Stratalize Real Estate Intelligence

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

Annotations already indicate read-only and non-destructive. The description adds that public mode returns only framework stages, which is useful. Could mention any limitations like rate limits, but overall fine.

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

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

Two concise sentences, front-loaded with core purpose, no fluff.

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

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

For a no-parameter reference tool, the description covers output scope, mode distinction, and next steps for deeper details. Lacks output format but is adequate.

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

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

No parameters exist, so baseline 4. The description doesn't need to add parameter info.

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

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

The description clearly states the tool returns FS AI RMF Adoption Stages (INITIAL, MINIMAL, EVOLVING, EMBEDDED) and distinguishes public mode from org-scoped details, making its purpose distinct among 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 says public mode returns only framework stages and directs users to connect org MCP or dashboard for org-specific classification, providing clear when-to-use and alternatives.

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

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

No annotations exist, so the description must carry the full burden. It states that output is JSON and mentions 'narrative fallbacks' and 'multi-platform belief synthesis', but does not explain what these entail behaviorally (e.g., data sources, rate limits, error cases, side effects). Critical behavioral traits are missing.

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

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

The description is two sentences and reasonably concise, but the second sentence is fragmented ('Example default themes cost optimization') and jargon-heavy ('Multi-platform belief synthesis'). It could be clearer without increasing length significantly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. 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 low schema coverage, the description should provide a complete picture. It lists return fields but does not explain their meaning, scales, or how to interpret them. No usage examples or notes on valid topics/categories are given. The agent lacks enough context to use the tool correctly without additional knowledge.

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

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

The schema has 2 parameters (topic required, category optional) with 0% coverage from schema descriptions. The description only implicitly references 'topic' and mentions an unclear example, but does not explain the 'category' parameter or provide any specification for valid values or format. The description fails to compensate for the lack of schema descriptions.

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

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

The description states it returns specific JSON fields (consensus_score, sentiment_mix, etc.) for researching a business topic or vendor, which clearly indicates the tool's purpose. However, the phrase 'from AI citations with narrative fallbacks' is somewhat vague, and the example 'default themes cost optimization' is fragmented, slightly reducing clarity.

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

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

The description only says 'for analysts researching a business topic or vendor' but does not provide any explicit guidance on when to use this tool versus its many siblings. No when-not-to-use or alternatives are mentioned, leaving the agent to infer usage from the name alone.

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

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It only lists data topics and does not mention safety (read-only), permissions, rate limits, or error handling. The agent has no awareness of side effects or operational constraints.

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

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

The description is one sentence plus a target audience line, totaling 25 words. It is concise and front-loads the core purpose. The bulleted list of data types aids quick scanning, though it could be formatted more cleanly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. 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 data types but does not explain the tool's return format, pagination, or behavior. With no output schema, the agent lacks information on what to expect (e.g., a JSON object, arrays, fields). The missing parameter descriptions further reduce completeness. For a tool with two simple enum parameters, this is insufficient.

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

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

Schema coverage is 0% with two parameters (focus, institution_type). The description does not mention either parameter or explain how they filter the output. The enums in the schema provide some guidance, but the description adds no value beyond the schema, failing to compensate for the low 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 'AML regulatory benchmarks' and lists specific data types (FinCEN SAR, OFAC SDN, etc.), which distinguishes it from siblings like get_bank_regulatory_benchmark. It explicitly identifies the target audience, adding clarity. However, it could be more precise about what 'benchmarks' entail (e.g., data ranges, units).

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

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

The description mentions the target audience ('compliance agents and financial institution risk officers') implying use cases, but gives no explicit guidance on when to use this tool vs alternatives. With many sibling tools, including a closely related one (get_bank_regulatory_benchmark), this omission makes it harder for an agent to select correctly.

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

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

With no annotations, the description carries full burden. It discloses the data source (static ASCA + CMS 2024 table), indicating it is not real-time, and specifies return format (JSON). This is clear and honest. However, it could explicitly state it is read-only and non-destructive.

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

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

Two sentences deliver core purpose, data source, and an example. No fluff. However, structuring the information (e.g., listing returned fields, input constraints) could improve scanability without adding length.

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

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

The description covers what the tool returns, data source, and provides a numerical example. However, it lacks input validation guidance (e.g., accepted state codes or specialties) and does not detail the output JSON structure, which is significant since there is no output schema. The description is adequate but leaves gaps.

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

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

The input schema has two untyped string parameters (state, specialty) with 0% description coverage. The description only references 'by specialty' and gives an orthopedics example but does not explain valid values, format, or state parameter usage. The description adds minimal semantic value beyond the sparse schema.

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

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

The description clearly states the tool returns JSON with cost_per_case_median and revenue-mix percentages for ASC administrators by specialty, sourced from static ASCA + CMS 2024 tables. The example for orthopedics further clarifies the use case. It distinguishes itself among many sibling benchmarks by focusing specifically on ASC cost benchmarking.

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

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

The description implies use for ASC cost benchmarking but does not explicitly state when to use this tool versus other benchmarks like get_hospital_supply_chain_benchmark or get_healthcare_category_intelligence. No exclusions or alternative recommendations are provided.

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

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

No annotations provided, so description carries full burden. It indicates a read-only lookup from public aggregate data. However, it does not disclose data recency, response structure, or behavior when optional parameters are omitted. Adequate but not detailed.

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

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

Two sentences efficiently convey purpose, granularity, source, and use case. No redundancy or extraneous information. Front-loaded with key details.

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

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

For a simple benchmark tool with no output schema, the description explains the output concept (total fees and percentage of revenue) and segmentation. It lacks return format details (e.g., single value vs. table) but is largely sufficient given the tool's simplicity.

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

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

Schema description coverage is low (33%). The description hints at the role of annual_revenue_usd and auditor_tier by mentioning revenue band and auditor tiers, but does not mention the industry parameter. It adds some meaning beyond the schema but misses a key parameter.

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

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

The description clearly states it provides audit fee benchmarks (total fees and percentage of revenue) broken down by revenue band and auditor tier, with source and target users. This distinguishes it from other benchmark tools like get_inflation_benchmark or get_salary_benchmark.

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

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

Mentions usage by CFOs and audit committees in RFPs and fee negotiations, implying context but lacking explicit when-to-use or when-not-to-use guidance. No alternatives are discussed 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?

With no annotations, the description carries full burden. It discloses that the tool returns JSON with specific financial metrics and that it is FDIC-sourced, but lacks details on data freshness, permissions, or rate limits.

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

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

Two sentences plus a short phrase, no wasted words. First sentence states purpose and output, second gives an example, third acts as a label. Extremely 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?

For a simple one-parameter tool without output schema, the description covers purpose, data fields, source, and target audience. Could mention data freshness or error handling, but overall sufficient.

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

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

The only parameter, bank_name, has schema coverage of 100% with examples. The description reinforces its use by stating 'querying FDIC-sourced intel by bank_name', but adds little 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 specific financial data (assets, deposits, capital ratios, loan quality) sourced from FDIC, for bank credit analysts, by bank_name. It distinguishes itself from siblings like get_bank_regulatory_benchmark by specifying the exact data fields and source.

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

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

The description explicitly targets bank credit analysts and mentions FDIC-sourced intel by bank_name, implying when to use. It provides an example context. However, it does not state when not to use or mention alternatives.

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

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

Description discloses data source (FDIC call report public aggregates) and that it is aggregated by asset size tier, indicating a read-only operation. However, there is no mention of update frequency, authorization, or limitations, and no annotations are provided to fill this gap.

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

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

The description is concise (two sentences), front-loaded with key information, and contains no redundant information. Every sentence adds value.

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

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

For a simple benchmark tool with two enum parameters and no output schema, the description is fairly complete: it covers metrics, grouping, source, and audience. It does not explain return format or include bank_type, but overall it provides sufficient context for an agent to understand the tool's purpose and usage.

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

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

Schema has 0% parameter description coverage. Description explains 'by asset size tier' for the asset_size_tier parameter but does not mention the optional bank_type parameter, leaving its purpose unclear. The description adds some value but does not fully compensate for the lack of schema descriptions.

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

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

Description clearly states it provides bank regulatory capital and financial performance benchmarks, listing specific metrics (CET1, NIM, etc.) and grouping by asset size tier. It distinguishes from siblings like get_aml_regulatory_benchmark by specifying focus on capital and performance 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?

Description implies usage for bank CFOs, risk officers, and analysts but does not explicitly state when to use this tool over alternatives. No exclusions or comparisons to siblings are provided.

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

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

No annotations are provided, so the description carries the full burden. It indicates the model is static and composite, and the output is JSON. However, it does not disclose whether the tool is read-only, destructive, or requires authentication, nor specify response structure or side effects.

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

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

The description is two sentences, front-loading the list of outputs and key characteristics. Every sentence adds value without repetition, making it highly concise.

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

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

Given the tool has 3 parameters, no output schema, and no annotations, the description covers the output content and static nature but lacks details on parameter influence, return format, or procedural steps. It is adequate for a simple risk screen but not fully complete.

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

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

Schema coverage is only 33% (only level_4_5_percentage has a description). The tool description does not explain how the parameters (specialty, annual_claim_volume, level_4_5_percentage) influence results. The description adds no meaning beyond the schema's minimal descriptions.

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

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

The description clearly states the tool returns JSON E/M mix benchmarks, upcoding risk heuristics, OIG priority audit themes, RAC watchlist, and a checklist for HIM and compliance leads. It uses specific verbs and resource names, distinguishes itself from siblings by focusing on billing coding risk and revenue integrity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb 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 'Static composite model — not claim-level coding,' which tells the agent when not to use it (for claim-level detail). It implies usage for HIM and compliance leads conducting a revenue integrity risk screen, but does not explicit state when to use vs alternatives.

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

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

No annotations are provided, so the description carries the full burden. It discloses a behavioral default (momentum_4week ~1.8 with sparse history) and a heuristic ('trend up or down'), adding value beyond the basic schema, but does not cover other aspects like read-only nature, rate limits, or error conditions.

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

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

The description is two sentences covering output, default behavior, and purpose. It is efficient but the second sentence slightly restates the first, and the structure could better front-load the most critical information for selection.

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

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

Given no output schema or annotations, the description provides a reasonable overview of outputs and a key behavioral nuance (default). However, it lacks details like date range, data frequency, or precision of the score series, leaving gaps for an agent to invoke correctly.

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

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

The parameter 'brand_name' is required with no schema description. The description adds no meaning about what constitutes a valid brand_name, expected format, or how to handle variations. With 0% schema description coverage, the description fails to compensate.

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

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

The description clearly states it returns JSON momentum_4week, trend_direction, week_over_week score series for marketers. It mentions a heuristic for trend direction and a default value when history is sparse, distinguishing it from sibling benchmark tools that focus on 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 mentions target users ('for marketers') and a use case ('brand trajectory monitoring'), but it does not explicitly state when to use this tool versus alternatives, nor does it provide when-not-to-use guidance or mention sibling tools.

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

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

No annotations are provided, so the description carries the full burden. It states the data is static and returns JSON, but does not disclose read-only status, side effects, or required permissions.

Agents need to know what a tool does to the 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 single-sentence description is concise, front-loading key information (returns JSON, static benchmark) and includes all essential details without redundancy.

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

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

Given no output schema and moderate complexity, the description covers what the tool returns, the parameters, and target users. Missing details on the output structure are acceptable as no output schema exists.

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

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

The description adds meaning by explaining that avg_contract_value_usd is used for LTV:CAC calculation and references channel efficiency notes tied to gtm_motion. Schema coverage is 67%; the description compensates for the undocumented gtm_motion parameter.

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

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

The description clearly states the tool returns JSON CAC payback ranges, LTV to CAC ratio guardrails, and channel efficiency notes, targeting SaaS CMOs and CFOs. It specifies the resource and differentiates from sibling tools dealing with other benchmarks.

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

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

The description implies usage for benchmark retrieval by industry and gtm_motion, but does not explicitly state when to use vs. alternatives, nor provide exclusions or prerequisites.

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

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

No annotations are provided, so the description carries full burden. It mentions data sources (CBRE and JLL quarterly surveys), which adds some transparency, but does not disclose behavioral traits such as data freshness, rate limits, authentication requirements, or side effects (though read-only is implied).

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

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

The description is two sentences with no extraneous words. The first sentence defines what the tool returns, the second provides source and audience context. Every word earns its place.

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

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

Given the tool's moderate complexity (3 enum parameters, no output schema), the description covers purpose and source but lacks explanation of return values or structure. Without annotations, it is marginally adequate but leaves gaps for agent inference.

Complex tools with many parameters or behaviors need more documentation. 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 0%, the description must add meaning. It mentions parameters (asset class, market tier, geography) in the first sentence but does not explain their meaning beyond the enum names. It provides no additional context for the allowed enum values or how they affect results.

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

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

The description clearly states what the tool does: returns commercial real estate cap rate benchmarks by asset class, market tier, and geography. It also provides data sources and target audience, making purpose highly specific and distinguishable from numerous sibling benchmark tools.

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

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

The description implies usage context by stating the tool is used by CRE acquisition teams and asset managers for property pricing and portfolio valuation. However, it does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives among siblings.

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

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

The description discloses that results are ordered by mention_count, limited to 15, and include a composite fallback (e.g., Salesforce 42 mentions). It also says 'Unprompted AI leader mentions', indicating no additional prompt needed. However, it does not explain error handling, data freshness, or authorization requirements. With no annotations, more details would be beneficial.

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

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

The description is two sentences, but the first is lengthy and includes an example, making it slightly dense. It is functional but could be streamlined for quicker parsing by an AI agent.

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

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

For a tool with one required parameter, no output schema, and no annotations, the description should provide more context about return structure, error states, or data sources. It mentions a fallback but not its conditions. The tool is simple, but completeness is limited.

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

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

The only parameter 'category' is a string with 0% schema description coverage. The description mentions it is a 'category query' but does not provide examples, allowed values, or format expectations. Given the high schema coverage gap, the description should offer more parameter guidance.

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

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

The description clearly states the tool 'Returns JSON leaders ranked by mention_count (up to 15) for brand strategists from ai_citation_results or composite fallback', specifying the output format, ranking criterion, limit, and data source. It differentiates itself from sibling tools by focusing on AI leader rankings in a category, though it could be more concise.

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

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

No explicit guidance on when to use this tool versus alternatives like get_sector_ai_intelligence or get_category_disruption_signal. The description implies it is for 'brand strategists' seeking share-of-voice leaderboards, but does not specify exclusions or compare to siblings.

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

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

With no annotations, the description carries full burden. It reveals output format (JSON, score 0-1, evidence strings), an example default (~0.55), and static nature. However, it lacks details on data sources, update frequency, or side effects, leaving behavioral aspects partially undisclosed.

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

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

The description is extremely concise—two sentences plus a short disclaimer—with no redundant text. Key information is front-loaded: output type, score range, audience, methodology, and a clarifying statement.

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

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

For a simple tool with one parameter, no output schema, and no annotations, the description covers essential aspects: output format, range, example, audience, and methodology. It omits evidence string structure and potential constraints, but overall is adequate.

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

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

Schema description coverage is 0%, and the description does not explain the 'category' parameter beyond its name. It does not specify what categories are valid (e.g., product categories, industries, or keywords), leaving the agent to infer meaning from context.

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

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

The description clearly states it returns a disruption_risk_score (0-1) with evidence strings for product strategists, using citation-volume heuristics. It explicitly distinguishes itself from equity research, and the purpose is specific and unique among sibling tools.

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

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

The description implies use for product strategists assessing AI disruption risk and warns against use in equity research. However, it does not explicitly compare to related sibling tools like get_competitive_displacement_signal or get_market_structure_signal, leaving some ambiguity.

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

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

No annotations provided, so description carries full burden. It discloses that the tool returns a static model (not org-specific) and gives example output values. However, it does not mention data freshness, update frequency, or any other behavioral traits like rate limits or authentication requirements. Adequate but incomplete.

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

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

The description is three sentences, front-loaded with the main purpose, includes a clarifying note about the model type, and provides an example. Every sentence adds value; no wasted words.

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

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

Given the tool has three parameters (one described), no output schema, and no annotations, the description should cover more. It explains the output shape but does not clarify the role of 'industry', the format of 'company_size', or differentiate from numerous similar sibling benchmarks. Significant gaps remain.

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

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

Schema coverage is 33% (only 'category' described). The description adds context that 'company_size' is used for filtering but does not explain 'industry' or accepted values. It partially compensates for missing schema descriptions but leaves ambiguity for two parameters.

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

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

The description clearly states it returns median monthly spend benchmarks with percentile ranges and sample size for finance teams, specifying the resource (software category spend) and the filtering dimension (company_size). It distinguishes itself from sibling tools like 'get_industry_spend_benchmark' by focusing on software categories.

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

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

The description implies when to use: for industry composite benchmarks, not org-specific spend. However, it lacks explicit guidance on when not to use or alternatives among siblings, such as 'get_industry_spend_profile' or 'get_spend_by_company_size'. Usage context is implied but not explicit.

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

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

With no annotations provided, the description adds context about the data being synced and not being legal advice, but does not fully disclose behavioral traits like rate limits, pagination, or destructive effects. The disclaimer helps but the transparency is incomplete.

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

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

The description is three sentences, front-loading the core function. The third sentence ('Consumer finance complaint surveillance') is somewhat redundant as it repeats the domain. Overall it is concise and to the point.

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

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

Given no output schema and no annotations, the description provides sufficient context about the return type (rollup JSON) and content (complaint volume and issue themes). It lacks explicit field lists but is reasonably complete for a simple rollup 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 description mentions filtering by company_name and optional product, which mirrors the schema but adds little extra meaning. Schema coverage is 0%, meaning the description should compensate, but it only minimally restates the schema fields without detailing format 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?

The description clearly states it returns CFPB consumer complaint rollup JSON for risk teams by company_name with optional product filter. It distinguishes itself from the many sibling tools by focusing specifically on consumer finance complaints, leaving no ambiguity about its function.

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

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

The description implies usage for risk teams needing CFPB complaint data, but lacks explicit guidance on when to use this tool versus alternatives. No when-not-to-use or alternative tools are mentioned, leaving the agent to infer context.

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

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

No annotations are provided, so the description carries full burden. It states it provides 'Live TVL' but does not disclose data source freshness, rate limits, authentication needs, or any side effects. Behavior beyond output content is absent, leaving significant gaps.

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

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

The description is two sentences, efficiently front-loading the core purpose in the first sentence and listing key features in the second. Every phrase adds value with no redundancy or fluff.

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

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

Given the moderate complexity of 2 optional parameters and no output schema, the description lists specific metrics but omits details on return format, pagination, or data interpretation. It is adequate for a simple data retrieval tool but lacks completeness in explaining the response 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?

With 0% schema description coverage, the description compensates partially by mentioning 'rankings' and '1D/7D change' which relate to the sort_by enum values. However, it does not explain the 'limit' parameter or default behavior, so meaning is only marginally enhanced 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 live TVL data by blockchain, listing specific chains and metrics like rankings, 1D/7D change, protocol counts, and comparative dominance. It distinguishes itself from sibling benchmark tools by focusing on this specific financial metric with unique comparison features.

Agents choose between tools based on descriptions. A clear purpose with a specific verb 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 x402 agent context but lacks explicit guidance on when to use this tool versus other benchmark tools. No alternatives or exclusions are mentioned, though the unique focus on chain TVL provides implicit context.

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

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

No annotations are provided, so the description must disclose behavioral traits. It mentions sources but does not state whether data is real-time, cached, or requires authentication. There is no mention of rate limits, data freshness, or any potential side effects, which is a significant gap for a data retrieval tool.

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

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

The description is extremely concise: two sentences covering purpose, examples, sources, and audience. It front-loads the core purpose and wastes no words. Ideal for quick agent 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?

While the description covers what the tool does and its sources, it lacks information about the output format (e.g., single benchmark value, array of scenarios). Given no output schema, more details on return structure would improve completeness. Still, for a benchmark tool, the description is minimally adequate.

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

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

The description does not explain individual parameters beyond listing risk types implicitly. With 0% schema description coverage, the agent must infer that 'region', 'risk_type', and 'property_type' are used for filtering. No examples or format hints are provided, making it harder for agents to construct valid inputs.

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

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

The description clearly states it provides climate financial risk benchmarks for physical and transition risks, with specific examples (flood, hurricane, carbon pricing). The name 'get_climate_risk_benchmark' aligns well, and it distinguishes itself from the many sibling tools by focusing on climate-specific risks.

Agents choose between tools based on descriptions. A clear purpose with a specific verb 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 ('ESG and risk agents') and data sources (FEMA NFIP, NGFS scenarios), which helps agents decide when to use it. However, it lacks explicit exclusions or comparisons to alternative tools, such as the generic get_esg_benchmark.

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

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

With no annotations, the description must disclose behaviors. It shares 'No login' (auth requirement) but omits rate limits, data freshness, return format details, error handling, or concurrency behavior. 'Example acute facility cost curve vs peers' hints at output but is vague.

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

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

The description is three sentences, front-loading the core purpose. Minor redundancy: 'benchmark pack' repeats 'benchmarks' from the first sentence. Could be tightened but remains efficient.

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

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

Given no output schema and many siblings, the description covers domain, metrics, and filters but lacks explanation of the output structure, the hospital_type parameter, and explicit usage guidance relative to other benchmark tools. It is adequate but has clear gaps.

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

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

Schema coverage is 0%, so description should explain parameters. It mentions filtering by bed_size and state, but the hospital_type parameter is not described. This omission reduces clarity for the third parameter.

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

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

The description clearly states the tool returns JSON CMS cost report benchmarks for hospitals, specifying metrics (IT, labor, supply chain, cost per adjusted patient day) and filters (bed_size, state). It distinguishes itself from siblings by explicitly naming 'CMS' and 'HCRIS-style.'

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

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

The description implicitly indicates usage for hospital CFOs and ops needing benchmark data, and notes 'No login' suggests open access. However, it does not explicitly state when to use this tool versus similar siblings like get_hospital_supply_chain_benchmark or get_ehr_cost_per_bed.

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

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

No annotations are provided, so the description must disclose behavior. It mentions returning aggregate JSON and applying filters, but omits details like pagination, rate limits, authorization, or data freshness. It correctly implies a read-only operation but lacks comprehensive transparency.

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

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

Three sentences, all informative, with the main action front-loaded. Each sentence adds value: what it does, for whom, and how it differs from individual attestations. No extraneous 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 moderate complexity, the description adequately conveys the tool's purpose and main filters. It could elaborate on the return structure (e.g., aggregate fields), but is sufficient for an AI agent to select 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 schema has 0% description coverage, so the description must compensate. It explains that recipient_name is the main filter and that program_year and manufacturer_name are optional, providing basic context. However, it does not describe valid value ranges, formats, or enum 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 'Returns CMS Open Payments aggregate JSON' with specific filters (recipient_name, optional program_year, manufacturer_name) and distinguishes from individual attestations. It is specific and differentiates this tool 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?

The description targets 'compliance teams' and clarifies it provides 'Sunshine Act payment rollups — not individual attestations,' which helps guide usage. However, it does not explicitly compare to sibling tools or state when not to use it.

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

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

No annotations provided; description discloses it's static and educational, but lacks details on authentication, 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?

Two concise sentences, front-loaded with key information, 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?

No output schema and missing parameter explanations; description does not fully equip an agent for correct invocation despite clear purpose.

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

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

Three parameters with 0% schema description coverage; description does not explain state, hospital_name, or current_star_rating at all.

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

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

Description clearly states the tool returns CMS Hospital Star methodology notes, domain weights, etc., and emphasizes it's a static educational pack, distinguishing it from live data 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?

Hints at non-live nature but does not explicitly name alternatives or specify when to use vs siblings like get_hospital_care_compare_quality.

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

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

With no annotations, description provides update frequency ('Updated daily'), source ('FRED'), and time granularity ('Weekly and monthly'). Does not describe response format, but adds valuable 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?

Two sentences: first covers commodities and metrics, second provides source and audience. Extremely efficient, front-loaded, 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?

Provides essential context (commodities, source, frequency, audience) but omits guidance on the category parameter. For a simple tool, this gap reduces completeness.

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

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

Schema coverage is 0% for the category parameter, and description does not mention or explain how to use it. Agent cannot infer parameter meaning from description alone; relies solely on enum values in schema.

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

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

Clearly specifies it provides live commodity price benchmarks for a defined set (WTI crude, natural gas, gold, copper, wheat, soybeans), with weekly/monthly changes and inflation signal. Differentiates from siblings like get_gas_benchmark by offering a bundle.

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

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

Targets traders and macro analysts, giving clear context. However, does not explicitly advise when to use this tool versus specific sibling tools like get_gas_benchmark for single commodities.

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

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

No annotations provided, so description carries full burden. It mentions 'Synced public filing rollups', implying data source and freshness, but lacks details on rate limits, authentication needs, or behavior when no data found. Adequate but minimal.

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

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

Description is two sentences, front-loaded with purpose. The first sentence is somewhat long but conveys necessary information without fluff. Minor verbosity does not detract significantly.

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

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

Given no output schema and no annotations, the description is incomplete. It mentions JSON but not the structure. It does not explain what 'disclosure aggregates' are, nor provide details on data source frequency or parameter constraints. Significant gaps remain.

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

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

Schema coverage is 0%, so description must compensate. It lists the four parameters (company_name required, optional job_title, state, fiscal_year) but provides no additional meaning like allowable formats, ranges, or examples. This is insufficient for reliable agent invocation.

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

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

Description clearly states the tool returns DOL LCA and H-1B wage disclosure aggregates for comp analysts, with specific filters (company_name required, optional job_title, state, fiscal_year). It distinguishes from siblings like get_employer_h1b_wages by mentioning 'employer-sponsored wage transparency lookup'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb 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 implicitly targets comp analysts and indicates filtering by company_name and optional fields, providing clear context for use. However, it does not explicitly state when to avoid this tool or mention alternatives.

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

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

No annotations exist, so description carries full burden. Mentions fallback behavior ('May fall back to Microsoft or Google style rows') and 'Switching narrative signal' but lacks details on side effects, rate limits, or response format variability.

Agents need to know what a tool does to the 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. However, the phrases 'May fall back to Microsoft or Google style rows' and 'Switching narrative signal' are cryptic and reduce clarity without adding clear 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 single parameter and no output schema, description covers basic purpose but leaves gaps: response format details, meaning of 'switching narrative signal', and when fallback occurs. Sufficient for simple use but incomplete for a competitive intel 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 describes vendor_name as string with 0% coverage (no description). Description mentions 'target_vendor' but doesn't clarify format, constraints, or examples. As schema provides no semantics, description fails to compensate meaningfully.

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

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

Clearly states it returns JSON with top_replacing_vendors, mention counts, and evidence_snippets for competitive intel. Specifies the target audience and use case (rip-and-replace of target_vendor). Slightly vague with fallback and switching narrative, but overall distinct 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?

Implied usage for competitive displacement tracking, but no explicit when-to-use or alternatives provided. Does not differentiate from siblings like get_vendor_risk_signal or get_market_intelligence_brief.

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

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

No annotations provided, yet description only lists data components and sources. Does not disclose whether the tool is read-only, latency, authentication, or any operational constraints. The 'live material cost escalation signals' hint at real-time nature but lack clarity.

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

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

Two sentences plus audience line efficiently convey purpose, components, and sources. Front-loaded with key terms. Minimal waste, though a slight expansion on output shape could be beneficial.

Shorter descriptions cost fewer tokens and are easier for agents to parse. 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 main outputs (hard cost, soft cost, contingency, escalation) and sources, but omits construction_class parameter entirely. No output schema exists, yet description does not provide expected return format or unit details. Adequate but incomplete.

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

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

Description adds context for region and building_type by mentioning building type and region in the output, but does not explain the construction_class parameter (0% schema coverage). The meaning of enum values is not elaborated, leaving some parameters under-specified.

Input schemas describe structure but not intent. Descriptions should explain 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 tool provides construction cost benchmarks with specific components (hard cost, soft cost, contingency, escalation) and sources. It is distinct among siblings but does not explicitly differentiate from other benchmark tools.

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

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

Identifies target audience (developers, lenders, project owners) but offers no when/when-not guidance or comparisons to alternatives. Usage context is implied rather than explicit.

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

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

No annotations are provided, so the description carries full burden. It only states data source (FRED) and lists indicators, but lacks details on safety (read-only), rate limits, data freshness, or any behavioral traits beyond the basic retrieval nature.

Agents need to know what a tool does to the 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 covers data sources, second covers usage. Front-loaded with key information, no extraneous text. Every sentence earns its place.

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

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

Given one optional parameter and no output schema, the description covers data source and intended use. However, it omits return format, error handling, and data freshness. Adequate but leaves gaps for a production 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 schema defines one parameter 'focus' with enum values, but the description adds meaning by listing concrete metrics (sentiment, spending, saving) that correspond to those values. Though not explicitly mapping, it enriches understanding beyond the raw schema.

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

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

The description clearly states it retrieves live consumer sentiment benchmarks from FRED, listing specific indicators (University of Michigan sentiment, Conference Board confidence, etc.). This distinctively positions it among many sibling get_*_benchmark tools.

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

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

Mentions usage for GDP and equity agents with strong/moderate/weak signal, providing clear context. However, no explicit guidance on when not to use or comparison to alternatives like get_inflation_benchmark or get_macro_market_signal.

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

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

No annotations are provided, so the description must carry behavioral transparency. It mentions data sources (S&P Capital IQ, Damodaran) but does not disclose any behavioral traits such as read-only nature, required permissions, rate limits, or response format. The agent gains no insight into how the tool behaves beyond its purpose.

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

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

The description is four sentences, front-loaded with key metrics and use cases. It is informative without excessive verbosity, though it could be slightly more concise regarding the data source mention.

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

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

Given the lack of annotations and output schema, the description should provide more comprehensive context. It omits parameter details, behavioral traits, and response expectations, leaving significant gaps for a tool with only two parameters.

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

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

The schema includes two parameters with enums, but the description only generically mentions 'by credit rating tier and industry' without explaining the specific enum values, required status, or how they filter results. With 0% schema description coverage, the description does not 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 specifies the tool provides corporate leverage and debt benchmarks (Net Debt/EBITDA, interest coverage, debt maturity profiles) by credit rating tier and industry. It distinguishes from numerous sibling tools by focusing on corporate debt benchmarks rather than broader financial or industry-specific metrics.

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

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

The description mentions use cases (refinancing, covenant setting, credit rating management) but does not explicitly differentiate from or suggest alternatives among the many similar sibling tools. It lacks guidance on when to use this tool versus other debt or benchmark tools.

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

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

No annotations provided. Description mentions public data sources (implying read-only) but does not explicitly state read-only nature, auth needs, or error behavior. Adequate but could be more transparent.

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

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

Two concise sentences. First sentence immediately states purpose and key parameters. Second adds source and audience. No fluff.

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

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

Given no output schema, description hints at return structure (e.g., DSCR min, LTV max, spread ranges). Mentions required parameter (property_type) implicitly. Sufficient for a simple 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 description adds meaning by explaining that benchmarks are filtered by property type and lender type, and lists what metrics are returned. Adds value beyond enum definitions.

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

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

The description clearly states it provides CRE debt benchmarks (DSCR, LTV, spreads) by property and lender type, with sources and target audience. This distinguishes it from siblings like cap rate or corporate debt benchmarks.

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

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

Description specifies target users (CRE CFOs, capital markets teams) and context (structuring financings). It implies use cases but lacks explicit when-not-to-use or alternative tools.

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

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

Discloses daily updates and included metrics, but no annotations exist, and description lacks details on rate limits, authentication, or behavioral traits beyond basic facts.

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

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

Two concise sentences, front-loaded with key info, no redundancy. Efficiently conveys purpose and context.

Shorter descriptions cost fewer tokens and are easier for agents to parse. 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 data source, metrics, update frequency, and audience, but lacks output format or interpretation guidance for the distress signal, given no output schema.

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

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

Input schema has one enum parameter (rating_tier). Description mentions 'rating tier' context but does not list allowed values; schema coverage 0%, so description partially compensates.

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

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

Clearly states it provides live credit spread benchmarks from FRED ICE BofA indices, specifying metrics (OAS, TED, Treasury spread, distress signal). Distinguishes from sibling benchmark tools by 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?

Targets credit analysts and fixed income PMs, implying usage context, but does not explicitly state when to use vs alternatives or provide exclusions.

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

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

With no annotations, the description carries full burden. It discloses the data source (NCUA quarterly call report) and the metrics offered, but does not mention output format, whether data is historical/current, or any limitations such as update frequency. The behavioral traits are partially transparent but lack completeness.

Agents need to know what a tool does to the 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-loading the key content (benchmark metrics) immediately. It efficiently conveys purpose, source, and audience without unnecessary words. A minor improvement would be integrating param details, but overall it is concise and well-structured.

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

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

Given no output schema and no annotations, the description provides moderate completeness. It covers what data is provided, from what source, and for whom, but lacks detail on output structure (e.g., single value or table) and does not explain parameters. For a simple data retrieval tool, it is adequate but could be more comprehensive.

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

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

The input schema has two enum parameters (charter_type, asset_size_tier) with 0% description coverage. The description only hints at 'by asset size' for asset_size_tier but fails to explain charter_type or the enum values. Since schema coverage is low (0%), the description should compensate but does not, leaving parameter semantics unclear.

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

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

The description clearly states the tool provides credit union financial performance benchmarks including capital ratios, net interest margin, loan growth, and delinquency rates by asset size. It specifies the data source (NCUA quarterly call report) and target audience (credit union CFOs for exams and board reporting), making the purpose highly specific and distinguishing it from the many sibling tools covering other domains.

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

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

The description implies usage for credit union financial analysis, particularly for NCUA exam preparation and board reporting. However, it lacks explicit guidance on when to use this tool versus alternatives (e.g., bank benchmarks from other tools). No 'when not to use' or direct comparison to siblings is provided, leaving the usage context somewhat implied.

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

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

No annotations exist, so the description carries full burden. It discloses the computational nature (rolling correlation, Pearson, beta, dominance) and source (DeFiLlama), giving a clear behavioral picture. However, it does not discuss read-only status, limitations, or potential side effects, but the overall behavior is transparent.

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

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

The description is a single, information-dense sentence that front-loads key details (what, how, source, audience). Every phrase adds value, with no redundant or filler content. It is efficiently structured for quick parsing.

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

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

The description covers the main outputs (correlation matrix, pairs, beta, dominance, signal) but lacks details on return format, whether historical data is included, or any pagination. Given no output schema, more specificity would improve completeness, but the core information is present.

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

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

The sole parameter 'period' has an enum but is not explained in the description. The description mentions '30-day rolling' but does not clarify that the parameter controls the lookback window. With 0% schema coverage, the description should elaborate, but it does not, leaving the agent to infer meaning from the enum values and tool name.

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

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

The description clearly states the tool computes a 30-day rolling correlation matrix for BTC, ETH, and SOL, including Pearson pairs, beta, dominance, and diversification signal. It specifies the source and intended audience, making the purpose unambiguous and distinct from siblings.

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

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

The description provides usage context ('For crypto portfolio agents') but lacks explicit guidance on when to use this tool versus alternatives or when not to use it. No exclusions or alternative tool mentions are provided, relying on the user to infer from the tool name.

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

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

No annotations are provided, so the description carries the full burden. It mentions the data source (DeepDAO public data) but does not disclose behavioral traits such as whether the tool performs a read operation, limits on result size, or any potential side effects. The description only states what the tool does, not its behavior.

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

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

The description is short (3 sentences) and front-loads the purpose. It includes median benchmarks as a quick reference, which adds value. However, it could be more structured by separating purpose from example values.

Shorter descriptions cost fewer tokens and are easier for agents to parse. 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 (2 parameters, no output schema, no annotations), the description is insufficient. It fails to explain the return format, any constraints on parameter values, or how the benchmarks are computed. The agent would have significant gaps in understanding how to 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?

With 0% schema description coverage and no mention of parameters in the description, neither the schema nor the description adds meaning to the parameters. The two parameters ('sort_by' and 'min_treasury_usd') are completely undocumented in the description, leaving the agent without guidance on how to use them.

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

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

Description clearly states 'DAO treasury benchmarks' and lists specific metrics (treasury size, stablecoin percentage, runway, governance token concentration). It distinguishes itself from sibling tools like 'get_chain_tvl_benchmark' or 'get_defi_yield_benchmark' by focusing specifically on DAO treasury data.

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

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

The description implies usage for DAO treasury analysis but does not explicitly state when to use this tool versus alternative benchmark tools. No guidance on when not to use it or any exclusions is provided.

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

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

With no annotations, the description adds value by noting the response is Ed25519-signed, free, and requires no API key. This provides important behavioral context beyond the schema.

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

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

Three sentences, each informative: main purpose, optional parameters, default behavior, and API details. No wasted words, front-loaded.

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

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

Covers key outputs and filters, but lacks details on pagination, sorting, or response structure. Without an output schema, the description could provide more on the response format beyond the signed mention.

Complex tools with many parameters or behaviors need more documentation. 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 100% coverage with descriptions. The description adds further clarification: protocol is a DeFiLlama project slug substring, asset is a symbol substring. This enriches parameter meaning beyond the schema.

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

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

The description clearly states the tool returns a DeFi lending and stable yield benchmark with specific outputs (APY percentiles, TVL, chain, pool id), and optional filters. It distinguishes itself from siblings by being DeFi-specific.

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

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

Mentions optional filters and default universe (stablecoin-marked pools) but does not explicitly compare to alternatives or state when not to use. Lacks explicit guidance on when this tool is preferred over siblings like get_stablecoin_yield_benchmark.

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

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

No annotations are provided, so the description bears full responsibility. It discloses data sources (NAHB, ULI, industry composite) but does not mention read-only nature, authentication needs, rate limits, or other behavioral traits. The tool name suggests a safe GET operation, but this is implicit.

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

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

The description is concise with two sentences. The first sentence front-loads the core information, and the second adds sources. No unnecessary words. It could be more structured (e.g., bullet points) but is efficient.

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

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

Given no output schema and no parameter descriptions, the description is incomplete. It does not explain the return format (single value, table, etc.) or how to interpret the benchmarks. For a benchmark tool, this missing information is critical for correct usage.

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

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

The input schema has 0% description coverage, and the description does not elaborate on the parameters. It mentions 'by product type' but does not explain the product_type enum or market_tier. The description adds no value to the schema beyond its own text.

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

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

The description clearly states it provides 'Development pro forma benchmarks' and lists specific metrics (yield on cost, profit-on-cost, etc.). It identifies the resource and verb, and the target users (developers, lenders). However, it does not explicitly differentiate from the many sibling benchmark tools, though its focus is distinct.

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

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

The description mentions target users and use cases ('For developers underwriting new projects and lenders sizing construction loans'), which indicates when to use. It does not provide when-not-to-use or mention alternatives, but the context is sufficient for basic usage guidance.

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

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

No annotations are provided, so the description must carry the burden of behavioral disclosure. It reveals the data source (SEC EDGAR aggregate + Sloan accruals model) and academic standard, adding transparency. However, it lacks details on update frequency, rate limits, data accuracy caveats, or whether the output is static or dynamic.

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

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

The description is concise (two sentences plus target users) and front-loaded with the tool's purpose. Every sentence adds value: what it does, data source, and intended 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 the tool has 2 parameters and no output schema, the description provides a good overview but lacks details on the output format (e.g., table, percentiles, raw numbers), data freshness, or interpretation guidelines. This leaves room for ambiguity about what the agent will receive.

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

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

Schema description coverage is 0%, so the description must compensate. It mentions 'by sector' and 'revenue recognition risk,' which relate to the two parameters (sector and revenue_recognition_model). This adds some meaning beyond the enum lists, but it does not explain the significance of each sector or how the revenue_recognition_model parameter affects 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?

The description clearly states that the tool provides 'Earnings quality and financial statement risk benchmarks' with specific metrics (accruals ratio, cash conversion, revenue recognition risk) by sector. This is a specific verb+resource+scope, and it distinguishes the tool from its many siblings, none of which focus on earnings quality.

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

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

The description targets CFOs, auditors, and analysts and specifies the use case: 'assessing financial reporting risk before M&A or investment.' This provides clear context for when to use the tool, but it does not mention when not to use it or list alternative tools for similar purposes.

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

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

Describes return format and optional calculation, and gives an example source. However, no annotations are provided, and the description does not disclose data freshness, error handling, or behavior for unknown vendor names.

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

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

Three sentences with clear structure: purpose, example, and summary. No repetition or fluff, though slightly more format could help.

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

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

Covers return values and optional behavior, but lacks details on error cases, parameter validation, or what happens if vendor_name is not recognized. Adequate but not complete for a 3-param 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?

Adds meaning by explaining that bed_count and annual_cost trigger an optional gap calculation, and provides an example value for vendor_name (Epic). This goes beyond the schema, though vendor_name constraints are not 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?

Clearly states it returns JSON with benchmark_cost_per_bed_median and optionally gap_vs_benchmark. The verb 'get' and resource 'ehr_cost_per_bed' are specific, but it does not explicitly distinguish from similar healthcare finance tools 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?

Implies usage for health IT finance benchmarking but lacks explicit when-not-to-use or alternative tools. Sibling list includes many other benchmarks, but no guidance is provided.

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

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

With no annotations, the description carries full burden. It discloses API key dependency and data staleness (not realtime). However, 'Energy price strip dependency' is vague, and there is no mention of rate limits, side effects, or result format beyond JSON.

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

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

Three sentences, each serving a purpose: main action, condition, and clarification. 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?

No output schema, so the description must compensate. While it specifies 'JSON' and mentions 'spot-price style,' the structure is not detailed. 'Energy price strip dependency' is cryptic. Overall adequate but could be clearer for an AI agent unfamiliar with energy markets.

Complex tools with many parameters or behaviors need more documentation. 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; schema coverage is 100%. The description adds no parameter info because there is none. It appropriately explains what the tool returns, meeting the baseline for no-parameter tools.

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

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

The description clearly states it returns EIA spot-price style JSON for commodity strategists, with a condition on API key configuration. The name matches. However, it does not explicitly differentiate from siblings like get_gas_benchmark or get_commodity_benchmark, though EIA specificity provides some 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?

Provides conditional guidance (API key required, empty/guidance otherwise) and clarifies it's not realtime. No explicit when-to-use or alternatives compared to sibling tools, leaving the agent to infer from the EIA 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 indicate readOnlyHint=true and destructiveHint=false, so the tool is safe to read. The description adds value by explaining the specific outputs (position, targets, invalidation levels, confidence scores), which goes beyond the annotations. There is 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 two sentences long, with the first sentence immediately stating the tool's purpose and the second providing a concrete example and target audience. 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?

Without an output schema, the description adequately explains the return values (position, targets, invalidation levels, confidence scores) and gives an example. It could be more complete by specifying the expected format for asset symbols or indicating data freshness, but it is sufficient for a simple tool with one optional parameter.

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

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

The input schema has one parameter 'asset' with description 'Asset symbol or "all" (default all)', achieving 100% schema coverage. The description adds context by naming the specific assets (BTC, SPY, TLT, Gold) and providing an example for Gold, which helps the agent understand valid inputs 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 specifies that the tool provides Elliott Wave position, targets, invalidation levels, and confidence scores for specific assets (BTC, SPY, TLT, Gold). It uses a specific verb ('get') and resource ('Elliott waves'), distinguishing it from many similar get_* tools in the same server.

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

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

The description states the tool is 'for traders and portfolio managers tracking technical market structure,' implying a use case. However, it does not provide explicit guidance on when to use this tool over alternatives (e.g., get_market_structure_signal) or when not to use it.

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

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

No annotations are provided, so the description carries the full burden. It states the tool returns JSON data but does not explicitly declare read-only behavior, rate limits, or data freshness. The description adds moderate context (DOL LCA filings, example companies) but lacks explicit safety or behavioral cues.

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

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

The description is two sentences, each adding distinct value: first defines purpose and output, second provides context and example. No redundant information or filler. Extremely 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 simple input (one parameter) and no output schema, the description covers the key outputs (prevailing wage stats, job titles, state mix). It could specify the data format (JSON implied) but is otherwise complete for the tool's complexity.

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

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

There is only one parameter (employer_name) with schema description providing examples. The tool description adds example companies but does not elaborate further. With 100% schema coverage, the description adds marginal value beyond the schema.

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

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

The description clearly states that the tool returns JSON prevailing wage stats, certified job titles, and state mix specifically for H1-B DOL LCA filings by employer name. It identifies target users (HR analytics/talent strategy teams) and distinguishes from sibling tools focused on other benchmarks.

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

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

The description implies use for HR analytics and talent strategy but provides no explicit guidance on when to use versus alternatives. It does not mention when not to use or suggest alternative tools, leaving the agent to infer from context.

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

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

With no annotations, the description carries full transparency burden. It only states data sources and metrics but omits behavioral traits like read-only nature, pagination, rate limits, or data freshness. The word 'get' implies retrieval, but the description does not explicitly declare idempotency or lack of side effects.

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

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

The description is a single sentence that front-loads the core purpose and lists key metrics, sources, and audience. It is concise with no fluff, but could be improved by briefly referencing the parameters to enhance informativeness without adding length.

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

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

Given the absence of output schema and annotations, the description lacks details on return format, pagination, or how parameters affect results. It fails to explain what happens when parameters are omitted (e.g., default behavior). For a tool with optional parameters and no output schema, this leaves agents guessing about invocation 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?

Input schema has two parameters (focus, sector) with 0% description coverage. The description only hints at sector filtering ('by sector') but does not mention the focus parameter or explain its values (carbon, social, governance, all). This forces agents to rely solely on the schema enum values without contextual enrichment.

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

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

The description clearly states it provides ESG benchmarks by sector with specific metrics (carbon intensity, net zero commitments, SBTi alignment, board independence, pay equity, ESG composite scores) and sources (EPA GHGRP, MSCI ESG methodology). This distinguishes it from sibling tools like generic get_carbon_benchmark or get_climate_risk_benchmark by explicitly naming the ESG dimensions and target audience.

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

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

No guidance on when to use this tool versus alternatives. The description mentions target users (sustainability agents, ESG analysts) but does not specify conditions or exclusions, nor does it mention other tools that might be more appropriate for specific use cases.

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

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

No annotations are provided, so the description carries full burden. It mentions 'composite mode' and 'implementation guidance' but does not disclose behavioral traits such as whether the tool performs read-only operations, has side effects, or requires authentication. The description is insufficient for safe invocation.

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

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

The description is a single sentence, which is concise, but it is dense and overloaded with terms like 'composite mode' and 'control library context' that are not explained. It could be restructured for clarity without sacrificing brevity.

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

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

For a tool with no output schema, the description should explain return values or expected output. It only states it provides 'reference coverage' and 'guidance' but lacks specifics. The parameter is not contextualized, and the overall completeness is low given the tool's potential complexity.

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

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

Schema description coverage is 0%, and the description does not mention the single optional parameter 'nistFunction' or explain its enum values. The description adds no value beyond the schema, failing to compensate for the lack of parameter documentation.

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

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

The description clearly identifies the tool as providing EU AI Act coverage with additional context like composite mode and non-org-specific scope. It distinguishes from sibling tools like 'get_uk_fca_coverage' by focusing on EU AI Act. However, the wording is somewhat jargon-heavy and lacks a strong action verb.

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

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

No guidance on when to use this tool vs alternatives. The description mentions 'non-org-specific' but does not provide explicit usage context, comparisons, or exclusions. Sibling tools exist for other regulations, but no differentiation is offered.

Agents often have multiple tools that could apply. Explicit usage 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 indicates the output is JSON and the data source is USASpending-synced, implying a read operation. However, it does not disclose limitations, data freshness, error behavior, or response structure. With no annotations, more detail would be beneficial.

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

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

The description is concise at three sentences, with the key information front-loaded. The second and third sentences add minor redundancy but do not harm clarity. Could be slightly more structured, but overall effective.

Shorter descriptions cost fewer tokens and are easier for agents to parse. 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 five parameters, no output schema, and no annotations, the description is incomplete. It lacks details on output format, pagination, expected data range, error responses, and any prerequisites, leaving gaps for the agent to infer.

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

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

The description adds context for the required vendor_name and three optional filters (agency, NAICS, fiscal year), compensating for the schema's low description coverage (20%). However, it omits the 'state' parameter and does not explain the meaning of each filter beyond listing them.

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

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

The description clearly states it returns USASpending-synced obligation rollups JSON, specifies the key parameter (vendor_name) and optional filters (agency, NAICS, fiscal year), and identifies the domain (federal vendor spend intelligence). It is specific and distinct from sibling tools that focus on benchmarks or market rates.

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

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

The description does not provide any guidance on when to use this tool versus alternatives, nor does it mention when not to use it or any prerequisites. There is no explicit context for selection among 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?

With no annotations provided, the description fully discloses that the probabilities are illustrative, conditioned on the latest FRED fed funds print, and explicitly not futures-implied market odds. This provides complete behavioral transparency.

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

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

The description is a single sentence that conveys the purpose, target audience, data source, and limitations without any redundant wording.

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

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

Given the simplicity of the tool (no parameters, no output schema, no annotations), the description completely covers what the agent needs to know: the output format, content, data source, and usage context.

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

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

The tool has no parameters, so the description does not need to add parameter information. The baseline of 4 applies as the description adds value beyond the schema.

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

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

The description clearly states the tool returns 'illustrative cut, hike, hold probabilities' for the next three meetings, specifically for macro educators. It distinguishes itself from sibling tools by emphasizing it is not futures-implied and is for scenario planning.

Agents choose between tools based on descriptions. A clear purpose with a specific verb 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 sets clear context: 'for macro educators' and 'scenario planning narrative only,' implying it should not be used for trading decisions. However, it does not explicitly state when not to use it or name 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?

With no annotations, the description carries the full burden. It discloses the data source (FRED) and update frequency (daily), which is helpful. However, it does not mention authentication requirements, rate limits, error behavior, or the shape of the response. Basic transparency is present but lacks depth.

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

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

The description is two sentences, front-loaded with essential information (what is returned, source, update frequency). Every part is relevant, and there is no wasted text.

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

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

Given the tool's moderate complexity (multiple benchmarks, no output schema), the description covers the main content and source. However, it lacks details on return format, parameter semantics, and potential pitfalls. It is mostly complete for a simple data retrieval tool but could be improved.

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

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

The schema has one parameter (base_currency) with three enum options, but the description does not explain what this parameter does or how it affects results. Given 0% schema description coverage, the description should compensate, but it only lists currency pairs without connecting them to the parameter. This ambiguity could lead to misuse.

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

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

The description clearly lists specific currency pairs (USD/EUR, USD/JPY, etc.) and additional data points (carry trade spread, rate changes), explicitly stating it provides live major currency pair benchmarks from FRED. This distinguishes it from other benchmark tools on the server, which cover different categories like inflation or commodities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb 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 FX rate benchmarks but provides no explicit guidance on when to use this tool versus alternatives, such as other financial benchmarks. It does not mention prerequisites, limitations, or when not to use it, leaving the agent to infer from context.

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

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

With no annotations, the description carries the full burden. It discloses that data is live, from public RPCs, requires no API key, and returns specific metrics. It does not cover rate limits or data latency, but the core behavioral traits are covered.

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

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

The description is four concise sentences, each adding value. It front-loads the purpose, details returns, then highlights a comparison, and ends with source and auth. No unnecessary words.

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

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

Given the simple schema and no output schema, the description covers purpose, return details, source, and authentication. It does not explain the 'x402 agent economy context' fully, but overall it is complete for an agent to decide and use the tool.

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

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

The schema has one enum parameter with 0% description coverage. The description mentions the chains but does not explicitly reference the parameter or its valid values. It adds limited meaning beyond the enum list.

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

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

The description clearly states 'Live gas price benchmarks' and specifies the chains (Ethereum, Base, Solana). It details the return values (Gwei, USD cost, congestion, x402 context) and includes a comparison. This distinguishes it from many sibling benchmark tools.

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

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

The description implies usage when gas price data is needed for the listed chains. It does not explicitly state when not to use or provide alternatives, but the context is clear given the tool's specific focus.

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

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

No annotations are provided, so the description must disclose behavioral traits. It mentions 'live' stats, 'JSON' return type, and 'subject to rate limits', but does not detail behavior on rate limit exceedance, authentication, or response structure. Some transparency is present but could be richer.

Agents need to know what a tool does to the 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 three short phrases that front-load the core action. Every part adds value without redundancy. It is well-structured for quick parsing.

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

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

Given the tool has one simple parameter and no output schema, the description adequately covers the basics: what it returns, for whom, and a constraint (rate limits). It is complete enough for a straightforward tool, though more details on output structure 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 'org_or_company' with 0% schema description coverage. The description adds meaning by calling it a 'footprint query' and indicating it's for an organization or company, but does not specify format or examples. This adds value beyond the bare 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 'live GitHub organization and repository stats JSON' for 'developer relations teams', using a specific verb ('returns') and resource ('GitHub stats'). It distinguishes itself from siblings, which are mostly financial or market benchmarks, by focusing on GitHub ecosystem data.

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

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

The description implies usage for developer relations teams needing GitHub stats via public API, and mentions rate limits. It does not explicitly state when not to use or name alternatives, but the context is clear given the diverse sibling tools.

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

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

No annotations are provided, and the description does not disclose behavioral traits such as whether the tool is read-only, if results are cached, or any rate limits. For a tool without annotations, the description should compensate by mentioning these aspects, but it only states the data returned.

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

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

Two concise sentences front-load key information (list of indices) followed by data types. No unnecessary words or repetition. Efficiently conveys purpose and scope.

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

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

Given the tool's simplicity (one optional param, no output schema), the description covers what data is returned (indices, YTD returns, P/E ratios, risk signal). However, it lacks information on return format (e.g., array of objects, data types), data freshness, or error handling. For a tool with no output schema, more detail on structure would improve completeness.

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

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

The single parameter 'region' has an enum but no schema description. The description lists indices by region (e.g., US, Europe, Asia, emerging), which partially explains the parameter's meaning. However, it does not explicitly state that the parameter filters results by region. Schema coverage is 0%, so the description adds some value but could be more direct.

Input schemas describe structure but not intent. Descriptions should explain 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 global equity index benchmarks, lists specific indices (S&P 500, Nasdaq, etc.), and mentions key metrics (YTD returns, P/E ratios, risk signal). The verb 'get' and resource 'global equity benchmark' make purpose explicit and distinguish from sibling tools focused on other asset classes.

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

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

No guidance on when to use this tool versus alternatives like get_public_market_multiples or get_macro_market_signal. The description implies it is for equity market data but does not specify when it is appropriate or inappropriate to use, nor does it mention alternative tools for similar purposes.

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

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

No annotations are provided, so the description must fully disclose behavioral traits. It mentions 'static' and 'not your invoices,' indicating it is not real-time or personalized, but it lacks details on authentication, rate limits, or side effects.

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

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

The description is concise, with two sentences that front-load key metrics. However, the second sentence is somewhat cryptic ('Static HFMA plus CMS-style composite — not your invoices') and could be clearer.

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

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

Given no output schema and only one parameter, the description should be more complete. It explains the model type but omits parameter semantics and output format, leaving gaps for the agent.

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

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

The input schema has one parameter ('category') with no description (0% coverage). The description does not explain the parameter's meaning, expected values, or usage, leaving the agent to guess.

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

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

The description clearly states the tool returns benchmark data for GPO contracts, including specific metrics like savings percentage, leakage, risk threshold, and top categories. It differentiates from sibling tools by specifying 'Acute care GPO savings model' and 'not your invoices,' but could be more explicit about the exact output structure.

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

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

The description implies usage for materials managers benchmarking group purchasing and clarifies it is a static model, not invoice-level data. However, it does not explicitly state when to use this tool versus alternatives or provide exclusions.

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

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

No annotations are provided, and the description only mentions data sources (AI citations or Epic/Meditech defaults) but does not disclose behavioral traits like required permissions, rate limits, or error handling. The agent is left uninformed about key behavior.

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

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

The description is short but poorly structured, with two standalone phrases at the end that seem tacked on. It could be more concise without losing clarity.

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

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

Given no output schema and limited annotations, the description partially explains return fields (vendors, ai_consensus, sample_size) but omits other metadata or structure. For a complex tool, this is adequate but not thorough.

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

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

The single parameter 'category' is documented only by name. With 0% schema description coverage, the description adds no explanatory value about valid values, format, or domain-specific meaning, failing to compensate for the schema gap.

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

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

The description states it returns JSON with top_recommended_vendors, ai_consensus blurb, and sample_size for health IT strategists, using AI citations or default styles. This clearly indicates the tool provides category intelligence, though the fragmented phrasing reduces precision.

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

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

The description offers no guidance on when to use this tool versus similar siblings like get_top_vendors_by_category or get_category_ai_leaders. It lacks explicit context for selection.

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

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

With no annotations, the description must disclose behavioral traits. It only states the return format and data sources (CMS, Stratalize), but omits authentication needs, rate limits, data freshness, or whether it is a read-only operation. The read-only nature is implied but not explicit.

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

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

The description is concise, consisting of two front-loaded sentences. It immediately states the tool's purpose, provides examples, and mentions data sources without extraneous detail. Every sentence adds value.

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

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

The description covers the tool's purpose, categories, and data sources, but lacks details on the output structure (e.g., fields, units) and does not explain the 'example maintenance $/bed style hints'. Given the absence of an output schema, more specificity is needed for full contextual 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 0% description coverage, and the description does not explain the meaning of 'vendor_name' or 'category'. While examples of categories are listed, the parameter semantics are not clarified, failing to compensate for the lack of schema descriptions.

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

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

The description clearly states the tool returns a JSON structured market-rate payload specifically for supply chain and IT sourcing across healthcare vendor categories like EHR, staffing, food, waste, and med-surg. It differentiates from siblings like get_vendor_market_rate by focusing on healthcare, and from specific tools like get_ehr_cost_per_bed by covering broader categories.

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

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

The description provides no guidance on when to use this tool versus alternatives such as get_vendor_market_rate or get_ehr_cost_per_bed. There is no mention of suitable contexts, exclusions, or prerequisites, leaving the agent to infer usage from the name alone.

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

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It implies a read-only operation by stating it returns data, but does not specify rate limits, data freshness, error handling (e.g., missing hospital name), or response structure. The mention of 'synced public data' hints at data recency but lacks specifics.

Agents need to know what a tool does to the 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 only two sentences. It front-loads the primary purpose and output, then adds an example of fields. No unnecessary words or repetition.

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

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

Given the lack of output schema and annotations, the description adequately covers the tool's main function and parameter. However, it omits details about the response format (e.g., structure, fields beyond examples), error behavior, and data source recency, leaving gaps in completeness for an agent to effectively invoke 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 100% with the single 'hospital_name' parameter clearly described in the schema. The description reaffirms the parameter's role but does not add additional constraints, formatting tips, or examples beyond the schema, so it meets the baseline but offers no extra semantic enrichment.

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

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

The description clearly states it returns JSON CMS Hospital Compare quality scores for clinical ops leaders, listing specific domains (safety, readmissions, patient experience, star rating) and mentioning example fields like mortality and HCAHPS. This distinguishes it from sibling tools like get_cms_star_rating that focus on star ratings alone.

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

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

The description does not provide any guidance on when to use this tool versus alternatives. It targets 'clinical ops leaders' but does not explain when to prefer it over similar tools like get_cms_facility_benchmark or get_cms_star_rating, and no exclusion criteria or prerequisites are mentioned.

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

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

Annotations absent, so description carries the burden. It mentions JSON output, CMS HCRIS source, and a median fallback (17%). However, it does not clarify if the operation is read-only, real-time, or any authentication needs. Adequate but incomplete.

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

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

Single sentence conveying essential details: output type, metrics, grouping, source, and fallback. No redundant information; highly efficient.

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

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

Covers output, source, fallback, audience, and grouping. Lacks explicit output structure (though implied) and example usage. For a simple benchmark tool with no output schema, this is relatively 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?

Description mentions parameters 'by bed_size and state', providing basic mapping. No details on valid values, units, or examples. Schema coverage is 0%, so description adds minimal meaning beyond parameter existence.

Input schemas describe structure but not intent. Descriptions should explain 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 supply cost percent of operating expense benchmarks (p25/p50/p75) for hospitals, specified by bed_size and state. This distinct focus on hospital supply chain differentiates it from numerous sibling benchmarks.

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

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

No explicit guidance on when to use this tool versus alternatives like get_cms_facility_benchmark or get_hospital_care_compare_quality. No exclusions or context for selection.

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

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

The description mentions data sources (FRED and Census) and that indicators are 'live', but does not disclose update frequency, output format, or whether the tool is read-only. With no annotations, this is adequate but not detailed.

Agents need to know what a tool does to the 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 adding usage context and audience. No unnecessary words.

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

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

Without an output schema, the description should explain what the returned data looks like (format, units, etc.). It does not, leaving the agent without essential information 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?

The description does not explicitly explain the parameters 'region' and 'structure_type', nor does it map 'market tier' to them. Schema coverage is 0%, so the description fails to add meaning beyond the enum choices.

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

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

The description clearly states the tool provides live housing supply indicators (starts, permits, completions, absorption) from FRED and Census, distinguishing it from other benchmark tools on the server.

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

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

The description notes it is a leading indicator for housing prices 6-12 months ahead and lists target users, providing clear context for when to use it, though it does not explicitly compare to sibling tools.

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

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

With no annotations provided, the description carries the full burden. It discloses the data source (HUD annual FMR) and that the tool is free, but does not mention whether authentication is needed, rate limits, or what happens if the metro area is not found. The behavioral details are adequate but minimal.

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

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

The description is concise with three short sentences. It front-loads the core purpose and parameters, then lists use cases. Every sentence adds value without redundancy.

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

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

For a simple lookup tool with no output schema, the description covers data source, parameters, and use cases. However, it does not hint at the return format (e.g., single dollar amount, annual values) or data freshness, leaving agents without complete expectations for the response.

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

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

The input schema has 50% description coverage: metro_area has examples but bedroom_count lacks any description. The tool description does not add meaning for bedroom_count (e.g., optional, range 0-4). The description fails to compensate for the undocumented parameter, limiting its 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 specifies the resource (HUD Fair Market Rents) and the filtering dimensions (metro area, bedroom count). It lists concrete use cases like affordable housing underwriting and Section 8 compliance, which helps distinguish it from sibling tools that provide general rental 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 this tool by listing regulatory and budgeting scenarios. However, it does not mention when not to use it or name alternative tools (e.g., get_rental_market_benchmark), leaving room for ambiguity in multi-tool environments.

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

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

With no annotations, the description effectively conveys that this is a read-only, non-destructive operation returning static JSON data. It mentions curated tables and pre-defined content, so agents understand no state changes or dynamic behavior.

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

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

The description is concise at two sentences, front-loading the essential purpose immediately. Every sentence adds value: first defines what it returns and its static nature, second clarifies coverage and intended use.

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

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

Given no parameters and no output schema, the description covers the primary outputs (GDP and inflation snapshot) and format (JSON). It could be more specific about the exact metrics, but for a simple static tool it is adequate.

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

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

The input schema has no parameters, so the baseline is 4. The description adds no parameter details, which is acceptable since none exist.

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

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

The description clearly states the tool returns IMF World Economic Outlook-style static macro composites JSON, specifying the resource (WEO-style composites) and action (returns). It distinguishes from live IMF API pulls and country desks, helping differentiate it from potentially similar tools.

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

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

The description provides context on when to use: when a static, curated snapshot of global GDP and inflation is needed for academic or reference purposes. It explicitly states it is not for live data, which guides agents away from using it for real-time queries, but no alternative tools are named.

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

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

No annotations are provided, so the description must carry the full burden. It states the data is 'canned' and 'static', hinting at non-real-time behavior, but does not disclose traits like idempotency, read-only nature, or performance implications. Minimal transparency beyond what is obvious from the name.

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

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

The description is a single sentence that is somewhat run-on, containing specific numbers and examples that could be structured more cleanly. It is not excessively long but lacks clear organization.

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

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

Given the complexity of two parameters with no descriptors, no output schema, and no annotations, the description is insufficient. It fails to explain parameter usage, output details, or how this benchmark differs from other similar tools, leaving the agent without critical 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%, and the description does not mention the two parameters (industry, company_size) at all. There is no guidance on acceptable values or how they affect the output, leaving the agent with no semantic information.

Input schemas describe structure but not intent. Descriptions should explain 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 median_total_spend_monthly and category_breakdown, indicating it provides industry spend benchmarks. It distinguishes itself by noting it's a static composite, not transactional GL data, and gives examples of productivity suite and CRM medians. However, the phrasing is somewhat messy with embedded numbers and examples.

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

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

No explicit guidance on when to use this tool versus similar siblings such as get_industry_spend_profile or get_category_spend_benchmark. The description only mentions it's not transactional GL data, which is a minor exclusion but lacks clear context for selection.

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

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

No annotations are provided, so the description must fully disclose behavioral traits. It mentions the data source (Stratalize tier-1 public composite) but does not specify data freshness, pagination, permissions, or potential errors. The description lacks explicit behavioral context beyond the return type.

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

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

Three sentences front-loaded with the purpose and key details. Each sentence adds value (purpose, example, context). Could be slightly more concise, but no waste.

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

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

No output schema exists, so the description must cover return values. It mentions 'industry spend bands, category ranges, and outlier flags' but does not detail format or structure. For a moderate-complexity tool, this provides enough to select the tool but lacks full completeness.

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

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

Schema coverage is 100% with parameter descriptions. The description adds meaning by linking employee_count to banding and providing context on industry verticals (e.g., healthcare vs manufacturing example). It adds value beyond the schema without repeating 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 returns JSON with industry spend bands, category ranges, and outlier flags, specifically for CFOs sizing SaaS and ops stacks. It distinguishes from siblings like get_industry_spend_benchmark by focusing on employee_count and providing examples (healthcare vs manufacturing).

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

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

The description says it's for CFOs sizing SaaS and ops stacks, but does not explicitly state when to use this tool versus alternatives like get_industry_spend_benchmark. No when-not-to-use or exclusions are provided, leaving the agent to infer usage context.

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

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

No annotations are provided, so the description must convey behavioral traits. It indicates the tool is live and read-only by nature, but it does not explicitly state side effects, authentication needs, or rate limits. The description is adequate for a simple data retrieval tool.

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

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

The description is a single, information-dense sentence that covers all key aspects without redundancy. Every part adds value.

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

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

For a tool with one optional parameter and no output schema, the description is comprehensive. It lists measures and mentions Fed target gap and policy implications. It does not describe output format, but this is acceptable given the tool's simplicity.

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

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

Even though schema description coverage is 0%, the description lists the available measures (CPI, PCE, breakeven, components), directly mapping to the enum values. This adds significant meaning beyond the schema, helping the agent understand what each option returns.

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

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

The description clearly states the tool provides live inflation benchmarks from FRED, listing specific measures like CPI, core CPI, PCE, and TIPS breakeven expectations. This distinguishes it from sibling tools focused on other benchmarks.

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

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

The description implies usage for inflation data but does not explicitly state when to use this tool versus alternatives like get_macro_market_signal or get_fomc_rate_probability. No when-not-to-use guidance is provided.

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

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

No annotations are provided, so the description must fully disclose behavioral traits. It mentions the data source (NAIC annual report) but does not state data freshness, read-only nature, or any constraints. The description leaves unknowns about output structure and behavior.

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

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

The description is concise (two sentences) and front-loaded with the key metrics. Every sentence adds value: what is returned and the target audience. No filler or redundancy.

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

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

Given no output schema, the description should clarify the output format. It lists metrics but does not specify if results are for a single line or multiple, or include company_size filtering. The lack of detail on output structure and omission of one parameter makes it 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?

With 0% schema description coverage, the description should explain parameter meanings. It mentions filtering 'by line of business' but does not describe the optional company_size parameter or the enum values. The description adds minimal value over the schema.

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

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

The description clearly states the tool provides insurance financial performance benchmarks with specific metrics (combined ratio, loss ratio, etc.) and the data source (NAIC). It targets a specific audience, differentiating it from many sibling benchmark tools by domain, though it does not explicitly contrast with siblings.

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

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

The description implies usage for reviewing underwriting performance and names the target audience, providing a basic use case. However, it does not specify when not to use the tool or mention alternative tools for related but different needs.

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

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

The description mentions 'citation heuristics and Snowflake style defaults when empty', which gives some insight into data handling behavior, but does not disclose authentication needs, rate limits, or potential side effects. With no annotations, more disclosure is needed.

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

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

The description is short but fragmented with three separate sentences that do not flow logically. It could be more concise and structured for clarity.

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

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

Given no output schema, the description mentions return fields (growth_signal, top_brands, evidence lines) but does not fully describe the structure or possible values. The mention of 'citation heuristics' and 'Snowflake style defaults' remains ambiguous without further context.

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

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

The input schema has one required parameter 'category' with 0% description coverage. The description does not explain what values 'category' accepts or how it is used, only a vague reference to 'VC software category'. This adds almost no 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 specifies the tool returns a JSON with growth_signal (stable or growth), top_brands, and evidence lines for growth investors, and mentions a 'VC software category heat check'. This provides a specific verb and resource, but it could be clearer about the exact boundaries compared to 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 does not provide guidance on when to use this tool versus other similar tools like get_category_disruption_signal or get_market_structure_signal. No when-to-use or when-not-to-use conditions are given.

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

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

No annotations are provided, so the description must carry the transparency burden. It mentions that data is 'live' from FRED and that it provides a 'tight/balanced/loosening signal'. However, it does not disclose update frequency, authentication requirements, or rate limits, which are important for an agent to select and invoke the tool correctly.

Agents need to know what a tool does to the 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 data points and followed by use case. Every word earns its place, with no redundancy or filler. It is highly concise for the information conveyed.

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

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

Given the tool has one optional parameter and no output schema, the description provides a strong overview of the tool's capabilities and purpose. However, it lacks details on the output format, time ranges, or granularity of data, which could be helpful for macro agents. Overall, it is mostly complete for a benchmark 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 optional 'focus' parameter with enum values, but schema description coverage is 0%. The description adds value by listing the specific data points available (unemployment, JOLTS, etc.), giving context to what 'focus' might filter. However, it does not explicitly map the enum values to the listed metrics, 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's purpose: retrieving live labor market benchmarks from FRED, listing specific metrics (unemployment, U-6, JOLTS, etc.) and its intended users (macro agents, portfolio managers). It distinguishes itself from the many sibling tools by focusing specifically on labor market data.

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

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

The description implies usage for labor market analysis and mentions the target audience (macro agents, portfolio managers). However, it does not explicitly state when not to use it or provide alternatives among siblings. The context is clear but lacks 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?

Given no annotations, the description reveals key behavioral traits: it depends on FRED_API_KEY, and returns empty fields if the key is not set. This provides useful context about failure modes and dependencies.

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

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

The description is compact, using two sentences and a fragment. It front-loads the main return type and key dependencies. It could be more structured but avoids verbosity.

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

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

With no output schema, no annotations, and a single undocumented parameter, the description leaves significant gaps: no example usage, no rate limit info, no comparison to similar siblings. It is insufficient for an agent to confidently select and invoke this tool.

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

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

The only parameter (signal_type) has zero schema description coverage, and the tool description does not explain its purpose, allowed values, or behavior. The description adds no meaning to this 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 specifies that the tool returns Fed funds, Treasury yields, CPI/PCE blocks, and employment hooks, with a live FRED feed dependency. However, it does not differentiate itself from many similar siblings like get_inflation_benchmark or get_yield_curve_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 mentions use by macro analysts and the need for FRED_API_KEY, but provides no explicit guidance on when to use this tool versus alternatives. There is no mention of when not to use it or comparison to 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, so the description does not need to reiterate safety. It adds context about the content but no additional behavioral traits (e.g., rate limits, auth). The description 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 with a front-loaded purpose and a follow-up example. No wasted words; every sentence adds value.

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

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

Given zero parameters and no output schema, the description adequately explains what the tool returns via the example and content list. It is complete enough for the agent to understand the output, though a brief note on format would be helpful.

Complex tools with many parameters or behaviors need more documentation. 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 schema description coverage is 100%. The description adds meaning by detailing what the playbook contains (regime, themes, triggers, opportunities), which goes beyond the empty schema.

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

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

The description clearly states the tool retrieves a 'current macro trading playbook' and specifies its contents: regime label, positioning themes, risk triggers, and tactical opportunities. The example further clarifies the output format. This differentiates it from sibling tools like get_macro_market_signal.

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

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

The description specifies target users ('For traders, portfolio managers, and macro allocators'), implying when the tool is appropriate. However, it does not explicitly state when not to use it or mention alternatives.

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

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

No annotations are present, so the description carries the full burden. It states the tool provides M&A multiples sourced from Damodaran and public aggregates, but does not disclose behavioral details like data recency, update frequency, or aggregation methods. The description is accurate but lacks depth beyond basic purpose.

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

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

The description is two sentences that concisely state the data provided, source, and users. It is efficient with no redundant information. However, it could be slightly improved by structuring the data types more clearly, but it remains effective.

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

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

The tool is a data retrieval benchmark with 2 parameters and no output schema. The description covers the purpose and source but omits details about the output format, time period, or data frequency. For a tool in a large set of siblings, this is adequate but not comprehensive.

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

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

The input schema has 2 parameters (industry, deal_size_tier) with enums, and schema description coverage is 0%. The description does not explain the parameters or their values beyond what the enum names imply. For example, it does not clarify what 'deal_size_tier' ranges represent. The description adds no additional meaning over the schema.

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

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

The description clearly states the tool provides M&A transaction multiples (EV/EBITDA, EV/Revenue, control premiums) by industry and deal size. It names the data source (Damodaran) and target audience (corp dev, PE, M&A advisors). This distinguishes it from sibling tools like get_public_market_multiples which focus on public company multiples.

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

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

The description mentions typical users (corp dev, PE, CFOs) but does not provide explicit guidance on when to use this tool versus alternatives. It does not specify conditions or exclusions, leaving it to the agent to infer from context.

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