leaves users guessing how specialty and annual_claim_volume affect the risk screen. The description should compensate for low schema coverage but fails to do so.

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

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

The description explicitly lists the returned outputs (E/M mix benchmarks, upcoding risk heuristics, OIG themes, RAC watchlist, checklist) and states it's a static composite model, clearly distinguishing it from claim-level coding tools. The purpose is specific and actionable.

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

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

The description implies usage for revenue integrity risk screening but does not explicitly state when to use this tool versus alternatives. With many sibling tools (e.g., get_cms_star_rating, get_asc_cost_benchmark), more explicit guidance on choosing this tool would improve clarity.

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

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

Annotations already mark the tool as read-only and non-destructive. The description adds value by specifying the data source (BLS CPI) and noting it's the Fed's primary benchmark. No additional behavioral traits (e.g., rate limits, pagination) are disclosed.

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 longer than necessary, mixing use cases, an example, and source attribution. The front-loading of use cases is good, but the example and source line could be shortened. Still, all content is relevant.

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, the description covers purpose, use cases, and data source adequately. However, it does not describe the output format or whether historical/current data is returned, relying on user intuition.

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 'category' parameter with an enum, but schema description coverage is 0%. The description mentions 'medical_care' and 'housing' in the example but does not list or explain all enum values (e.g., all_items, food, energy). This leaves meaning unclear for other options.

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

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

The description clearly states the tool retrieves BLS inflation components by spending category, specifying use cases like analyzing inflation exposure and contract escalation. The name and title further clarify purpose, though it does not explicitly differentiate from the sibling 'get_inflation_benchmark'.

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

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

Multiple concrete use cases are listed (analyzing exposure, contract clauses, healthcare/real estate benchmarking, monetary policy context). An example with medical care CPI vs headline is given. However, no explicit guidance on when not to use or comparison with alternatives like get_inflation_benchmark.

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

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

Annotations already indicate read-only and non-destructive behavior. The description adds valuable context: it covers 650K worksites, is the same data used by the Fed, and includes an example output. It does not disclose rate limits or auth needs, but the annotations cover safety.

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

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

The description is a single paragraph with good front-loading of use cases. However, it is somewhat verbose for a simple tool, and some sentences (e.g., source details) could be trimmed without losing 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 adequately explains the return structure (employment numbers, MoM/YoY changes, trend classification) and provides a concrete example. It also cites the source, making it self-contained for a single-parameter tool.

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

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

Schema description coverage is 0%, and the description does not explain the 'sector' parameter values beyond an example. The enum names are self-explanatory but 'all_private' is not clarified. The description 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 BLS payroll employment by major sector with MoM/YoY changes and trend classification, and provides specific use cases like workforce planning and board reporting. It distinguishes itself from siblings through its unique data source and focus.

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

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

The description lists four explicit use cases (benchmarking, planning, reporting, talent acquisition timing), which is clear and context-rich. However, it does not mention when not to use the tool or provide 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?

Discloses default value ~1.8 for momentum_4week under sparse history and heuristic trend calculation, but does not cover error behavior, read-only nature, or other side effects. Without annotations, more detail could be added.

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 deliver key purpose and behavioral insight efficiently. While concise, structure could prioritize parameter explanation more.

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

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

Lists output fields and a notable default behavior, fairly complete for a single-param tool without output schema. Lacks error handling or scope details, but covers essential usage 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 single parameter brand_name is defined in schema but not described in the description. Schema coverage is 0%, and the description adds no extra meaning (e.g., format, examples), though the context makes it understandable.

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

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

Clearly states the tool returns JSON with specific fields (momentum_4week, trend_direction, week_over_week) for marketers, distinguishing it from many sibling tools focused on other 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?

Implies usage for brand trajectory monitoring with heuristic trend, but lacks explicit when-not-to-use or alternatives. The mention of default under sparse history hints at specific conditions.

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 notes it's a 'static' benchmark returning JSON, but lacks details on error handling, rate limits, or required auth. It adequately discloses read-only behavior but not comprehensively.

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 that front-loads the output type and purpose. No redundant information, earning its place.

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

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

For a simple retrieval tool with three parameters and no output schema, the description sufficiently covers purpose, parameters, and output type. Minor gaps like missing output format details, but overall complete enough.

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 covers 67% of parameters with descriptions. The description adds context for avg_contract_value_usd ('for LTV:CAC calculation') and mentions the role of industry and gtm_motion. Does not elaborate on enum values, so baseline 3 is appropriate.

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

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

The description clearly states it returns JSON CAC payback ranges, LTV:CAC ratio guardrails, and channel efficiency notes, specifying the audience (SaaS CMOs/CFOs) and parameters (industry, gtm_motion, optional ACV). This 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 implies use for SaaS CAC benchmarking but does not explicitly state when to avoid it or mention alternative tools for different verticals. No guidance on prerequisites or limitations.

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

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

No annotations are present, so the description must handle behavioral disclosure. It mentions the data source (quarterly surveys) implying periodic updates, but lacks details on data freshness, authorization needs, rate limits, or error handling. Critical 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?

Three sentences efficiently convey the core function, data source, and use cases. No redundant information; well front-loaded.

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

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

For a data retrieval tool with three enums and no output schema, the description covers purpose, source, and user needs. Lacks specifics on return format or data limitations, but is reasonably complete.

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

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

Schema description coverage is 0%, but the description groups parameters under 'asset class, market tier, and geography' which provides context. However, it does not explain each parameter's meaning beyond the enum values. Partial compensation but not thorough.

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

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

The description clearly specifies the tool provides cap rate benchmarks for commercial real estate, filtered by asset class, market tier, and geography. It differentiates from sibling tools by focusing on cap rates and cites authoritative sources and target users.

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

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

The description implies usage by CRE professionals for pricing and valuation, but does not explicitly state when to use this tool over alternative benchmarks like get_cre_debt_benchmark or get_property_operating_benchmark. No alternative guidance 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?

Discloses key behaviors: returns JSON, limited to 15 results, uses mention_count, has composite fallback (e.g., Salesforce 42 mentions), and is unprompted by category. Lacks details on errors or rate limits, but adequate for a read tool.

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

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

Three sentences, front-loaded with core functionality, each sentence adds value: output description, fallback mechanism, and purpose. 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 parameter and no annotations, description covers purpose, behavior, and fallback. Minor gap: parameter details could be more precise, but overall sufficient.

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

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

Single parameter 'category' is described only as 'by category query' with no format constraints or examples. Schema coverage 0% forces description to compensate, but it provides minimal additional meaning beyond the 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?

Description clearly states the tool returns JSON leaders ranked by mention_count (up to 15) for a given category, specifying resource, verb, and scope. It distinguishes from sibling tools which cover various benchmarks, not 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?

Description implies use for category AI leader rankings but does not explicitly state when to use this over alternatives or when not to use. No exclusions or comparisons provided.

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

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

No annotations provided, so description carries full burden. It discloses the return format (JSON with score and evidence), an example default (~0.55 when sparse), and states it is a static AI disruption radar (not dynamic, not equity research). This adequately conveys behavioral traits for a read-only tool.

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

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

Three sentences, each providing distinct and valuable information: output and audience, example default, and nature clarification. No redundant or unnecessary words, front-loaded with key 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 one parameter, no output schema, and no annotations, the description covers the return format, example default, and boundary conditions. It lacks parameter explanation and interpretation of the score, but is largely adequate for a simple tool.

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

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

Schema description coverage is 0%. The only parameter 'category' is not explained in the description beyond implying it is a product category via 'product strategists'. No examples or format details are given, so the description adds minimal semantic value beyond the schema.

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

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

Description clearly states the tool returns a disruption_risk_score and evidence strings, targeting product strategists, using citation-volume heuristics. The name and content distinguish it from siblings like get_sector_ai_intelligence by specifying the static radar and non-equity research nature.

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 says 'for product strategists' and 'not equity research', providing some context on intended use. However, it lacks explicit guidance on when to use this tool over alternatives or when to avoid it beyond the equity research exclusion.

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 burden. It states 'Returns JSON' implying read-only, gives example values, and clarifies it's a static model not org-specific. It discloses key behaviors 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?

Two sentences plus an example, front-loading the main output. No fluff or repetition. Efficient and clear.

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

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

For a simple benchmarking tool with 3 params and no output schema, the description adequately explains output format, use case, and limitations. Could mention error handling for missing categories, but overall sufficient.

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

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

Schema has 3 params with only category described. Description adds context that company_size is used for benchmarking and gives example output values. It partially compensates for the low schema coverage but does not formally describe each 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 median_monthly_spend, p25/p75, and sample_size for a software category by company_size. It specifies the resource and verb. However, it does not explicitly differentiate itself from similar sibling tools like get_industry_spend_benchmark or get_spend_by_company_size.

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

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

Description mentions it's for finance teams benchmarking by company_size and notes it's an industry composite static model, not org-specific. This implies when to use and when not, but no explicit alternatives or exclusions are provided.

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

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

No annotations are provided, so the description carries the full burden. It mentions 'not legal advice' as a caveat, but does not disclose important behavioral traits such as authentication requirements, rate limits, or whether the tool is read-only. The description is minimally transparent.

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

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

The description is relatively concise at three sentences. However, the third sentence 'Consumer finance complaint surveillance' somewhat repeats the first sentence, and could be integrated for tighter structure.

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 there is no output schema and no annotations, the description should provide more context about the return format, aggregation logic, and any limitations. It only gives high-level purpose, leaving gaps about what the agent can expect from the tool.

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

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

With 0% schema description coverage, the description adds value by stating that 'company_name' is the primary filter and 'product' is optional. However, it does not provide additional details like formats, examples, or constraints that would 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 verb 'Returns', the resource 'CFPB consumer complaint rollup JSON', the target audience 'risk teams', and the key parameters 'company_name' with optional 'product' filter. This distinguishes it from the 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 implies usage for risk teams doing consumer finance complaint surveillance, but does not provide explicit guidance on when not to use or mention alternative tools. The context is implied, not 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?

No annotations provided, so description carries full burden. States it provides live data, implying a read operation. Lacks details on data freshness, rate limits, or error behavior, but is adequate for a simple data retrieval 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?

Single sentence, front-loaded with key purpose and scope. No fluff, every word earns its place.

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

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

Despite no output schema, description lists expected return elements (rankings, changes, protocol counts, dominance, comparison). Sufficient for a simple two-parameter tool. Could mention pagination or default limit.

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

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

Schema description coverage is 0%, so description should explain parameters. Does not mention 'limit' or 'sort_by'. Only hints at scope (50+ chains). Adds minimal value over schema, which already has 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?

Clearly states it provides live TVL by blockchain, lists specific chains (Ethereum, Base, Solana, Arbitrum) and metrics (rankings, 1D/7D change, protocol counts, Ethereum dominance, Base vs ETH). Distinguishes from siblings which are other benchmark tools.

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

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

Implies usage for TVL data retrieval, mentions 'for x402 agent context' but doesn't explicitly state when to use vs alternatives like other benchmark tools. No exclusions or prerequisites.

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

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

No annotations are provided, so the description must carry behavioral disclosures. It does not mention whether the tool is read-only, requires authentication, or has any side effects. Being a 'get' tool implies read-only, but this is not stated.

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

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

The description is two sentences with no wasted words. It front-loads the core concept and includes data sources and audience. However, it could be slightly more structured.

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

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

The description provides source and audience but does not explain output format (e.g., numerical benchmarks, risk scores) or cover all parameter options. For a tool with 3 enum parameters and no output schema, more detail is needed.

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

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

Schema description coverage is 0% and the description does not mention any parameters. Although the enums (region, risk_type, property_type) are somewhat self-descriptive, the description adds no value beyond the schema for understanding 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 climate financial risk benchmarks, listing specific physical and transition risks. It distinguishes from sibling benchmark tools by specifying climate and risk types. However, it does not explicitly state the action (e.g., 'retrieves') and could be more precise.

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

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

The description mentions the target audience ('For ESG and risk agents') but does not specify when to use this tool vs alternatives, nor any exclusions or prerequisites. Among many sibling benchmark tools, no comparative 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?

Annotations indicate read-only and non-destructive; description confirms this. It adds valuable context: composite score, six perils, data sources (NOAA, FEMA, USGS), and a detailed example. No contradictions.

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

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

Three sentences, front-loaded with purpose. The example is detailed and useful but slightly verbose. Could be trimmed without losing meaning.

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

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

For a single-parameter tool with no output schema, the description fully explains input, output (composite risk score across perils), data sources, and provides a comprehensive example. No 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 covers the 'location' parameter with description and example. The tool description reinforces this with a concrete example ('Miami-Dade FL') and hints at expected format, adding modest value beyond schema.

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

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

Description explicitly states verb ('pricing physical climate risk') and resource ('location'), lists specific use cases (real estate acquisition, underwriting, etc.), and clearly distinguishes from sibling tools by focusing on climate risk. No ambiguity.

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

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

Explicitly states when to use ('Use when pricing physical climate risk for a location') but does not mention when not to use or provide alternatives among siblings. The context is clear but lacks exclusion guidance.

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

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

No annotations are provided, so the description carries full burden. It explains that the tool returns JSON and requires no login, implying read-only safe operation. However, it does not explicitly state behavioral traits such as idempotency, data freshness, or rate limits, leaving some gaps.

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

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

The description is two sentences, front-loaded with the main purpose and immediately providing key details (metrics, groupings). No redundant or 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?

For a tool with 3 parameters and no output schema, the description covers the essentials: target audience, data source (CMS HCRIS-style), and key dimensions. It lacks details on return format beyond 'JSON' and data coverage (e.g., years), but is still fairly 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?

With 0% schema description coverage, the description must compensate. It mentions bed_size and state as key filters but does not explain valid values (e.g., state format, bed_size range) and completely omits the hospital_type parameter. This leaves ambiguity for the agent.

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

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

The description clearly states it returns CMS cost report benchmarks, lists specific metrics (IT, labor, supply chain, cost per adjusted patient day), and specifies grouping by bed_size and state. It distinguishes from siblings by explicitly mentioning CMS, HCRIS, and hospital context, making it unique among many benchmark tools.

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

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

The description provides clear context: target audience (hospital CFOs/ops), ease of access (no login), and an example (acute facility cost curve vs peers). However, it lacks explicit when-not-to-use guidance or comparisons to sibling tools like get_ehr_cost_per_bed or get_hospital_supply_chain_benchmark.

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

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

With no annotations, the description must disclose behavioral traits. It correctly indicates a read-only operation returning aggregate data, but lacks details on authentication, rate limits, error handling, or data freshness. The transparency is adequate but not comprehensive.

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

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

Three concise sentences front-load the core functionality ('Returns CMS Open Payments aggregate JSON'), then add context. No fluff or repetition; every sentence earns its place.

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

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

No output schema or annotations exist, so the description carries a heavy burden. It covers purpose and parameters, but omits return structure, pagination, error scenarios, and data provenance. Adequate for basic use but incomplete for complex scenarios.

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

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

The schema has 0% description coverage, but the description adds meaning by naming parameters as filters: 'filtering recipient_name, optional program_year, manufacturer_name.' It clarifies that recipient_name is primary and the others are optional, partially compensating for the missing 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 it returns aggregate JSON for CMS Open Payments, specifies filters (recipient_name required; program_year, manufacturer_name optional), and distinguishes it as Sunshine Act rollups, not individual attestations. This clearly differentiates it from sibling tools like get_cms_facility_benchmark or get_cms_star_rating.

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

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

Usage context is implied ('for compliance teams'), but no explicit guidance on when to use this tool versus alternatives. No when-not-to-use instructions or sibling comparisons are provided, leaving the AI to infer applicability.

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 explicitly states it is static and not live patient-specific data, which helps set expectations. However, without annotations, it omits details on authentication, rate limits, or side effects. It adequately indicates read-only behavior but lacks depth.

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

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

The description is three sentences, front-loaded with a clear statement of outputs, followed by clarifying caveats and value. It is efficient, though the third sentence (education pack) could be integrated into the first.

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

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

The description lacks details on the output structure (fields, nesting) and how to use the optional parameters. For a tool with 3 parameters and no output schema, more information is needed for effective invocation. It does not describe the JSON format beyond naming data types.

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

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

The input schema has three parameters (state, hospital_name, current_star_rating) with no descriptions. The description does not explain their purpose, usage, or how they affect the output. With 0% schema coverage, this is a significant gap.

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

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

The description clearly states it returns CMS Hospital Star methodology notes, domain weights, benchmarks, and an improvement checklist. It distinguishes itself by clarifying it's static, not live patient data, and a free education pack. However, it lists multiple outputs rather than a single verb+resource, slightly reducing clarity.

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

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

No guidance on when to use this tool versus the many sibling tools. It does not specify prerequisites, alternatives, or exclusions, leaving the agent to infer context from the 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?

Annotations indicate readOnlyHint=true and destructiveHint=false, and the description aligns by stating it returns legal information without side effects. It adds transparency by specifying the source, effective date, and penalty structure, which are beyond what annotations provide.

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

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

The description is concise yet rich, covering purpose, use cases, legal details, and an example in a compact structure. 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?

Despite lacking an output schema, the description explicitly lists what the tool returns (obligations, criteria, rights, penalties, comparison) and provides a concrete example. This fully sets expectations for a legal information 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?

The input schema has one parameter with full description coverage (100%). The description does not repeat the parameter definition but implies tailored analysis based on system type. Since schema already covers semantics, the description adds minimal additional value for parameters.

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

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

The description clearly states the tool returns developer and deployer obligations, high-risk AI system criteria, consumer rights, penalty structure, and comparison to EU AI Act for Colorado SB 205. It explicitly mentions use cases like building governance roadmaps and advising on obligations, distinguishing it from sibling tools focusing on other regulations.

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

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

The description provides clear contexts for use: 'when building an AI governance compliance roadmap, advising on high-risk AI deployment obligations in Colorado, or briefing boards on upcoming US state AI regulatory requirements.' While it doesn't explicitly exclude alternatives, it is specific enough to differentiate from other regulatory tools like get_eu_ai_act_coverage.

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 partially carries the behavioral burden. It states the source (FRED) and update frequency (daily), but does not disclose whether the tool returns historical data, handles the optional 'category' parameter, or any limitations. The mention of 'live' suggests real-time, but not fully transparent.

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

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

Three succinct sentences with key information front-loaded. No wasted words, but could be more structured (e.g., separate usage notes). Still efficient and clear.

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

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

Given no output schema and one optional parameter, the description covers purpose, source, update frequency, and target audience. However, it omits parameter details and does not specify if the tool returns raw values or formatted reports, leaving some gaps for a data tool.

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

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

The input schema has one optional parameter 'category' with enum values (energy, metals, agriculture, all) and 0% description coverage. The description lists example commodities but does not explain how the parameter filters results or map examples to categories, failing to add meaning beyond the schema.

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

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

The description clearly states the verb (get), resource (commodity price benchmarks), and specifies commodities (WTI crude, natural gas, gold, copper, wheat, soybeans). It also mentions weekly/monthly price changes and inflation pressure signal, leaving no ambiguity about what the tool does.

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 target users ('For traders and macro analysts') and provides context (source FRED, updated daily), but does not explicitly state when to use this vs. other related benchmarks like get_gas_benchmark or get_inflation_benchmark. No exclusions or alternatives 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?

With no annotations, the description partially discloses behavior by stating the output is JSON aggregates synced from public filing rollups, indicating data source and update method, but lacks details on rate limits, auth, pagination, or error handling.

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

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

Three sentences with no extraneous words; the first sentence packs the core action, context, and parameters efficiently, and the rest adds minimal but relevant detail.

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

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

While the description covers the main purpose and filters, it lacks details on return value structure (fields in the aggregates) and does not mention data range or error scenarios, which is notable given no 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?

Schema coverage is 0%, but the description adds meaning by identifying company_name as required and listing optional filters (job_title, state, fiscal_year), though it does not specify parameter formats or constraints 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 DOL LCA and H-1B wage disclosure aggregates, specifies the primary filter (company_name) and optional filters, and targets compensation analysts, effectively distinguishing it from siblings like get_salary_benchmark or get_employer_h1b_wages.

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

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

No explicit guidance on when to use this tool versus alternatives; it implies use for wage disclosure data but does not mention when to avoid or suggest other tools for different salary information.

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

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

No annotation provided; description mentions fallback behavior and 'switching narrative signal' but lacks details on performance, error handling, or data freshness. Minimal 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 short but contains ambiguous phrases ('May fall back to Microsoft or Google style rows', 'Switching narrative signal'). Could be streamlined without losing meaning.

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

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

Without output schema, the description should fully describe the return structure. It mentions 'top_replacing_vendors with mention counts and evidence_snippets' but lacks specificity. Also, no mention of error conditions or empty 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 description fails to explain the vendor_name parameter. It implicitly references 'target_vendor' but does not clarify that it is a required string. With 0% schema coverage, the description should compensate, but it does not.

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

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

The description clearly states it returns JSON top_replacing_vendors with evidence for competitive intel, differentiating from related tools like get_vendor_alternatives. However, phrases like 'fall back to Microsoft or Google style rows' and 'Switching narrative signal' are ambiguous.

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 vs. alternatives like get_vendor_alternatives. The description vaguely implies use for rip-and-replace tracking but lacks explicit context.

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

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

With no annotations, the description carries the full burden. It discloses the data sources and types of outputs (hard costs, soft costs, contingency, escalation), which helps agents understand the tool's behavior. However, it lacks details on data freshness, response format, or whether the operation is read-only. No contradictions with annotations.

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

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

The description is two sentences, directly stating the tool's outputs and sources, then targeting audience. Every sentence adds value, no fluff. The structure is clear 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?

The description covers overall purpose and sample data types but omits any details about the response structure or format. Since no output schema exists, the description should at least hint at whether the result is a single number, a table, or a report. This gap reduces completeness for an agent that needs to parse the output.

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

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

The input schema has 0% description coverage, so the description must compensate. It mentions 'building type' and 'region' generally but does not explain the 'construction_class' parameter (A, B, C) or the specific meaning of enum values. The description adds little beyond what the parameter names and enums already imply.

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

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

The description clearly states the tool provides construction cost benchmarks including specific metrics like hard cost per SF, soft cost ratios, contingency standards, and material cost escalation. It references authoritative sources (NAHB, Turner, RSMeans) and targets developers, lenders, and owners, distinguishing it from other benchmark tools.

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

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

The description identifies the target audience (developers, lenders, project owners) implying appropriate contexts, but provides no explicit guidance on when to use this tool versus alternatives like get_asc_cost_benchmark or get_development_pro_forma_benchmark. No when-not-to-use or alternative names 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 exist, and the description does not disclose behavioral traits such as readability, update frequency, or authentication requirements, leaving the agent without key 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 a single concise sentence that front-loads the purpose and lists indicators, though it could use slightly more structure.

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 specific indicators but does not describe the output format or data freshness, leaving gaps despite the simple optional parameter.

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

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

The description does not explain the single parameter 'focus' beyond what the enum values imply; with 0% schema description coverage, it fails to add meaning.

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

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

The description clearly states it provides 'Live consumer sentiment benchmarks from FRED' and lists specific indicators, distinguishing it from many sibling benchmark tools by specifying the data source and focus on consumer 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 GDP and equity agents but does not explicitly state when to use this tool versus 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 are provided, so the description must disclose behavioral traits. It describes the tool as providing benchmarks but does not mention any safe/read-only property, permissions, rate limits, or side effects. The read-only nature is implied but not stated.

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

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

The description is two sentences, front-loading the core output and then providing source and usage context. Every sentence adds value with no redundancy.

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

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

The description lacks output schema, so it should describe the return format or structure. It only mentions the metrics available. Given the simplicity of the tool and no annotations, the description is adequate but not complete; more details on output format would improve 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 description mentions both parameters (industry and credit rating tier) as dimensions but adds no further meaning beyond the schema's enum lists. With 0% schema description coverage, the description should elaborate on how to select values or what each tier represents, but it does not.

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

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

The description clearly states it provides corporate leverage and debt benchmarks, specifies exact metrics (Net Debt/EBITDA, interest coverage, debt maturity profiles), and the dimensions (industry, credit rating tier). It also names data sources and typical users, making the purpose highly specific and transparent.

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 usage contexts (refinancing, covenant setting, credit rating management) but does not provide explicit guidance on when not to use this tool or how it compares to sibling benchmark tools. The guidance is implicit, not explicitly comparative.

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

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

Annotations already indicate readOnlyHint=true. The description adds context about the source (FFIEC database) and the impact of specific ratings (e.g., 'Needs to Improve' delays approvals). No contradictions. However, it does not disclose any additional behavioral traits such as rate limits or data freshness.

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

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

The description is concise (3-4 sentences) and front-loaded with the use case. It uses bold for key terms and provides an example. Every sentence adds value, though it could be slightly more streamlined.

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

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

For a simple lookup tool with one parameter and no output schema, the description covers purpose and context well. It mentions the source and provides a meaningful example. However, it lacks description of the output format or possible values, which would aid completeness.

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

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

Schema description coverage is 0%, so the description must compensate. However, the description does not explicitly describe the 'institution_name' parameter. While the example suggests an institution name, the lack of parameter description means the agent must infer meaning, leaving room for error.

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

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

The description clearly states the tool retrieves CRA performance ratings for banks, specifying the use case (evaluating track record before mergers, etc.) and listing possible ratings. It includes an example, making the purpose unambiguous and differentiating it from other 'get_*' tools.

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

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

The description explicitly advises when to use the tool: before mergers, acquisitions, branch expansions, or community lending partnerships. It does not mention when not to use or provide alternatives, but the context is clear and sufficient for the intended use case.

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

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

With no annotations, the description explains the output content (DSCR, LTV, spreads) and data sources (MBA CREF, Trepp), which is adequate. It does not detail update frequency or authentication, but the read-only nature is inferred.

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

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

Two concise sentences with no redundancy. First sentence defines the tool's purpose and content, second adds audience and source. 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 and no annotations, the description reasonably covers what data is returned (three metrics) and filtering dimensions. It could clarify output format but is sufficient 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 0%, but description mentions parameters indirectly ('by property type and lender type'). It adds meaning by explaining they are filtering dimensions, but lacks detail on enum values or defaults.

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

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

The description clearly states the tool provides 'Commercial real estate debt benchmarks — DSCR minimums, LTV maximums, and spread ranges' by property and lender type, specifying the resource and actions. It distinguishes from siblings like get_cap_rate_benchmark by focusing on debt 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?

It indicates the target audience ('CRE CFOs and capital markets teams structuring financings'), implying usage context. While no explicit when-not or alternatives, the specificity implicitly differentiates it from other benchmark tools.

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

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

States 'Live' and 'Updates daily', implying a read-only, non-destructive operation. However, with no annotations provided, the description fails to disclose potential rate limits, authentication requirements, or what happens when parameters are omitted. Adequate but not comprehensive.

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

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

Two concise sentences: first explains what the tool provides and from where, second adds update frequency and target audience. No extraneous words, easy to scan.

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

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

For a simple tool with one optional parameter and no output schema, the description covers key elements: data source, metrics, freshness, and audience. Minor gap: does not describe output format, but that is acceptable given the nature of the tool.

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

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

The only parameter (rating_tier) has enum values but is not mentioned in the description. With 0% schema description coverage, the description should compensate, but it adds no meaning beyond the enum names. An agent might not know that filtering by rating tier is possible or how it works.

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

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

Clearly states the tool provides credit spread benchmarks from FRED ICE BofA indices, listing specific metrics (OAS, TED spread, 2s10s spread, distress signal). Distinguishes from sibling benchmark tools by focusing on credit spreads and mentioning target users (credit analysts, fixed income PMs).

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?

Identifies target users but does not explicitly contrast with sibling tools like get_corporate_debt_benchmark or get_inflation_benchmark. Lacks guidance on when to use or not use this tool over alternatives, leaving the agent to infer from context.

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

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

No annotations are provided, and the description does not mention behavioral traits such as read-only nature, potential side effects, rate limits, or data freshness. It implies the tool is safe but fails to explicitly disclose behavior beyond listing data content.

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. No redundant information. Could include more detail without being 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?

No output schema and no annotations. The description lists metrics but does not describe the return format, structure, or whether results are aggregated. For a tool with only two parameters, more detail on expected output is needed to be 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 add meaning. It explains 'by asset size' for the required parameter but does not mention charter_type at all. This partial coverage leaves a significant gap for the optional parameter.

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

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

The description clearly states the tool returns credit union financial performance benchmarks with specific metrics (capital ratios, net interest margin, loan growth, delinquency rates) and specifies the source (NCUA quarterly call report). It distinguishes from many sibling benchmark tools by focusing on credit unions.

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

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

The description provides usage context ('For credit union CFOs preparing for NCUA exams and board reporting') but does not explicitly state when to use this tool over alternatives or when not to use it. No sibling differentiation 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 present, so the description should compensate. It mentions the output (correlation matrix, beta, etc.) and data source, but lacks behavioral details such as auth requirements, rate limits, data freshness, or whether the request is read-only.

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

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

The description is two sentences long, front-loaded with the main output and source. It is efficient and easy to scan, though it could better incorporate the parameter.

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 components, but doesn't detail format or structure. The undocumented parameter is a gap. The sibling context shows no overlap, so completeness is adequate but not thorough.

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

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

The schema has one optional parameter ('period') with enum values, but the description does not mention it. Moreover, the description says '30-day rolling' while the parameter allows 7d and 90d, which is misleading. With 0% schema description coverage, the description fails to compensate.

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

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

The description clearly states the tool computes a 30-day rolling correlation matrix for BTC, ETH, and SOL, listing specific outputs like Pearson correlation pairs and beta to BTC. It distinguishes itself from siblings by being crypto-specific, though it implies a fixed 30-day period while the parameter allows 7d and 90d.

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

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

The description says 'For crypto portfolio agents', giving an implied use case. No explicit guidance on when to use vs. alternatives is provided, but the sibling list contains no other crypto correlation tool, so context is clear enough.

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 describes the content (benchmarks) but does not disclose behavioral traits such as whether it is read-only (obvious), rate limits, or any side effects. No additional context beyond the 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 with no extra words, front-loading the purpose and providing example data for context. It is concise but could be slightly more structured to include parameter hints.

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

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

Given the absence of an output schema, the description should explain the return format. While it mentions median benchmarks, it does not describe whether the output is a single object or a list, nor does it detail other fields. Incomplete for a data tool with no 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?

Schema description coverage is 0%, and the description does not explain the two parameters (sort_by, min_treasury_usd). It adds no semantic meaning beyond what is in the schema, leaving agents unaware of how to use these filters.

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

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

The description clearly states the tool provides DAO treasury benchmarks, listing specific metrics (treasury size, stablecoin percentage, runway, governance token concentration) and giving median example values. It differentiates from sibling tools which cover other benchmarks across various 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 DAO treasury data but provides no explicit when-to-use or when-not-to-use guidance. Among many sibling benchmark tools, there is no differentiation or alternatives 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 provided. Description discloses free public API, no key required, and Ed25519-signed response. Missing details on rate limits, error behavior, or data freshness. Partial transparency – covers key access and authenticity but not operational 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 short, focused sentences. Front-loaded with purpose, followed by filter details and then access/signature. No unnecessary words or repetition.

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

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

Describes output fields (APY bands, TVL, chain, pool id), filter options, default filter behavior, and response signing. No output schema exists, so description compensates well. Missing potential details like result limit or sorting, but still adequate for a simple data fetch 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. The tool description paraphrases the schema (e.g., 'protocol (project slug substring)') without adding significant new meaning. Baseline 3 is appropriate as schema carries the burden.

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?

Explicitly states it provides DeFi lending and stable yield benchmarks from DeFiLlama Yields, including APY percentiles, TVL, chain, and pool id. Clearly distinguishes from siblings like get_stablecoin_yield_benchmark by specifying the DeFiLlama source and lending 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?

Describes optional filters (protocol, asset) and default universe (stablecoin-marked pools). Provides clear context for use, but does not explicitly contrast with related tools like get_stablecoin_yield_benchmark or suggest when not to use.

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

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It does not mention authentication requirements, rate limits, data freshness, or whether the operation is read-only. The only behavioral hint is the data sources (NAHB, ULI), which implies the data is sourced from industry composites but does not guarantee timeliness or access constraints.

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

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

The description is three sentences, front-loading the key metrics and audience. Every sentence adds value: listing metrics, specifying users, and citing sources. No wasted words.

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

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

Given the tool has 2 parameters, no output schema, and no annotations, the description adequately covers the tool's core function and intended use. However, it lacks behavioral details (e.g., response format, pagination) that would aid an agent in fully understanding the tool's capabilities and limitations.

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

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

The input schema has 2 parameters (market_tier enum, product_type enum) with 0% description coverage. The description mentions 'by product type' but does not elaborate on market_tier or how parameters affect results. While the enum values are self-explanatory, the description adds minimal semantic value 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 clearly specifies the tool's purpose: providing development pro forma benchmarks (yield on cost, profit-on-cost, etc.) for developers and lenders. It distinguishes itself from sibling benchmark tools like get_construction_cost_benchmark by focusing exclusively on development pro forma metrics and stating the target audience.

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

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

The description explicitly states when to use the tool: for developers underwriting new projects and lenders sizing construction loans. While it does not list alternatives or exclusions, the context is clear enough for an agent to infer appropriate use cases against related 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?

Annotations already indicate read-only and non-destructive. The description adds behavioral context: returns enforcement history with back wages and employees affected, mentions repeat violations as a class action predictor, and gives an example. This goes beyond the annotations.

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

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

The description is a concise paragraph of 6 sentences, front-loaded with the use case. Each sentence adds value, including an example and source attribution. No unnecessary repetition, though could be slightly more streamlined.

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

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

Given the tool's simplicity (2 params, no output schema), the description is comprehensive. It explains purpose, data sources, violation types, return elements (back wages, employees affected), and predictive value of repeat violations. No output schema exists, but description compensates well.

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 implies required parameter 'employer_name' through the use case and example, but does not explicitly describe the 'state' optional parameter or constraints. Provides some context but not full parameter semantics.

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

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

The description clearly states the tool is for screening employers for wage and hour compliance risk, specifying the verb 'screening' and resource 'employer, vendor, or acquisition target'. It details the specific data returned (DOL WHD enforcement history, violation types) and distinguishes itself from siblings by focusing on labor violations, not benchmarks.

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

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

The description explicitly states when to use the tool: 'before a contract award, supply chain partnership, PE acquisition, or HR due diligence review'. It does not mention when not to use it or provide alternatives, but the context makes it clear this is the dedicated labor violations 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 convey behavioral traits. It implies a read-only operation by providing benchmarks from public data, but it does not explicitly state that it is non-destructive, does not mention authentication needs, rate limits, or data freshness. The lack of any behavioral detail leaves uncertainty.

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 extraneous words. The first sentence packs the core purpose and metrics, the second adds use case and source. Front-loaded with key information, every sentence earns its place.

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

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

There is no output schema, so the description should describe the return format. It mentions metrics but does not specify whether the output is a single number, a table, or ranges. The revenue_recognition_model parameter is left unexplained, and the tool's behavior with optional parameters is unclear. The description lacks completeness for effective use.

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

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

The schema has two parameters (sector and revenue_recognition_model) with 0% description coverage. The description does not explain the meaning or impact of these parameters beyond mentioning 'by sector' implicitly. It fails to clarify what revenue_recognition_model is or how it affects the output, leaving the agent to guess.

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 citing authoritative sources (SEC EDGAR, Sloan accruals model). It distinguishes itself from siblings by focusing on earnings quality specifically, though it does not explicitly differentiate from other benchmark tools.

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

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

The description explicitly targets CFOs, auditors, and analysts assessing financial reporting risk before M&A or investment, providing clear context for when to use the tool. However, it does not include when-not-to-use guidance or alternative tools, which would be helpful given the long 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?

The description explains that the tool returns JSON data and that passing bed_count and annual_cost yields a gap metric, but it does not address data freshness, error conditions, or prerequisites (e.g., authentication). As no annotations are provided, the description carries the full burden.

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

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

The description is very concise with two sentences and a reference line, front-loading the key information and avoiding unnecessary 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?

The description covers the core return data and optional behavior, but it lacks details on the exact format of the output, error handling, and limits of the benchmark. Given no output schema, more explanation 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 description adds meaning to the parameters by explaining that bed_count and annual_cost trigger a gap calculation and hints at vendor_name via the Epic example. However, it does not fully clarify all parameter constraints or types, and with 0% schema coverage, the compensation is partial.

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

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

The description clearly states the tool returns benchmark_cost_per_bed_median and an optional gap_vs_benchmark, with an explicit example and source citation. It effectively distinguishes from sibling tools by focusing on EHR cost 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 implies use for health IT finance and mentions when to pass optional parameters for a gap calculation, but it does not provide explicit guidance on when to choose this tool over alternatives or list 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 fully handles behavioral disclosure: it reveals the API key dependency, potential empty response, and non-realtime nature. Additional detail on data freshness or rate limits would push it to 5.

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 purpose, usage conditions, and limitations. Every sentence adds unique information with no redundancy, front-loading the key action.

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 covers the essentials: purpose, output type, authentication condition, and real-time caveat. It could specify the JSON structure slightly more, but overall it is 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?

There are no parameters (schema coverage 100% with zero properties), so the baseline is 4. The description adds value by describing the output format and constraints 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 'EIA spot-price style JSON' for 'commodity strategists', specifying both the resource and audience. It distinguishes from siblings by being parameterless and energy-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?

It explains when the tool works (server API key configured) and when it doesn't (empty/guidance), plus clarifies it is not realtime. No alternatives are named, but the context of a 0-param tool makes usage straightforward.

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. Description does not contradict and adds context about outputs (position, targets, invalidation, confidence) but does not disclose additional behavioral traits like data freshness 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 sentences with no filler. First sentence states what it provides, second gives an example and target audience. Every element earns its place.

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

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

Adequately covers purpose, assets, and output types for a simple read-only tool with one parameter and no output schema. Example adds clarity, though could specify output format more precisely.

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 (asset) with schema description covering 100%. Tool description does not add further meaning beyond what the input schema already provides.

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

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

Description clearly states the tool provides Elliott Wave position, targets, invalidation levels, and confidence scores for specific assets (BTC, SPY, TLT, Gold). Includes an example, making the purpose unmistakable and distinct from siblings.

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

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

Identifies target users ('traders and portfolio managers tracking technical market structure') and implies use cases. Does not explicitly state when to use versus alternatives or when not to use.

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

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

No annotations provided, so the description carries full burden. It mentions returning JSON but does not disclose rate limits, data freshness, authentication requirements, or whether the operation is read-only. Lacks detail beyond return type.

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

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

Two sentences plus a phrase, all relevant. Front-loaded with key information. Could be slightly more structured but 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 one-parameter tool with no output schema, the description explains what data is returned (stats, job titles, state mix) and the data source (DOL LCA filings). Adequate for basic understanding, but lacks detail on output format.

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

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

Schema coverage is 100% with parameter description providing examples. The description adds 'querying DOL LCA filings by employer_name', which provides context but no 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 it returns 'JSON prevailing wage stats, certified job titles, state mix' for H-1B compensation intelligence, querying DOL LCA filings by employer_name. This specific verb and resource distinguishes it from siblings like get_salary_benchmark or get_company_salary_disclosure.

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 'HR analytics and talent strategy teams' and mentions querying DOL LCA filings by employer_name. It implies usage context but does not explicitly state when not to use or provide alternatives, though the sibling list gives implicit differentiation.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does not mention read-only nature, authentication needs, rate limits, or side effects. It does list data sources, adding minor transparency, but overall lacks essential behavioral context.

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

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

The description is two sentences, front-loaded with the tool's purpose and key metrics, followed by sources and target audience. Every sentence adds value, and there is no extraneous information.

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

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

Given no output schema, the description provides a good overview of the data (carbon intensity, scores, etc.) and sources, which helps set expectations. It lacks specifics on output format or structure, but for a simple benchmark retrieval tool with two optional parameters, the description is reasonably complete.

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

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

The input schema has two enum parameters (focus, sector) with 0% description coverage. The description adds meaning by listing ESG metrics and mentioning 'by sector', which maps to possible parameter values. However, it does not explicitly explain each parameter, leaving some ambiguity.

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

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

The description clearly states it provides ESG benchmarks by sector, listing specific metrics like carbon intensity, net zero commitments, etc. It distinguishes from sibling benchmark tools through its ESG focus, though it does not explicitly highlight differences from other domain-specific benchmarks.

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

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

The description targets sustainability agents and ESG analysts, implying use for ESG analysis. However, it offers no explicit guidance on when to use this tool versus alternatives, no when-not-to-use scenarios, and no comparisons with 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 present, so the description carries full burden. It mentions 'composite mode' and 'control library context' but does not disclose behavioral traits like read-only nature, required permissions, or whether it modifies state. The description is vague on what the tool actually does beyond providing reference.

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

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

The description is a single sentence, but it is dense with jargon ('composite mode', 'control library context') that reduces clarity. It could be restructured to be more accessible while retaining 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?

With no output schema and only one optional parameter, the tool is simple, but the description does not explain what the tool returns or how to effectively use the parameter. It leaves the agent guessing about the output format and the significance of the nistFunction enum.

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, nistFunction, has an enum but is not described in the text. With 0% schema description coverage, the description should compensate but fails to add any meaning beyond the enum values. The agent must infer the parameter's role from the tool's title and context.

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

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

The description states it provides 'EU AI Act framework reference coverage in composite mode with control library context and implementation guidance', which clearly indicates the tool's focus on EU AI Act coverage. However, it does not distinguish explicitly from sibling tools, missing an opportunity to highlight unique value.

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

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

No guidance is provided on when to use this tool versus alternatives. The sibling list is long, but the description lacks any contextual cues about preferred use cases or exclusions.

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

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

Annotations already indicate read-only operation. The description adds that it returns recall classifications with reasons and mentions mandatory press releases for Class I. No contradiction, but lacks details on data recency or limitations.

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

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

Two paragraphs with clear usage, return values, and an illustrative example. Could be more structured, but no wasted sentences.

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

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

No output schema, but description explains return values with classification definitions. Includes source and example, making it complete for a simple reporting tool.

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

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

Schema description coverage is 0%, so description must compensate. It implies company_name as entity and product_type via example, but does not explicitly describe parameters or their formats beyond what 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 states the tool returns FDA recall classifications for evaluating companies. It specifies use for pharmaceutical, medical device, or healthcare vendors, clearly differentiating from sibling 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?

Explicitly states when to use the tool (evaluating product safety risk, etc.) and describes recall classes. However, it does not mention when not to use or provide alternative tools.

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

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

No annotations provided, so description carries full burden. It only mentions 'returns JSON' and 'USASpending-synced', omitting details on data freshness, rate limits, authentication, 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?

Three sentences, first sentence provides main purpose and key parameters. Minor redundancy in the third sentence, but overall 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 no output schema and no annotations, the description covers the basic return type and key parameters. However, lacks details on data scope, pagination, and constraints, leaving some gaps for a 5-parameter 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 low (20%), and the description lists optional filters (agency_name, naics_code, fiscal_year) adding some meaning, but does not fully compensate for the missing parameter descriptions. The 'state' parameter is already described in 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 USASpending-synced obligation rollups JSON for government affairs teams, with required vendor_name and optional filters. It differentiates from sibling tools in other domains.

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

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

No explicit guidance on when to use this tool versus alternatives like get_vendor_contract_intelligence or get_industry_spend_profile. No when-not-to-use or contextual 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?

Annotations declare readOnlyHint=true and destructiveHint=false, which the description aligns with by describing read-only retrieval of case details. The description adds value by listing returned fields (case names, docket numbers, etc.) and the data source (CourtListener). No contradictions.

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

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

The description is well-structured and front-loaded with the use case, followed by details and an example. It is concise but comprehensive, with minimal redundancy.

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

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

Given the complexity and no output schema, the description covers the tool's purpose, when to use it, return fields, and data source. It lacks discussion of limitations (e.g., federal only, not state), but overall it provides sufficient context for an AI agent.

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

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

Schema description coverage is only 33% (only court has a description). The description does not explicitly describe the parameters party_name or years_back. While it implies party_name via the screening context, it offers no guidance on format or usage, failing to compensate for low schema coverage.

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

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

The description clearly states the purpose: screening companies/executives/vendors for federal litigation exposure. It specifies the scope (active and historical federal court dockets across all US district and appellate courts) and provides a concrete example, distinguishing it from sibling tools that focus on financial benchmarks or other regulatory data.

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

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

The description explicitly states when to use the tool: 'before a contract award, acquisition, investment, board appointment, or enterprise partnership.' It does not provide explicit when-not-to-use guidance or alternatives, but the context of sibling tools makes the specialization clear.

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

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

With no annotations, description discloses illustrative nature, data source (FRED), and non-market-implied property. Adequate for a read-only, parameterless 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?

Single sentence front-loads main output and qualifies scope. No unnecessary words; every phrase 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?

Describes output (probabilities for 3 meetings), purpose (educational), and data source. No output schema, but format is implied by 'JSON illustrative cut, hike, hold probabilities.' Minor gap: doesn't specify exact structure (e.g., percentages vs decimals).

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; schema coverage 100%. Description adds context about underlying data (FRED print) but not needed for 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?

Clearly states returns illustrative cut/hike/hold probabilities for next three meetings for macro educators, distinguishing from market odds. Verb 'returns JSON' specifies resource and output format.

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

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

Explicitly says when to use (for scenario planning, not futures-implied odds) and conditions (based on FRED print). Lacks direct mention of sibling tools but context is clear enough.

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. Description adds valuable context about FTC consent orders (duration, penalties, binding on acquirer) without contradicting annotations.

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

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

Description is about 80 words, well-structured with example and source. Efficient but could be slightly tighter without losing key information.

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

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

No output schema, but description provides enough context via example (consent order details) for an agent to understand expected return structure. Low complexity with only one parameter makes it sufficient.

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

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

Schema has 1 required parameter (company_name) with 0% description coverage. Description implies company_name via example 'Tech Platform Corp' but lacks format guidance (e.g., exact name vs. ticker). Adds some meaning but insufficient to fully compensate.

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

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

The description clearly states it retrieves FTC enforcement history for antitrust and consumer protection evaluation. It uses a specific verb and resource, distinguishing it from sibling tools like get_occ_enforcement_actions.

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

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

Explicitly lists when to use (antitrust exposure, consumer protection liability, etc. before acquisitions or vendor selection). Provides a detailed example but does not explicitly state when not to use.

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 responsibility. It states 'Live' and 'Updated daily,' implying freshness, but does not disclose authentication requirements, rate limits, or any side effects. For a read-only tool, this is adequate but could be improved.

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

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

Two sentences, no filler, front-loading the key information: what the tool provides, specific metrics, source, and update frequency. Every word earns its place.

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

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

Given the tool's simplicity (one optional param, no output schema), the description covers the main purpose, data scope, and source. It is nearly complete but could note that the parameter is optional and what the default behavior is.

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

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

The input schema has one optional parameter (base_currency) with enum values, but the description does not mention this parameter at all. With schema description coverage at 0%, the description should compensate by explaining that base_currency filters the benchmark pairs, but it only lists examples without linking to the parameter.

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

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

The description explicitly states it provides 'Live major currency pair benchmarks' and lists specific pairs (USD/EUR, USD/JPY, etc.) and derived metrics (carry trade spread, rate change), clearly distinguishing it from sibling tools like get_commodity_benchmark or get_inflation_benchmark.

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

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

The description indicates usage context (major currency pairs, source FRED, daily updates) but does not explicitly state when not to use it or mention alternative tools. The context is clear but lacks exclusion guidance.

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

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

Without annotations, the description carries full burden. It states 'live' data, source (public chain RPCs), and that no API key is needed, indicating read-only and safe operation. Missing details on rate limits or error handling, but adequate for a read benchmark.

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 redundancy. Front-loaded main purpose, then lists return types, then adds contextual details (source, no API key). 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 simple parameter and no output schema, description covers purpose, return types, and usage requirements (zero API key). 'x402 agent economy context' could be clearer, but overall sufficient for an agent to understand the tool's function.

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

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

Only one parameter (chain) with enum values. The description adds meaning by naming the three chains and hinting at 'Base vs ETH savings comparison,' which informs parameter usage. Schema coverage is 0%, so this compensation is effective.

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

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

The description clearly states the tool provides 'live gas price benchmarks' for specific chains (Ethereum, Base, Solana) and lists return values like Gwei and USD cost. It distinguishes from sibling tools, which are various benchmarks but none focused on gas prices.

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

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

Usage context is implied by the tool's specific purpose (gas prices). No explicit guidance on when to use or avoid, nor alternatives mentioned. The specialization makes it clear, 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, the description must disclose behaviors; it mentions 'live' data via public API and notes rate limits. However, it does not clarify whether authentication is needed, what constitutes a valid 'org_or_company' (e.g., exact GitHub login), or how error cases (e.g., invalid org) are handled. The transparency is adequate but not comprehensive.

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

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

The description is extremely concise—three sentences totaling 24 words—with no redundant phrases. It front-loads the core function and then adds important caveats (rate limits, target audience). Every sentence contributes meaningful 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?

Without an output schema, the description should outline what the returned JSON contains (e.g., specific stats fields). It only vaguely says 'organization and repository stats' and 'Open-source ecosystem snapshot', leaving agents uncertain about the data structure. The completeness is insufficient for reliable 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 sole parameter 'org_or_company' has no schema description, but the description adds 'footprint query' suggesting it expects a GitHub organization or company name. This provides basic semantic context but lacks details on the expected format or examples. Given 0% schema description coverage, the description partially compensates but could be more explicit.

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

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

The description clearly states that the tool returns 'live GitHub organization and repository stats JSON' for developer relations teams via a public API. It specifies the resource (GitHub stats), format (JSON), and audience, making its purpose distinct among the many sibling tools that focus on other domains like finance, healthcare, etc.

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 audience (developer relations) and 'org_or_company footprint query', but provides no explicit guidance on when to use this tool versus the many alternative tools in the sibling list. There is no 'when to use' or 'when not to use' advice, leaving agents to infer usage based on the topic 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 exist. The description does not disclose data freshness, update frequency, or any behavioral traits like whether results are real-time or delayed. It also fails to mention what happens for unsupported regions (though enum restricts input).

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—one sentence plus a list. It front-loads key indices and data points. However, it could be restructured to separate the list of indices from the data fields for better readability.

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

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

The description lacks details about the output format (e.g., whether it returns a table, numbers, or signals). Given the absence of an output schema and multiple sibling tools, more context is needed 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?

The input schema has 0% description coverage. While the description implies coverage by region (e.g., us, europe), it does not explicitly map region values to the listed indices, leaving ambiguity about which indices correspond to which region.

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 lists specific indices (S&P 500, Nasdaq, etc.) and data points (YTD returns, P/E ratios, risk signal), making it distinct from sibling tools like get_cap_rate_benchmark or get_macro_market_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 provides no guidance on when to use this tool versus alternatives such as get_public_market_multiples or get_macro_market_signal. No exclusion criteria or context is given.

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

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

With no annotations, the description discloses important behavioral traits: it returns a static composite model, not live data. It mentions the output is JSON and the approximate values. However, it does not cover parameter behavior or potential dependencies, missing some 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 relatively concise, fitting key information into a few sentences. It front-loads the primary output values and then explains the model type. Some reorganization could improve clarity, but it is not verbose.

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

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

Given no output schema and a single optional parameter, the description should fully cover both. It partially covers the return value but leaves the parameter's role ambiguous. It also does not explain default behavior when category is omitted. This makes the tool less complete for an agent unfamiliar with GPO benchmarks.

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

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

The input schema has one optional string parameter 'category' with 0% description coverage. The description mentions 'top categories' but does not explain what the parameter does, expected values, or how it affects the output. This fails to compensate for the schema gap.

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

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

The description clearly states that the tool returns JSON with typical GPO savings percentages, leakage, risk threshold, and top categories for materials managers. It specifies the model is static and for acute care, effectively distinguishing it from other benchmark tools.

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

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

The description provides usage context by noting it is a static HFMA plus CMS-style composite, not from user invoices. This helps the agent understand it's not for real-time or custom data. However, it does not explicitly contrast with alternative sibling benchmarks, 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 the full burden. It discloses the output structure and data sources, which adds value. However, it does not mention any behavioral traits such as read vs. write intent, potential cost, latency, or required permissions. The description is partially transparent but lacks critical 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 short but poorly structured. The first sentence is a run-on listing outputs. The following two fragments ('Healthcare vendor narrative scan. Category recommendation audit.') are not full sentences and add confusion. Every sentence should earn its place, and these fragments are vague.

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 single parameter, lack of output schema, and many similar siblings, the description is incomplete. It does not explain what the category parameter expects, nor does it provide usage examples. The output fields are named but not described. The fragmentary extra sentences add little 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 schema has only one parameter (category) with no description and 0% coverage. The description adds minimal value; it mentions 'healthcare category' implicitly but does not specify expected values, format, or examples. The agent must infer that category is a text string identifying a specific healthcare domain.

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

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

The description clearly states that the tool returns JSON with top_recommended_vendors, ai_consensus blurb, and sample_size for health IT strategists. It mentions specific data sources (AI citations or Epic/Meditech defaults), which distinguishes it from generic category tools. However, the verb 'returns' is passive and the description could more explicitly differentiate from siblings like 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 is provided on when to use this tool versus alternatives like get_top_vendors_by_category or get_category_ai_leaders. The description lacks any exclusions, prerequisites, or context that would help an agent choose appropriately 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 provided. The description mentions data sources (CMS, Stratalize composite) and hints about structure (e.g., maintenance $/bed). Lacks details on behavior like rate limits, mutability, 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?

Single sentence, front-loaded with main purpose. Could be slightly more structured but 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, no annotations. Description lacks return format details, prerequisites, or error handling. Example categories help but are insufficient for completeness.

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

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

Schema coverage is 0%, so description must add meaning. It lists example categories for the optional 'category' parameter, but does not describe 'vendor_name' format or constraints. Adds marginal value.

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

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

The description clearly states it returns market-rate payload for healthcare vendor categories, listing examples like EHR and staffing. It differentiates from generic benchmark tools, but could be more precise about the exact resource.

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 other similar tools like 'get_vendor_market_rate' or other benchmarks. Does not 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 full burden. It states the tool returns JSON but lacks detail on permissions, rate limits, error handling, or response structure beyond that it includes example fields. This is 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?

The two-sentence description is moderately concise but dense with a list of metrics in the first sentence. The second sentence adds little value ('Quality transparency'). Could be more tightly written.

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 single parameter and no output schema, the description covers the main output categories but omits details like field names or structure. It is adequate but not comprehensive for a tool that returns specific health data.

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 a clear description for hospital_name. The description adds context (synced public data, example fields) but does not significantly enhance the schema's own explanation. Baseline 3 is appropriate.

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

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

The description clearly states the tool returns JSON CMS Hospital Compare quality scores for clinical ops leaders, specifying metrics like safety, readmissions, patient experience, and star rating. This distinguishes it from many siblings, but does not explicitly differentiate from similar tools like get_cms_star_rating.

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

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

The description implies use by clinical ops leaders and mentions synced public data, but provides no explicit guidance on when to use this tool versus alternatives (e.g., get_cms_star_rating) or when not to use it.

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

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

No annotations are provided, so the description carries the full burden. It discloses the data source (CMS HCRIS), the use of a median fallback, and the peer comparison. However, it does not mention rate limits, authentication requirements, 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 a single sentence that efficiently packs key information: output format, metric, filters, data source, fallback, and comparison. Every phrase adds value with no waste.

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

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

The description covers the output structure (JSON with p25/p50/p75) and mentions the fallback and peer comparison. However, it lacks an example output or details on JSON keys. Given no output schema, a bit more detail 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 description mentions bed_size and state as filters, adding meaning beyond the schema's type-only definitions. However, it does not specify valid ranges for bed_size or state format, leaving ambiguity for the agent.

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

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

The description clearly states the tool returns JSON supply cost percent of operating expense percentiles (p25/p50/p75) for hospital CFOs, filtered by bed_size and state, with a median fallback. It identifies the specific resource and differentiates 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 hospital supply chain benchmarking, but lacks explicit guidance on when to use this tool versus alternatives like get_industry_spend_benchmark. No prerequisites or exclusions 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 mentions data sources (FRED and Census) and that it's a leading indicator, but lacks details on behavioral aspects such as read-only nature, rate limits, authentication needs, or any side effects. The name suggests a read operation, but more disclosure is expected.

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

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

The description is three sentences, front-loading the key indicators and sources. It is concise, with no wasted words, and effectively communicates the tool's essence.

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

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

While the description covers the tool's domain and purpose, it omits details on return format or content. Without an output schema, the agent lacks clarity on what the tool returns. The description is adequate but not fully complete for an agent to understand expected output.

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

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

Input schema has two parameters (region, structure_type) with 0% description coverage. The description mentions 'by market tier' but does not clearly explain the parameters or their enum values, leaving the agent to infer from the schema alone. The 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 the tool provides live housing supply indicators (starts, permits, completions, absorption) from FRED and Census, and identifies it as a leading indicator for housing prices. It distinguishes itself from sibling benchmark tools by specifying the exact data domain, though it does not explicitly contrast with siblings.

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

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

The description lists target users (developers, lenders, investors, policy analysts), implying context of use. However, it does not explicitly state when to use this tool vs alternatives or when not to use it, 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?

No annotations provided, so description carries full burden. It mentions source (HUD annual FMR dataset) and that it's free, but fails to disclose whether it is read-only, what happens on missing inputs, data freshness, or any side effects. Barely adequate.

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

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

Three short sentences: purpose, use cases, source/cost. No fluff, all details relevant. 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?

Covers purpose, use cases, source, and cost. However, with no output schema, the description should hint at the return format (e.g., a number or object). Missing that, so incomplete for a data retrieval tool.

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

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

Schema coverage is 50%; metro_area has an example format, bedroom_count lacks description. The description adds context that data is 'by metro area and bedroom count' but does not elaborate on valid values for bedroom_count beyond what schema says (0-4). Marginal improvement.

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

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

Description clearly states it provides HUD Fair Market Rents by metro area and bedroom count, listing specific use cases (affordable housing underwriting, Section 8 compliance, LIHTC calculations, housing authority budgeting). This distinguishes it from sibling tools like get_rental_market_benchmark and get_residential_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?

Explicitly states when to use: for affordable housing programs and compliance. Provides clear context but does not mention when not to use or list alternative 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 provided, the description fully relies on itself. It discloses the tool is static, curated, and returns JSON. For a read-only tool with no parameters, this is adequate, though it could mention data freshness 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 three concise sentences that efficiently convey purpose, distinction, and context. 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 parameters, the description adequately explains what the tool returns and its nature. However, it does not specify the exact structure of the JSON output, which may leave some ambiguity for an agent needing precise field names.

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

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

There are zero parameters, so schema coverage is 100%. The description adds context about the output nature beyond the schema, earning a baseline of 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 specifies the tool returns IMF World Economic Outlook-style static macro composites JSON for economists, mentioning curated tables and distinguishing it from live IMF API pulls or country desks. It also explicitly states it provides a global GDP and inflation reference snapshot, making the purpose highly specific.

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

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

The description implies when not to use (not for live API pulls or country desks), but does not explicitly state when to use over alternatives among the many sibling tools. The usage context is fairly clear for a niche tool.

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