Stratalize Healthcare

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds value by detailing the output (classification with criteria and phases) and providing an example behavior for the EVOLVING stage. No contradiction.

Agents need to know what a tool does to the 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 front-loaded with the key purpose and usage guidance. It includes a helpful example but has some redundancy (e.g., 'not org-specific scoring' mentioned twice). Still efficient overall.

Shorter descriptions cost fewer tokens and are easier for agents to parse. 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 fully explains the return values (INITIAL, MINIMAL, EVOLVING, EMBEDDED with criteria and priorities) and provides context for governance assessment. Complete for a read-only tool with no parameters.

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

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

The input schema has zero parameters, so coverage is 100%. The description does not need to add parameter info. Baseline for 0 params is 4, and the description is adequate.

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

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

The description clearly states the tool returns FS AI RMF reference data with a classification (INITIAL, MINIMAL, EVOLVING, EMBEDDED) and distinguishes itself from org-specific scoring by noting 'Connect org MCP for org-specific scoring.' This explicitly separate it from sibling tools.

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

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

Explicitly states when to use: 'assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation.' Also tells when not to use (for org-specific scoring) and directs to an alternative ('Connect org MCP').

Agents often have multiple tools that could apply. Explicit usage 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 (safe read). Description adds what it returns (consensus, sentiment, themes, breakdown) without contradicting annotations. No additional behavioral info needed.

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

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

Two clear sentences plus an example. Front-loaded with usage context. 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 2 params and no output schema, description covers what it does and returns adequately. Siblings don't overlap. Could be clearer on parameter format but 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 2 parameters (topic required, category optional) with 0% coverage. Description only mentions topic via example and general phrasing ('vendor, category, trend, or business topic') but doesn't explain category parameter or type constraints.

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

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

The description clearly states it researches AI consensus across platforms, with specific outputs: consensus score, sentiment, themes, breakdown. Example given. Distinct from siblings like benchmarks.

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

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

Explicitly says 'Use when researching ... across platforms simultaneously.' Implied use context but no explicit when-not or alternatives. Good 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, covering safety. The description adds context about the data scope (FinCEN, OFAC, BSA, etc.) but does not disclose behavioral traits such as pagination, rate limits, or data freshness 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 two sentences, front-loaded with the tool's output categories, followed by target audience. Every sentence adds value with zero wasted words.

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

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

For a simple benchmark tool with two optional enum parameters and no output schema, the description covers the key data returned. It lacks mention of limitations or typical usage scenarios, but annotations handle safety context.

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

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

Schema description coverage is 0%, meaning the schema has no descriptions for parameters. The description does not explain the focus or institution_type parameters or how they affect output; the enum values are self-explanatory but no additional meaning is provided.

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

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

The description clearly states it provides AML regulatory benchmarks and lists specific data types (FinCEN SAR filing rates, OFAC SDN counts, etc.). It distinguishes from siblings like get_bank_regulatory_benchmark by its explicit AML focus.

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

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

The description identifies target users ('compliance agents and financial institution risk officers') but does not provide explicit guidance on when to use this tool versus alternatives, nor does it 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?

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?

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds context about the data source (Audit Analytics public aggregate data) and typical users (CFOs, audit committees), which goes beyond the annotations. It does not contradict annotations.

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

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

The description is concise, with three sentences that front-load the usage guidance and then describe the output and source. Every sentence adds value, and there is no redundancy.

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

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

There is no output schema, but the description adequately describes the output: total fees and fees as a percentage of revenue by company revenue band and auditor tier. This is sufficient for a benchmark tool. The context signals indicate many siblings, but this description is specific enough to distinguish the tool.

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

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

Schema description coverage is 33% (only annual_revenue_usd has a description). The description mentions 'company revenue band and auditor tier,' which maps to annual_revenue_usd and auditor_tier parameters, but does not add details for the industry parameter. The schema provides some parameter descriptions, so the description adds marginal value.

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

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

The description clearly states the tool's purpose: benchmarking audit costs, evaluating proposals, and preparing RFPs. It specifies the resource (audit fee benchmarks with total fees and percentage of revenue) and distinguishes it from sibling tools by focusing on audit fees, which is unique among many get_*_benchmark tools.

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

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

The description explicitly states when to use the tool: 'Use when benchmarking audit costs, evaluating auditor proposals, or preparing an audit committee RFP.' This provides clear context. It does not specify when not to use it or name alternatives, but given the specific niche, this is adequate.

Agents often have multiple tools that could apply. Explicit usage 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 adds value by specifying data source (FDIC BankFind) and types of data returned (assets, deposits, capital ratios, loan quality, peer benchmarks). 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: use-case, returns, example. Front-loaded and 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?

Despite no output schema, description lists key data categories and provides a concrete example. For a one-parameter tool, this is fairly complete, though exact fields are not enumerated.

Complex tools with many parameters or behaviors need more documentation. 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 (bank_name) with schema description coverage 100%. Description does not add further parameter details beyond the schema, so baseline score of 3 is appropriate.

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

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

Description clearly states the tool returns FDIC-sourced financial data for banks and lists specific use cases (acquisition, partnership, etc.). Distinguishes from numerous sibling tools by focusing on bank financial intelligence.

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

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

Explicitly states when to use (evaluating a bank for acquisition, etc.). No explicit when-not or alternatives, but the specific context is clear.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, signaling a safe read operation. The description adds meaningful context by specifying the data comes from FDIC call report public aggregates, indicating it is aggregated and not institution-specific. This helps the agent understand the nature of the data.

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

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

The description is two sentences long, front-loaded with the most important information (what metrics are provided and how they are grouped). The second sentence adds source and audience without redundancy. 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 has no output schema, the description lists the specific metrics returned, which is sufficient. It also cites the data source (FDIC call report public aggregates). However, it could mention that results are aggregated summary data and not per-institution, which would further clarify the tool's behavior.

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

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

The input schema has two parameters (bank_type and asset_size_tier) with enums but no descriptions. The description mentions asset_size_tier implicitly by stating 'by asset size tier', but does not explain bank_type or its values. With 0% schema description coverage, the description should compensate but falls short, leaving the agent unclear on what bank_type options mean.

Input schemas describe structure but not intent. Descriptions should explain 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 bank regulatory capital and financial performance benchmarks, listing specific metrics like CET1, NIM, and charge-off rates. It identifies the grouping by asset size tier. However, it does not explicitly differentiate from sibling tools such as get_bank_financial_intelligence, which may offer similar 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 targets bank CFOs, risk officers, and analysts, implying the intended audience. It lacks explicit guidance on when to use this tool versus alternatives, such as get_bank_financial_intelligence or get_aml_regulatory_benchmark. No when-not or exclusion criteria are provided.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows it is a safe read operation. The description adds transparency about the return content (benchmarks, risk signals, themes, watchlist) and provides a detailed example of the output, which 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 single paragraph but efficiently covers usage context, outputs, and an illustrative example. It is not verbose, but a slightly more structured format (e.g., separating usage, outputs, example) could improve scannability. Overall, it is concise and front-loaded with key use cases.

Shorter descriptions cost fewer tokens and are easier for agents to parse. 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 three parameters and no output schema, the description should detail return structure. It mentions the types of return data and gives an example, but does not specify the exact format or field names. The example is helpful but not exhaustive. It provides adequate guidance for an agent to understand what to expect, but lacks completeness for precise programmatic use.

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

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

Schema description coverage is only 33% (only level_4_5_percentage has a description). The tool description does not explain the 'specialty' or 'annual_claim_volume' parameters beyond the example mentioning 'Cardiology practice' and '67% level 4/5'. It fails to specify valid values for specialty or how annual_claim_volume affects results. This does not adequately compensate for the 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 tool's purpose: assessing coding compliance risk before OIG audits, RAC reviews, or revenue integrity programs. It specifies exact outputs (E/M distribution benchmarks, upcoding risk signals, OIG audit priority themes, RAC watchlist) and includes a concrete example. This distinguishes it from sibling tools that are generic benchmarks.

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

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

The description explicitly states when to use the tool ('when assessing coding compliance risk before an OIG audit, preparing for a RAC review, or building a revenue integrity program'). It provides a realistic example scenario. It does not explicitly state when not to use it, but the specialized context makes it clear that it is for billing coding risk assessment.

Agents often have multiple tools that could apply. Explicit usage 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?

The annotations already declare readOnlyHint=true and destructiveHint=false, indicating a safe read operation. The description adds behavioral context by specifying the output details and providing an example, which helps the agent understand what to expect. No contradictions. The description does not add information about authentication or rate limits, but the annotation coverage is sufficient.

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

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

The description is concise at about three sentences, front-loading the key information: when to use, what it returns, and an example. Every sentence adds value, and there is no unnecessary text. 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?

The description explains both the input (brand name) and the output (momentum score, trend direction, weekly series) with an example. Given the tool's simplicity (one parameter, no output schema), this provides sufficient completeness for an agent to understand and invoke the tool correctly.

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

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

The input schema has only one parameter, brand_name, which is required. The schema description coverage is 0%, meaning the JSON schema provides no description. The tool description implicitly indicates that brand_name refers to a vendor brand name (via the example 'HubSpot'), but it does not explicitly define the parameter or its format. This leaves room for 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 the tool's purpose: monitoring vendor brand trajectory and week-over-week competitor momentum. It specifies the output (4-week momentum score, trend direction, weekly movement series) and provides a concrete example (HubSpot). This clearly distinguishes it from the many sibling tools, which focus on other benchmarks.

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

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

The description explicitly states when to use the tool ('Use when monitoring a vendor brand trajectory... or tracking week-over-week competitor momentum for a CMO brief'). This provides clear context for appropriate usage. However, it does not include explicit guidance on when not to use it or mention alternative tools, so a slight deduction.

Agents often have multiple tools that could apply. Explicit usage 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 the description adds value by detailing output contents (payback ranges, guardrails, channel efficiency) and providing an illustrative 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?

Two sentences plus an example and source, all front-loaded with usage context. Every sentence adds value with no redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. 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 provides a concrete example and a specific metric range, which sufficiently informs the agent of expected output. The source adds credibility. Could be slightly more explicit about output format, but adequate.

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

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

Schema description coverage is 67%; the description adds meaning for the undocumented 'gtm_motion' parameter by mentioning it as a filter in the context ('by industry and GTM motion'), and the example uses both industry and GTM motion, clarifying its role.

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

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

The description clearly states the tool returns CAC payback ranges, LTV/CAC guardrails, and channel efficiency benchmarks, with a specific verb 'Returns' and resource metrics. It implies distinction from sibling benchmark tools by focusing on CAC metrics, but does not explicitly contrast with them.

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

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

Starts with 'Use when...' explicitly listing scenarios for evaluating sales/marketing efficiency, setting targets, and benchmarking before board reviews. No exclusions or alternatives given, but the context is clear.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description's main behavioral contribution is indicating data source (CBRE and JLL quarterly surveys) and update frequency (quarterly). It does not disclose other important behaviors like response format or pagination.

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

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

Two concise sentences: first defines the tool's function, second provides audience and usage. No redundant words, front-loaded with key information.

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

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

Given no output schema, the description should clarify what the tool returns (e.g., average cap rate, range). It provides domain context and data source but omits operational details like return format or multiple results.

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

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

Schema description coverage is 0%, so the description must explain parameters. It only lists 'by asset class, market tier, and geography' without explaining what each parameter means or how values map to real-world concepts. The enum values are provided in schema but lack semantic context.

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

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

Description clearly states it provides commercial real estate cap rate benchmarks by asset class, market tier, and geography. The verb 'get' and resource 'cap rate benchmark' are specific, and it distinguishes from siblings like 'get_cre_debt_benchmark' by focusing on cap rates. Intended audience and use cases are mentioned.

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

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

Description provides usage context by naming typical users (CRE acquisition teams, asset managers, real estate CFOs) and purposes (property pricing, portfolio valuation). However, it does not explicitly exclude specific scenarios or compare to 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?

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the description is not required to state safety. The description adds value by explaining the ranking methodology (unprompted AI mention frequency) and providing an example with concrete numbers. 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 only three sentences and front-loads the purpose and usage. Every sentence adds value: first defines usage, second states output, third provides a concrete example. No wasted words.

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

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

Given one parameter, no output schema, and annotations providing safety context, the description is nearly complete. It explains what the tool does, when to use it, and gives an example. However, it does not explicitly describe the return format (e.g., list of objects with vendor name and frequency), which is a minor gap.

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

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

The input schema has a single required 'category' string with 0% description coverage. The description implies the parameter is a software category (e.g., 'CRM') but does not specify format, constraints, or allowed values. It relies heavily on the example to convey meaning, which is insufficient for complete 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 returns vendors ranked by unprompted AI mention frequency for a given category. It specifies a specific verb ('returns') and resource ('vendors ranked by AI mention frequency'), which distinguishes it from siblings like get_top_vendors_by_category.

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

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

The description explicitly says 'Use when assessing brand visibility in AI-generated recommendations or researching which vendors dominate AI platform responses in a software category.' It provides clear context but does not mention when not to use or suggest alternative tools among the many siblings.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds value by stating the tool returns a disruption risk score from 0 to 1 with evidence strings, and provides an example output, offering behavioral context beyond annotations.

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

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

The description is concise: two purpose sentences plus an example sentence. It is well-structured, front-loads the use case, and every sentence provides essential information without redundancy.

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

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

The tool has one parameter, no output schema, and strong annotations. The description sufficiently covers input (category), output (risk score with evidence), and usage context. No critical information is missing for correct invocation.

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

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

There is only one required parameter 'category' (string) with no schema description (0% coverage). The description provides an example ('ERP category') but does not fully specify acceptable formats or values. Given the single parameter and example, it adds some context but is not exhaustive.

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

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

The description clearly states the tool's purpose: assessing near-term displacement risk for a software category or timing market entry/exit decisions. It specifies the action (assess/returns), resource (software category), and differentiates from siblings like 'get_competitive_displacement_signal' by focusing on category-level disruption.

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

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

The description explicitly says 'Use when assessing whether a software category faces near-term displacement risk, or timing a market entry or exit decision', providing clear context for usage. It lacks an explicit statement of when not to use or alternatives, but the context is sufficient.

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

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

Annotations declare readOnlyHint=true and destructiveHint=false, which the description reinforces by implying it's a read operation. The description adds valuable context by detailing the return values (median, p25/p75, sample size) and citing the data source, going beyond what annotations provide.

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

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

The description is concise, consisting of two sentences plus an example and source attribution. It front-loads the usage purpose, making it easy to scan. The example adds value without unnecessary verbosity.

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

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

Given no output schema, the description adequately explains what the tool returns (median, p25/p75, sample size) and provides context. It does not cover data freshness or formatting, but for a benchmark tool, this is 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?

With schema description coverage at 33% (only category has a description), the description does not compensate for the missing parameter descriptions. It mentions category in the usage but does not explain the industry or company_size parameters, 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's purpose: benchmarking total spend in a software category against same-size peers. It specifies the output (median monthly spend, p25/p75 band, sample size) and distinguishes itself from sibling tools like get_industry_spend_benchmark by focusing on software category rather than industry.

Agents choose between tools based on descriptions. A clear purpose with a specific verb 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 begins with 'Use when benchmarking total spend in a software category against same-size peers,' providing clear guidance on when to use the tool. However, it does not explicitly state when not to use it or mention 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?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds that it returns rollups and mentions source, which is consistent. It does not contradict annotations and provides additional behavioral context (e.g., includes response rate trends), but the safety profile is already well covered.

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

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

Three sentences plus an example with no wasted words. Front-loaded with usage context, then output summary, then concrete illustration. Every sentence adds value.

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

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

No output schema, so description must describe returns. It covers volume, issue themes, and response rate trends, plus source. The example illustrates format. Could detail more fields, but for a rollup tool this is largely adequate.

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

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

Schema coverage is 0%, meaning the description must compensate. It mentions 'by company and product,' which maps to the two parameters, but does not specify that company_name is required or define valid product values. The description adds partial meaning beyond the schema but is insufficiently detailed.

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

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

The description states it returns CFPB complaint rollups by company and product with volume, issue themes, and response rate trends. It clearly specifies the resource (CFPB complaints) and verb (returns), distinguishing it from sibling tools that cover other financial or benchmark 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 lists use cases: assessing consumer finance risk, benchmarking complaint volume, and pre-acquisition due diligence. It provides an example but does not mention when not to use or explicitly name alternatives among siblings. However, the context is clear enough for appropriate selection.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable context about the data scope (50+ chains, rankings, 1D/7D change, protocol counts, dominance) and purpose ('x402 agent context'). No mention of rate limits or data freshness, but the added detail justifies a score above baseline.

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

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

One sentence that efficiently communicates the tool's output and purpose. No redundant language, though the content is dense. Could potentially be more scannable, but it is sufficiently concise.

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

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

Given the moderate complexity (2 optional params, no output schema), the description lists many output components (rankings, change, protocol counts, etc.). It is fairly complete for a list tool, though a response structure overview would improve completeness.

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

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

Schema description coverage is 0%, and the tool description does not elaborate on the parameters (limit, sort_by). The schema provides enum and numeric constraints but lacks explanations; the description should compensate but 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 'Live TVL by blockchain' from DeFiLlama for 50+ chains including specific metrics. It distinguishes from sibling tools like get_crypto_correlation_benchmark or get_defi_yield_benchmark by its explicit focus on chain-level TVL and comparison 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 obtaining chain TVL data but does not explicitly state when to use this tool versus alternatives. No exclusion criteria or alternative tool references are provided.

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

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

Annotations already indicate the tool is read-only. The description adds context about data sources (FEMA NFIP, NGFS scenarios) and risk types, but does not discuss other behavioral aspects such as response format, pagination, or potential delays. It provides moderate added value beyond 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 long, directly states the tool's purpose, and lists key categories. No unnecessary words or redundancy. It is efficiently front-loaded with essential information.

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

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

Given the tool has three parameters with enums and no output schema, the description should explain how to use the parameters and what the return data looks like. It does not cover region or property_type, and only hints at the return types. The description is incomplete for an agent to confidently invoke the tool without further schema investigation.

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

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

The input schema has zero description coverage for its three parameters, all of which are enums. The description only mentions risk types (physical, transition) but does not explain 'region' or 'property_type'. It fails to compensate for the lack of parameter documentation, leaving ambiguity about valid values and usage.

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

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

Description specifies exactly what the tool provides: climate financial risk benchmarks for physical and transition risks, citing specific data sources and intended use for ESG and risk agents. It clearly distinguishes from the sibling tool 'get_climate_risk_score' by focusing on benchmarks rather than scores.

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

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

The description states it is 'for ESG and risk agents', giving a general target audience. However, it does not provide explicit when-to-use or when-not-to-use guidance, nor does it compare with sibling tools like 'get_climate_risk_score' or 'get_esg_benchmark'. Usage is implied but not clearly differentiated.

Agents often have multiple tools that could apply. Explicit usage 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?

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context: returns peer group, percentiles, metadata, source attribution, and optional detail. No contradictions.

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

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

The description is three sentences: purpose first, then return fields, then example. No wasted words, front-loaded, easy to parse.

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

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

Given the tool has 3 parameters (2 required) and no output schema, the description covers purpose, parameters via example, and context. It is adequate but could mention default behavior if hospital_type omitted.

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

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

With 0% schema coverage, the description adds meaning by mentioning parameters (bed_size, state, hospital_type) and provides an example (300-bed hospital in Illinois). However, it does not specify allowed values or constraints for parameters like state codes or hospital type 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 benchmarks hospital operating costs against CMS peer cohort, specifying the resource and action. However, it does not explicitly differentiate from sibling benchmark tools for other healthcare categories, though the CMS focus provides implicit distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb 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 begins with 'Use when...' providing explicit use cases (benchmarking costs, CFO presentations). However, it lacks guidance on when not to use this tool or alternatives among many benchmark 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 show readOnlyHint=true and destructiveHint=false. Description adds value by specifying data source (CMS Open Payments Sunshine Act database), aggregate nature, and example breakdown (amounts, types, program year). No contradiction.

Agents need to know what a tool does to the 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 5-6 sentences, front-loaded with usage context, then details and an example. It is well-structured but the example could be more concise. No verbosity.

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

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

No output schema, so description should describe return values. It mentions payment amounts, types, program year breakdown, and gives a numeric example. However, it does not specify how data is grouped or what fields are in response. Parameter details are also incomplete.

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

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

Schema coverage is 0%, so description must compensate. It implies recipient_name can be a physician or manufacturer (example uses name), but does not clarify that manufacturer_name is optional or explain program_year. Example provides some context but leaves gaps.

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

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

The description clearly states the tool returns CMS Open Payments aggregates by physician or manufacturer, with explicit use cases (payment transparency risk, manufacturer relationships, Sunshine Act compliance). It distinguishes itself from sibling tools by specific domain.

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

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

Description explicitly lists three use cases at the start: assessing physician payment transparency risk, evaluating manufacturer relationships, and preparing Sunshine Act compliance reporting. It does not mention when not to use or alternatives, but the guidance is clear.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description's addition of return content (domain weights, distributions) provides some context. However, it does not disclose any behavioral traits beyond what annotations cover, 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 relatively concise, front-loading the use case and then listing outputs and an example. It could be slightly shorter but overall efficient.

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

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

Given the tool has three optional parameters and no output schema, the description lacks information on how parameters influence results. It also does not explain the significance of 'improvement priorities.' The example helps but is insufficient for complete 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?

With 0% schema description coverage, the description must compensate but does not. It mentions returns but provides no explanation of how the three optional parameters (state, hospital_name, current_star_rating) affect the output. The example does not link to parameters.

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

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

The description clearly states the tool is for CMS Hospital Star Rating strategy and quality benchmarking, specifying it returns domain weights, distributions, and improvement priorities. However, it does not differentiate from siblings like get_hospital_care_compare_quality, which may have overlapping functionality.

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

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

The description provides explicit when-to-use guidance ('Use when advising on CMS Hospital Star Rating strategy or benchmarking'). However, no guidance is given on when not to use this tool or what alternatives exist among the 100+ 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?

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?

Annotations already indicate read-only and non-destructive. The description adds valuable behavioral context: HTTP 503 handling when upstream is unavailable for >50% of fields, daily updates, and a data_source field for provenance. This exceeds 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 information-dense but contains redundancy (503 error repeated twice) and extraneous phrases ('Live source.'). It could be more streamlined while retaining key points.

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

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

Given one optional parameter and no output schema, the description covers purpose, data source, error handling, and cost. However, it lacks details on the output structure (e.g., fields returned) which would help the agent understand the response 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?

Only one parameter 'category' with enum values (energy, metals, agriculture, all). Schema description coverage is 0%, and the description does not explain the parameter or how to use it. The commodity list implies categories but not explicitly. More guidance is needed.

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

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

The description clearly states it provides live commodity price benchmarks for specific commodities (WTI crude, natural gas, gold, copper, wheat, soybeans) with weekly and monthly changes, distinguishing it from the many sibling benchmark tools. The verb 'get' and resource 'commodity benchmark' are explicit.

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

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

The description mentions 'For traders and macro analysts' implying usage context, but does not provide explicit when-to-use or when-not-to-use guidance, nor does it reference alternative tools. Cost and SLA info is provided but not as usage guidelines.

Agents often have multiple tools that could apply. Explicit usage 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, nondestructive. Description adds value by specifying the data source (DOL LCA and H-1B filings), aggregation levels, and provides an example illustrating wage levels and geographic distribution. Does not contradict annotations.

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

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

Two sentences plus an example. Front-loaded with purpose and return type. Every sentence adds value; example is illustrative 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?

Given no output schema and 0% schema coverage, the description adequately explains the tool's capabilities. The example fills in details about output granularity. However, lacks specifics on data freshness or pagination, but acceptable for a benchmark tool.

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

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

Schema coverage is 0%, but the description maps parameters by listing aggregation dimensions (employer, job title, state, fiscal year). This provides partial meaning beyond the schema, though it lacks format details or 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?

Description clearly states the tool benchmarks compensation against DOL wage data and assesses H-1B practices. It distinguishes from siblings like get_salary_benchmark and get_employer_h1b_wages by specifying the DOL source and aggregation dimensions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb 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 benchmarking compensation or assessing H-1B practices before hiring. Does not explicitly mention when not to use or alternative tools, but the context is clear.

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

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

Annotations already confirm read-only and non-destructive behavior. Description adds that the tool returns replacement vendors, narratives, and counts, plus a data source. 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: use case, output summary, and illustrative example. No unnecessary words. Front-loaded with purpose.

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

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

Covers purpose, usage, output structure, and data source. For a simple tool with one parameter and annotations, it is fully 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?

Only one parameter (vendor_name) with 0% schema coverage. Description implies it via example (Salesforce) but does not explicitly define format or constraints. Adequate given simplicity.

Input schemas describe structure but not intent. Descriptions should explain 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 tracks competitive threats and switching trends for an incumbent vendor, with a concrete example. It distinguishes itself by focusing on displacement signals with switch narratives and mention counts.

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

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

Explicit 'Use when' phrasing guides the agent to appropriate scenarios. Lacks mention of when not to use or specific alternatives, but the context is clear enough for selection.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the read-only nature is clear. The description adds context like 'live material cost escalation signals' and lists data sources, but does not disclose behavioral details such as rate limits, response format, 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 efficient at two sentences, covering purpose, outputs, sources, and audience with no redundant words. However, it is slightly front-heavy and could be structured into clear bullet points 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 lists the types of benchmarks provided (hard cost, soft cost, contingency, escalation), but does not specify the return structure or unit of measure. It also omits the construction_class parameter, which is a gap given the absence of an 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%, so the description must compensate. It explains that building_type and region are used for hard cost per SF, but fails to mention construction_class. This leaves one parameter undocumented, limiting the agent's understanding of how to use it.

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

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

The description clearly states the tool provides construction cost benchmarks including hard cost per SF by building type and region, soft cost ratios, contingency standards, and live material cost escalation signals. It names specific sources and target audience, making it distinct from sibling benchmark tools.

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

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

The description implies usage for developers, lenders, and project owners but does not provide explicit guidance on when to use this tool versus alternatives like get_development_pro_forma_benchmark or get_residential_market_benchmark. No exclusions or when-not-to-use advice is given.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds transparency by specifying that it returns HTTP 503 (no charge) when upstream data is unavailable for >50% of fields and that it includes a data_source field. 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?

Description is moderately concise, front-loading the main purpose and data sources, then adding behavioral details and pricing. It is structured with clear sections separated by '|', 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?

Description covers data sources, error behavior, and pricing, but lacks detail on the response structure (e.g., schema of return values) which is not provided by an output schema. It mentions a data_source field but does not fully explain the output format. For a tool of this complexity, additional context on the response would improve completeness.

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

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

The input schema has one optional parameter 'focus' with an enum, but the description does not explain how the parameter filters or selects data. Schema coverage is 0%, and the description fails to add meaning beyond listing the provided indicators. The parameter's role is left ambiguous.

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

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

Description clearly identifies the tool as providing live consumer sentiment benchmarks from FRED, listing specific indicators (University of Michigan sentiment, Conference Board confidence, retail sales, PCE, personal saving rate) and stating it is intended for GDP and equity agents. This distinguishes it from siblings that focus on other domains.

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

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

Description mentions that it is a live source and provides error behavior and pricing, but does not explicitly state when to use this tool versus alternatives or provide conditions for when not to use it. The statement 'Strong/moderate/weak consumer signal for GDP and equity agents' hints at use cases but lacks direct comparison.

Agents often have multiple tools that could apply. Explicit usage 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, covering safety. The description adds value by disclosing data sources (S&P Capital IQ and Damodaran) and target users (CFOs, treasurers). 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 three sentences: usage context, data content, source and audience. It is concise and front-loaded with the most important usage guidance. However, the second sentence is a bit dense; could be more structured.

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

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

Given the tool has 2 parameters, no output schema, and annotations cover safety, the description covers what data it returns (the three metrics) but does not specify output format or granularity. The context signals indicate many sibling tools, and the description is adequate but not fully complete for a first-time user.

Complex tools with many parameters or behaviors need more documentation. 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 no property descriptions (0% coverage), so the description must compensate. It mentions 'by credit rating tier and industry' which hints at the parameters, but does not explicitly describe what each parameter means or how to use them. The enum values are somewhat self-explanatory, but the description lacks detailed guidance.

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

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

The description clearly states the tool provides corporate leverage and debt benchmarks (Net Debt/EBITDA, interest coverage, debt maturity profiles) by industry and credit rating tier. It uses specific verb 'assessing', 'benchmarking', 'preparing' and distinguishes from siblings like get_cre_debt_benchmark by focusing on corporate debt.

Agents choose between tools based on descriptions. A clear purpose with a specific verb 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 opens with 'Use when assessing a company debt capacity...' which provides explicit usage scenarios. It lists three specific contexts. However, it does not explicitly contrast with sibling tools or state when not to use, though the tool name and context imply corporate debt focus.

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

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

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. Description adds data source context (MBA CREF, Trepp) but does not disclose update frequency, pagination, or output format.

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

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

Two sentences with no wasted words. First sentence packs key info: metrics, dimensions, sources. Second sentence targets audience. Ideal length for quick comprehension.

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

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

Given no output schema and moderate complexity (enums, multiple dimensions), description covers essential context: metrics, dimensions, sources, audience. Lacks details on data freshness or interpretation, but adequate for initial selection.

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

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

Schema coverage is 0%, so description must compensate. It mentions 'by property type and lender type' and lists some lender types (bank, agency, CMBS, life company) but omits debt_fund and bridge from the enum, providing incomplete guidance.

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

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

Description clearly states it provides CRE debt benchmarks (DSCR, LTV, spreads) by property and lender type, with specific data sources. The verb 'get' plus resource 'cre_debt_benchmark' is distinctive among 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?

States intended audience (CRE CFOs, capital markets teams) and context (structuring financings). However, it does not explicitly mention when to avoid this tool or compare to alternative sibling tools for other debt benchmarks.

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

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

Beyond annotations (readOnlyHint, destructiveHint), the description discloses error behavior (503 for unavailable data), update frequency (daily), live source, and data_source field provenance. This adds useful behavioral context.

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

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

The description is dense and front-loaded with core functionality, but includes some secondary details (pricing, SLA) that could be separated. Still efficient for the amount of 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?

Despite lacking output schema, the description covers data sources, metrics, update frequency, target users, error handling, and provenance. Missing output structure details, but otherwise comprehensive for a financial benchmark tool.

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

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

Schema description coverage is 0%. The description mentions 'OAS by rating tier' but does not explicitly explain the 'rating_tier' parameter or how its enum values affect results. Only minimal implied relation.

Input schemas describe structure but not intent. Descriptions should explain 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 credit spread benchmarks from specific FRED ICE BofA indices, listing specific metrics (OAS, TED spread, etc.). It distinguishes from numerous sibling benchmark tools by its specific focus and named indices.

Agents choose between tools based on descriptions. A clear purpose with a specific verb 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 (credit analysts, fixed income PMs) and error handling (503 when unavailable), but lacks explicit guidance on when to use this tool vs. alternatives or when to avoid it.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false; description adds data source and intended audience but does not disclose additional behavioral traits 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?

Two sentences, front-loaded with purpose and metrics, followed by source and audience. Every sentence adds value with no wasted words.

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

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

For a simple data retrieval tool with few parameters and good annotations, the description covers purpose, source, and context sufficiently. Lacks only explicit parameter descriptions.

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

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

Schema description coverage is 0%; description mentions 'by asset size' corresponding to asset_size_tier but does not explain charter_type or provide meaningful parameter details beyond the schema 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?

Description clearly states the tool provides credit union financial performance benchmarks with specific metrics (capital ratios, net interest margin, etc.) and distinguishes from siblings like get_ncua_credit_union_financials by focusing on benchmarks and asset size tiers.

Agents choose between tools based on descriptions. A clear purpose with a specific verb 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 credit union financial analysis and board reporting but does not explicitly state when to choose this over alternatives such as get_ncua_credit_union_financials or 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?

Annotations already indicate readOnlyHint=true and destructiveHint=false. Description adds live source, error behavior (HTTP 503 with no charge when upstream unavailable), x402 SLA with pricing ($0.10 USDC per call), and data provenance via data_source field. This provides valuable context beyond 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 relatively concise but includes repetitive information about HTTP 503 errors (mentioned twice). Could be more structured by grouping related information (e.g., error handling, pricing). It front-loads the core purpose effectively.

Shorter descriptions cost fewer tokens and are easier for agents to parse. 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 metrics (Pearson correlation pairs, beta to BTC, dominance context, portfolio diversification signal) and notes the data_source field. With one optional parameter and simple enum, the description covers most essential aspects for a complete understanding.

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

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

Only one parameter 'period' with enum values (7d, 30d, 90d). Description does not explain these values beyond the tool's 30-day focus. Schema coverage is 0%, and the description adds little meaning since the enum values are self-explanatory but not explicitly described in relation to the output.

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

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

Description clearly states the tool returns a 30-day rolling correlation matrix for BTC, ETH, and SOL, specifying metrics like Pearson correlation pairs, beta to BTC, dominance context, and diversification signal. It distinguishes from sibling tools by focusing on crypto correlation 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?

Description mentions 'For crypto portfolio agents' but does not provide explicit guidance on when to use this tool versus alternatives. No exclusion criteria or comparisons to sibling tools like get_chain_tvl_benchmark or get_defi_yield_benchmark are given.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description does not need to restate that. It adds value by mentioning the data source (DeepDAO public data) and listing specific metrics included, which goes 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?

Two sentences: the first lists content, the second provides example median values. No fluff, front-loaded, 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 describes output metrics and provides median examples, but does not specify output format, number of top DAOs returned, or default sorting. For a tool with no output schema, these details would improve completeness.

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

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

Schema description coverage is 0%, so the description should compensate. It mentions 'treasury' and 'stablecoin percentage' which correspond to the sort_by enum values, but does not explain min_treasury_usd or clarify how sort_by affects output. This leaves a gap in parameter understanding.

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

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

The description clearly states it provides DAO treasury benchmarks with specific metrics (treasury size, stablecoin percentage, runway, governance token concentration). This distinguishes it from sibling tools like get_cac_benchmark or get_saas_metrics_benchmark by specifying 'DAO' and the exact 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 when DAO treasury benchmarks are needed, but does not explicitly state when to use this tool over alternatives or provide exclusions. With many sibling benchmark tools, explicit guidance would be beneficial.

Agents often have multiple tools that could apply. Explicit usage 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 context about the free public API and no key requirement, but does not detail rate limits, pagination, or other behavioral traits beyond annotations.

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

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

Three sentences: first states purpose and key outputs, second describes optional filters, third explains default scope and free access. No redundancy, efficiently structured.

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

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

With no output schema, the description compensates by listing returned fields (APY bands, TVL, chain, pool ID). It covers tool purpose, filters, default behavior, and access info, suitable for a simple data retrieval tool.

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

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

Schema coverage is 100% with descriptions. The description adds examples (e.g., USDC, DAI, ETH for asset; aave-v3, compound-v3 for protocol) and clarifies that filters are substring matches, going 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 provides a DeFi lending and stable yield benchmark from DeFiLlama Yields, including top pools by APY with p25/p50/p75 bands, TVL, chain, and pool ID. It distinguishes from siblings like get_stablecoin_yield_benchmark by specifying DeFi lending/money-market 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 explains the default universe (stablecoin-marked pools) and mentions optional filters, but does not explicitly compare with similar sibling tools like get_stablecoin_yield_benchmark or provide when-not-to-use guidance.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description adds value by naming sources (NAHB, ULI, industry composite) and listing metrics. It does not disclose additional behavioral details like authentication requirements or error handling.

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

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

The description is two sentences with no fluff. It front-loads the core purpose and immediately lists key metrics, followed by target users and sources. Every sentence serves a clear function.

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

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

Given no output schema, the description should clarify the return format but only mentions metrics without specifying structure. It also inadequately covers parameters, ignoring market_tier. For a tool with two enum parameters and no output schema, more completeness is needed.

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

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

The input schema has 0% description coverage, and the description only explains the product_type parameter implicitly ('by product type'). The market_tier parameter is not mentioned at all, leaving its purpose and possible values unclear despite being defined in the schema.

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

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

The description clearly specifies the tool provides development pro forma benchmarks including yield on cost, profit-on-cost, construction-to-perm spread, and return hurdles by product type. It distinguishes itself from sibling tools like get_cap_rate_benchmark and get_construction_cost_benchmark by focusing on pro forma 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 states the tool is for developers underwriting new projects and lenders sizing construction loans, giving clear context for when to use it. However, it does not specify when not to use it or mention alternatives, such as get_construction_cost_benchmark for standalone cost data.

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

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

Annotations already 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, covering safety. The description adds methodology and source background (SEC EDGAR aggregate, Sloan accruals model) but does not disclose additional behaviors like data freshness, rate limits, or pagination. It adds some value beyond annotations but not extensive.

Agents need to know what a tool does to the 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, then providing source and target audience. Every sentence adds value with no redundancy or wasted words.

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

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

Given the tool's complexity (two parameters, no output schema, annotations present), the description covers purpose, use case, methodology, and source. It is sufficient for an agent to decide whether to invoke, though it could clarify if results are for a single sector only.

Complex tools with many parameters or behaviors need more documentation. 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 'by sector' aligning with the required sector parameter, and 'revenue recognition risk' hints at the optional revenue_recognition_model parameter. However, it does not fully explain each parameter's purpose or how they affect results, leaving room for 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 the tool provides earnings quality and financial statement risk benchmarks, specifying metrics (accruals ratio, cash conversion, revenue recognition risk) and source (SEC EDGAR + Sloan model). It distinguishes from sibling benchmark tools by focusing on earnings quality and academic standard.

Agents choose between tools based on descriptions. A clear purpose with a specific verb 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, giving clear usage context. It does not directly compare to alternatives or state when not to use, but the context is sufficient for an agent to infer appropriate use cases.

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

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

Annotations already indicate read-only and non-destructive behavior. The description adds value by explaining the gap analysis feature when optional parameters are provided, and cites data sources (KLAS 2024, Kaufman Hall). No contradictions.

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

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

The description is three sentences, front-loading the use case, followed by a concrete example, and ending with sources. Every sentence adds value without redundancy.

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

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

For a benchmark tool with no output schema, the description sufficiently conveys the core functionality and optional derived analysis. It could be slightly more explicit about the output format, but overall it is complete.

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

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

With 0% schema description coverage, the description partially compensates by mentioning 'actual cost and bed count' for gap analysis and implicitly referencing the required vendor_name. However, it 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?

The description clearly states the tool's purpose: benchmarking EHR maintenance costs per bed. It distinguishes from sibling tools by focusing on EHR costs with optional gap analysis, using specific verbs ('benchmark', 'returns').

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

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

The description explicitly states when to use the tool ('before a contract renewal or evaluating health IT budget efficiency') and provides an example with decision triggers. It does not explicitly mention when not to use it, but the context is clear.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds that it returns WTI crude and natural gas spot prices with an example, but no mention of dependencies beyond configuration.

Agents need to know what a tool does to the 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, 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 tool with no parameters and no output schema, the description fully covers what it does, when to use it, and what it 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?

No parameters in schema, so baseline is 4. Description adds no param info, which 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 explicitly states the tool returns current WTI crude and natural gas spot prices, with clear use cases for commodity briefs, input cost analysis, and energy sector context. It distinguishes from sibling tools by specifying energy price 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?

Provides explicit contexts for when to use (CFO/investment briefs, energy sector context) but does not mention when to avoid or suggest alternatives like get_commodity_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 declare readOnly and non-destructive, and the description explains the output fields (wave position, degree, target high/low, invalidation, confidence) and example assets. This adds behavioral context beyond annotations.

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

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

Two sentences: first defines use case, second covers output and example. No unnecessary words; efficient and informative.

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

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

Given the simple tool (1 param, no output schema), the description covers the purpose, outputs, and example. It lacks details on confidence interpretation or update frequency, but these are minor for the tool's scope.

Complex tools with many parameters or behaviors need more documentation. 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 coverage 100%. The description mentions the default 'all' and gives examples of assets (BTC, SPY, TLT, Gold) but doesn't elaborate on the parameter's semantics beyond what the schema provides. Baseline of 3 is appropriate.

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

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

The description clearly states the tool is for technical traders needing Elliott wave counts, targets, and invalidation levels for major assets. It specifies the verb (get), resource (wave counts), and scope (major assets). Among siblings like get_trader_signals, it stands out as specific to Elliott wave analysis.

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

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

The description explicitly says 'Use when a technical trader needs wave counts...', providing clear context. It doesn't contrast with alternatives, but the specificity makes it obvious when 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?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds that data comes from DOL LCA filings, but does not disclose other behavioral traits like pagination, rate limits, or error handling. This is acceptable given annotation coverage.

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

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

The description is two sentences plus an example. The first sentence is clear and front-loaded. The example, while helpful, is somewhat lengthy. Overall efficient but not maximally concise.

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

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

No output schema is provided, but the description lists what data is returned (prevailing wage statistics, certified job titles, wage levels, state distribution). This gives sufficient context for a 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 100% with a single parameter. The description includes an example ('Google') but does not add semantic detail beyond the schema's description. 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's purpose: retrieving H-1B wage data for an employer. It uses specific verb ('analyzing') and resource ('employer H-1B compensation strategy'), and distinguishes from sibling tools by focusing on a specific data domain.

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

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

The description explicitly states when to use the tool ('when analyzing an employer H-1B compensation strategy or benchmarking tech sector wages'). It provides a clear context but does not mention when not to use it or contrast with sibling tools.

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

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

Annotations already declare readOnlyHint=true. The description adds context on data sources and metrics but does not disclose behavioral traits like return format or pagination.

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

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

Two sentences, front-loaded with the core offering and followed by sources and audience. No wasted words.

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

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

Given no output schema and many sibling tools, the description should clarify output structure and differentiate from similar benchmarks. It lists components but is incomplete.

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

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

Schema coverage is 0%, yet the description does not explain the 'focus' and 'sector' parameters beyond implying sector filtering. The enumeration values are present in the schema but not elaborated.

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

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

The description states it provides ESG benchmarks by sector, listing specific components and sources. It is clear but does not differentiate from sibling tools like get_climate_risk_benchmark.

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

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

The description targets 'sustainability agents and ESG analysts' but lacks explicit guidance on when to use this tool over alternatives such as 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?

Annotations already declare readOnlyHint=true, so the description adds value beyond annotations by detailing the composite payload structure (framework, deadline, total_controls, controls[], hint, query timestamp) and the optional filtering behavior. 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 concise, with a clear front-loaded usage statement, a succinct description of the payload, and a concrete example. Every sentence adds value without redundancy.

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

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

Given the tool's simplicity (one optional parameter, no output schema), the description fully covers the return payload and filtering behavior. It provides enough detail for an AI agent to understand and use the tool correctly.

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

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

Schema description coverage is 0%, so the description must compensate. It explains that the parameter optionally filters by NIST function and gives an example using MAP. However, it does not explain the meaning of each enum value (GOVERN, MAP, MEASURE, MANAGE) or their impact on results, 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 the tool's purpose: assessing EU AI Act compliance readiness ahead of the August 2, 2026 deadline or preparing board AI governance briefings. It specifies the verb 'returns' and describes the composite payload, distinguishing it from sibling tools by its EU AI Act focus and NIST function filtering.

Agents choose between tools based on descriptions. A clear purpose with a specific verb 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 EU AI Act compliance readiness and board briefings) and provides an example of filtering by MAP. However, it does not mention when not to use it or reference alternative tools among the many siblings.

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

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

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?

Annotations indicate readOnlyHint=true and destructiveHint=false, which the description does not contradict. Description adds context by citing the data source (USASpending.gov) and giving a concrete output example (Acme IT Services with dollar amounts and percentages), enhancing transparency beyond annotations.

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

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

Three sentences: first states purpose and use cases, second specifies returned data with a concrete example, third cites source. No extraneous information—every sentence earns its place.

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

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

Despite lacking output schema, description explains what the tool returns (obligation data by multiple dimensions) and gives a realistic example. It mentions the data source but not pagination or exact output format. Sufficient for understanding core behavior.

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

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

Schema coverage is 20% (only state has a description). The description explains that parameters like vendor_name, agency, NAICS code, and fiscal_year are used to filter and return contract obligations, adding meaning to the otherwise sparse schema. This compensates for the 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?

Description clearly states the tool returns contract obligation data from USASpending, with specific verb and resource. It lists use cases like researching federal revenue concentration and identifying competitors, distinguishing it from many other data-oriented 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?

Explicitly states when to use (researching federal revenue, identifying competitors, assessing vendor dependency). Does not mention when not to use or alternative tools, but the use cases are sufficiently specific.

Agents often have multiple tools that could apply. Explicit usage 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?

Annotations already mark as read-only and non-destructive. Description adds valuable context: illustrative nature, data source (FRED fed funds data), and scenario planning purpose. Example output clarifies behavior. No contradictions. Could mention caching or frequency, but current detail is strong.

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

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

Four sentences, front-loaded with usage scenario, each sentence adds value: purpose, context, output example, source. 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?

No output schema, but description provides example output and states returns probabilities for three meetings. Minor ambiguity: mentions cut, hike, and hold but example only shows cut and hold. Otherwise complete for a zero-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?

Input schema has 0 parameters, so baseline is 4. Description correctly doesn't invent parameter details. No need for extra explanation.

Input schemas describe structure but not intent. Descriptions should explain 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 illustrative cut, hike, and hold probabilities for the next three FOMC meetings using FRED data. Uses specific verb 'returns' and resource 'probabilities for FOMC meetings', differentiating from dozens of sibling tools that cover other topics.

Agents choose between tools based on descriptions. A clear purpose with a specific verb 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: 'when providing monetary policy narrative context for a macro brief, investment committee, or CFO rate planning session.' Also distinguishes from market-implied odds with 'Scenario planning tool — not futures-implied market odds.' No explicit when-not-to-use, but intent is clear.

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

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

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?

Annotations declare readOnlyHint=true and destructiveHint=false, and the description adds valuable behavioral details: returns HTTP 503 on upstream unavailability, pricing per call, and data_source field disclosure. No contradiction with annotations.

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

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

The description is informative but slightly dense, including multiple data points and an SLA note. It front-loads the core purpose effectively, but some details could be 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 complexity (multiple benchmarks, fallback behavior, pricing, provenance), the description covers most aspects. However, lacking an output schema, it does not fully describe the successful response structure beyond mentioning the data_source field.

Complex tools with many parameters or behaviors need more documentation. 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 (base_currency) with enum values, but the description does not explicitly explain how it affects output. Schema description coverage is 0%, and the description only indirectly implies the parameter through listed pairs, offering little added meaning.

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

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

The description clearly states the tool provides 'live major currency pair benchmarks' listing specific pairs and metrics, distinguishing it from the many sibling tools covering other financial benchmarks.

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

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

The description includes context for when to use (for live FX benchmarks), provides notes on data source (FRED) and update frequency, and mentions fallback behavior and pricing, but does not explicitly state when not to use or suggest 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?

The description adds significant behavioral context beyond annotations: it discloses HTTP 503 on upstream unavailability, zero API key requirement, x402 SLA pricing, and data provenance. No contradiction with readOnlyHint.

Agents need to know what a tool does to the 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 packed with useful info across two segments, each sentence adds value, though it could be slightly more concise.

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

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

Despite no output schema, the description covers chains, return fields, source, auth, error handling, pricing, and provenance, making it comprehensive for a simple one-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?

With 0% schema description coverage, the description partially compensates by mentioning the chains (matching the enum) but does not explain the 'all' value explicitly.

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

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

The description clearly states the tool provides live gas price benchmarks for Ethereum, Base, and Solana, listing return fields and sources. It is specific and distinct 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 obtaining gas benchmarks on supported chains but provides no explicit guidance on when to use this tool versus siblings, nor does it 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?

The annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows this is a safe read operation. The description adds context about the data returned (stars, forks, etc.) and source (GitHub public API), but does not disclose potential rate limits or other behavioral nuances 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 at three sentences, front-loaded with usage context, then return details, then an illustrative example. Every sentence adds value without redundancy.

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

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

For a tool with one parameter and no output schema, the description fully captures what the tool does, when to use it, what data it returns, and even provides an example. It is complete and self-contained.

Complex tools with many parameters or behaviors need more documentation. 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 'org_or_company' with 0% description coverage. The description helps by implying the parameter represents the organization or company name (e.g., HashiCorp), but it does not explicitly define the parameter format or constraints. Given the low schema coverage, the description provides moderate compensation.

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

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

The description clearly specifies the tool's purpose: assessing technology vendor open-source presence, evaluating developer community strength, and researching company GitHub footprint before due diligence. It precisely states the returned data (organization profile, top repo stats) and provides a concrete example. The tool name and context differentiate it from sibling tools.

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

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

The description begins with 'Use when...' and enumerates three specific scenarios, giving clear guidance on when to invoke the tool. However, it does not mention when not to use it or suggest alternative tools, missing an opportunity for full 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?

Goes beyond annotations by disclosing error behavior (HTTP 503 when upstream unavailable for >50% of fields), pricing ($0.10 per call), and data provenance field. Adds useful context beyond readOnlyHint and destructiveHint.

Agents need to know what a tool does to the 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 informative but somewhat cluttered with pricing and error details mixed with core functionality. Could be better organized with clearer separation of concerns.

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

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

No output schema is provided, and the description does not describe the response structure (e.g., keys, types). Missing details on how data are returned for different regions or metrics. 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 has 0% description coverage for the single parameter 'region'. The description does not explain how the region parameter maps to the listed indices or how to use it. Missing critical guidance for a simple optional enum.

Input schemas describe structure but not intent. Descriptions should explain 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 the specific global equity index benchmarks (S&P 500, Nasdaq, etc.) and the metrics provided (YTD returns, P/E ratios, risk signal). This makes the tool's purpose distinct from other benchmark tools in the sibling list.

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

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

No explicit guidance on when to use this tool vs. alternative benchmark tools (e.g., get_commodity_benchmark). The description implies it's for equity benchmarks but lacks direct usage context or exclusions.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds value by detailing the return data (savings percentage, leakage rate, top categories) and providing concrete example numbers and sources (HFMA, CMS). 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 concise (3 sentences) and front-loaded with usage and purpose. Every sentence adds value: usage, output summary, and a concrete example. No redundant or wasted words.

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

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

Despite good purpose clarity, the description is incomplete: it fails to document the single parameter 'category' and does not explain how to use it. No output schema exists, so more guidance on expected return format would be beneficial.

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

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

The input schema has one parameter 'category' with no description (0% coverage). The description does not mention this parameter at all, leaving the agent without guidance on how to specify it. The example uses 'Acute care' but does not connect it 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 the tool's purpose: benchmarking GPO contract performance and building supply chain cost reduction cases for a hospital board. It uses a specific verb ('benchmarking') and a specific resource ('GPO contract performance'), clearly distinguishing it from many sibling tools that cover different domains.

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

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

The description provides clear context on when to use the tool ('when benchmarking GPO contract performance or building a supply chain cost reduction case') but does not explicitly exclude scenarios or mention alternative tools. Given the large sibling set, the usage guidance is sufficiently specific.

Agents often have multiple tools that could apply. Explicit usage 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 the description adds value by detailing what the tool returns (top vendors, AI consensus narrative, sample size) and providing a realistic example. It doesn't disclose limitations like required inputs beyond category, but the read-only nature reduces the need for extensive behavioral notes.

Agents need to know what a tool does to the 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 succinct at three sentences plus a concrete example, with no wasted words. It front-loads the use case and immediately provides value.

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

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

Given the simplicity (one param, no output schema), the description covers the tool's function and return types adequately through the example. It could specify the format of sample size or consensus narrative, but the example provides sufficient context for an agent to understand the output structure.

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

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

The input schema has 0% description coverage, but the description partially compensates by giving an example ('EHR category') that implies the 'category' parameter expects a healthcare technology category. However, it does not explicitly define the parameter or list valid values, leaving some 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's purpose: researching which vendors dominate AI recommendations in a healthcare technology category or validating health IT vendor selection. It provides a concrete example (EHR category with specific market shares), distinguishing it from sibling tools like get_category_ai_leaders which may be broader.

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

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

The description explicitly says 'Use when researching which vendors dominate AI recommendations in a healthcare technology category or validating a health IT vendor selection,' providing clear context. While it doesn't explicitly exclude other use cases, the healthcare-specific focus implies boundaries among siblings.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds source (CMS and Stratalize) and typical recovery rates, providing useful context beyond annotations.

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

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

Two sentences with a clear usage directive and example. Somewhat dense but no wasted words; could be slightly more structured.

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

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

Given simple tool with annotations, description covers purpose, usage, example, and source. No output schema, but example illustrates expected output. Sufficient for a 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% and description does not explicitly explain parameters. It implies category values (e.g., EHR, staffing) via examples but does not map clearly to vendor_name parameter.

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

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

Description clearly states it returns market rates for healthcare vendors (EHR, staffing, etc.) by facility type, with specific examples. Distinguishes from sibling 'get_vendor_market_rate' by focusing on healthcare.

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

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

Explicitly says 'Use when benchmarking a healthcare vendor quote or preparing a supply chain contract negotiation.' Provides clear context, though no exclusion of alternatives.

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

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

Annotations indicate readOnlyHint=true and destructiveHint=false. Description adds value by detailing the specific scores returned (safety, readmissions, patient experience, mortality, star rating) and data source (CMS Care Compare). 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: usage context, output description, and an illustrative example. Every sentence adds value; 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?

For a tool with one parameter and no output schema, the description provides sufficient detail on the kind of data returned and a concrete example. Could mention if results are per-hospital or aggregated, but overall adequate.

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

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

Schema coverage is 100% with a clear description for hospital_name. The tool description adds no additional meaning beyond what the schema already provides.

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

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

The description specifies exact use cases (referral network, acquisition, competitive analysis) and lists returned metrics (safety, readmissions, etc.) with a concrete example, clearly distinguishing it 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?

Explicitly states when to use (evaluating hospital quality) but does not mention when not to use or provide direct alternatives among many healthcare 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 declare readOnlyHint=true and destructiveHint=false, so the safety profile is established. The description adds behavioral context by stating the data source (CMS HCRIS cost reports) and provides an example of expected output, but does not detail additional behaviors like authorization needs or rate limits.

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

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

The description is three concise sentences with no wasted words. It is front-loaded with the core purpose, followed by a concrete example and source citation. Every sentence adds value.

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

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

Given the tool has 2 parameters (1 required) and no output schema, the description provides sufficient context: it explains the output format (percentiles), gives an example calculation, and cites the data source. It is complete enough for an agent to understand what the tool returns and how to use 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?

Schema coverage is 0%, but the description compensates by mentioning parameters: 'by bed size and state' maps to bed_size (required) and state (optional). The concrete example (200-bed hospital) adds meaningful context beyond the schema, though individual parameter descriptions are not provided.

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

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

The description explicitly states what the tool does: 'Returns supply cost as percentage of operating expense at p25/p50/p75 by bed size and state.' It identifies the specific resource (hospital supply chain benchmark) and verb (returns), and distinguishes from sibling benchmarks by specifying the hospital context and CMS peer cohort.

Agents choose between tools based on descriptions. A clear purpose with a specific verb 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 usage guidance: 'Use when benchmarking hospital supply chain efficiency against CMS peer cohort or building a materials management cost reduction case.' It does not explicitly state when not to use or list alternatives, but the context is sufficiently 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?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds value by mentioning data sources (FRED and Census) and the leading indicator nature, but does not disclose update frequency, data quality, or return format. The added context is moderate beyond annotations.

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

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

The description is two sentences, front-loading key information (indicators, sources, leading indicator role) and specifying target audiences. 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?

For a tool with no output schema and two simple enum params, the description covers purpose and audience but lacks details on return format (e.g., time series, table), data frequency, and whether historical data is available. This leaves uncertainty for an AI agent invoking 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 input schema has two enums (region, structure_type) with 0% description coverage. The description does not explain any parameters. Although enum names are somewhat self-explanatory, the lack of parameter documentation in both schema and description means the agent misses clarity on acceptable values and scope (e.g., US Census regions).

Input schemas describe structure but not intent. Descriptions should explain 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 housing supply indicators (starts, permits, completions, absorption) from specific sources (FRED, Census) and identifies its role as a leading indicator for housing prices. It uniquely positions itself among sibling tools by specifying the resource and use case.

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

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

The description provides context on when to use it—as a leading indicator for housing prices 6-12 months ahead—and targets specific audiences (developers, lenders, etc.). However, it does not explicitly state when not to use it or compare to alternative sibling tools like get_rental_market_benchmark.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds only that the data is from HUD and free, which is minimal context beyond annotations.

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

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

Three concise sentences covering purpose, use cases, and source. 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?

Adequate for a simple data retrieval tool with no output schema. Tells what, why, source, and cost. Minor gap: no mention of data freshness.

Complex tools with many parameters or behaviors need more documentation. 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 both parameters. Description mentions 'by metro area and bedroom count' but adds no further meaning beyond the schema examples. Baseline 3 due to high 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?

Clearly states the tool returns HUD Fair Market Rents by metro area and bedroom count. Lists specific use cases like affordable housing underwriting and Section 8 compliance, distinguishing it from many sibling tools.

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

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

Implied usage via listed use cases, but no explicit when-to-use or when-not-to-use guidance. No mention of alternatives among sibling tools like get_rental_market_benchmark.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the tool is safe. The description adds valuable behavioral context: it is a 'static composite' with 'semi-annual update', indicating data freshness. This goes beyond annotations and helps the agent understand the data's timeliness.

Agents need to know what a tool does to the 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 plus an example. It front-loads usage context, states what is returned, gives a concrete example, and cites the source. Every sentence adds value, and there is no redundancy.

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

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

Given no parameters and no output schema, the description is fully complete for a snapshot tool. It explains what data to expect, provides an example usage, and notes the update frequency. The agent can confidently use the tool based on this description alone.

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

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

The input schema has no parameters, so the description does not need to explain parameters. The baseline for zero parameters is 4. The description adds value by explaining what data is returned (GDP growth, inflation, current account balance) and providing an example, which compensates for the lack of schema details.

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

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

The description clearly states the tool returns IMF WEO macro composites (GDP growth, inflation, current account balance) by country group, using a specific verb 'Returns' and resource. It distinguishes itself from sibling tools by specifying the exact data source and offering an example with numerical values, 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 explicitly states when to use the tool: 'when providing global macro context for an international expansion brief, country risk assessment, or board-level economic outlook presentation.' It provides clear context but does not mention when not to use it or suggest alternatives, which would be helpful given 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.