Stratalize Real Estate Intelligence

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 adds significant behavioral context beyond annotations: it details the six perils, data sources (NOAA, FEMA, USGS), and an example output. This complements the readOnlyHint and destructiveHint annotations without 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 concise, front-loaded with purpose, and efficiently includes use cases, peril details, data sources, and an illustrative example. Every sentence adds value without redundancy.

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

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

Given the tool's complexity (composite risk across multiple perils), the description fully explains its output, data sources, and typical use. No output schema exists, but the description provides sufficient context for correct invocation.

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

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

The single parameter 'location' is described in the schema as 'US city and state', with 100% coverage. The description includes an example ('Miami-Dade FL'), which adds marginal clarity but does not substantially increase 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 explicitly states the tool's purpose: pricing physical climate risk for a location, with specific use cases (acquisition, underwriting, site selection, disclosure) and details about the composite risk score across six perils. This clearly distinguishes it from sibling tools, which cover diverse benchmarks and intelligence.

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

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

The description starts with 'Use when pricing physical climate risk for a location', providing clear usage context. While it doesn't explicitly state when not to use it or list alternatives, the use cases are specific and the tool's scope is well-defined.

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

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

No annotations 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 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?

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?

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?

No annotations are provided, so the description carries the burden. It discloses that rates update weekly, which is useful. However, it does not mention other behavioral traits like response format, data freshness guarantees, or any limitations (e.g., only US-specific data implied by state parameter).

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

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

The description is a single concise sentence that front-loads the key purpose. It lists multiple data points efficiently. Slight improvement could be structuring as bullet points or separating audience from content.

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

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

Given two optional parameters, no output schema, and no annotations, the description lacks completeness. It does not explain default behavior when parameters are omitted, nor describe the response structure. It covers content but not operational context.

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

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

The schema has two parameters (state, loan_type) with 0% description coverage. The description does not explain what state or loan_type does, nor does it provide context for the enum values. It lists data elements but fails to link them to parameters.

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

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

The description clearly states it provides live mortgage rate benchmarks including specific metrics (30Y/15Y fixed, ARM spreads, etc.) and identifies target users. This distinguishes it from sibling tools like get_commodity_benchmark or get_credit_spread_benchmark.

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

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

The description mentions the audience but does not provide explicit guidance on when to use this tool versus alternatives. No 'when not to use' or comparison with other benchmark tools on the same server.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the data content but does not disclose operational aspects such as data freshness (only 'quarterly public data' hints at update frequency), pagination, rate limits, error handling, or whether the output is a single value or time series. The name 'get' implies read-only, but this is not explicitly stated.

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

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

The description consists of three concise sentences, each adding distinct value: the core data, its significance, and the target audience. No redundant or filler content. Front-loaded with the primary purpose.

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

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

Given no output schema, the description should clarify the return format. It mentions three return types but does not specify whether the output is a single aggregate, multiple values, or a structured table. The period parameter includes values like '1y' and '3y', but the description only mentions 'quarterly public data' without explaining the relationship (e.g., trailing returns). The tool has 3 enum parameters with no required fields, but the description does not address behavior for omitted parameters.

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

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

Schema description coverage is 0%, but parameters are self-explanatory with enums for period, region, and property_type. The description mentions 'by property type and region' linking to two parameters, and period is implied by 'quarterly' but not explicitly mapped. The description adds value by listing return types (total, income, appreciation) but does not explain parameter syntax or defaults beyond what the enum names provide.

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

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

The description clearly states the tool returns NCREIF Property Index institutional return benchmarks with specific breakdowns by property type and region (total, income, appreciation returns). It explicitly names the data source and target audience, distinguishing it from sibling benchmarks like get_reit_benchmark or get_property_operating_benchmark.

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

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

The description implies usage for institutional real estate portfolio benchmarking but does not provide explicit guidance on when to use this tool versus alternatives, nor does it state when not to use it. The mention of 'standard benchmark for institutional real estate portfolios' gives some context but no comparative direction.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds that the tool returns annual data with historical context and trend acceleration. However, it omits details on rate limits, data freshness, or pagination, which are beyond annotations but would add value.

Agents need to know what a tool does to the 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 verbose with multiple sentences and includes credibility statements (e.g., 'Cited by the Federal Reserve'). It is front-loaded with usage context but could be more concise without losing meaning.

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

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

Given no output schema, the description explains the output includes event count, total losses, deaths, and historical trend, with a concrete example for 2023. For a tool with one optional parameter, this is mostly complete, though it could clarify the exact output structure.

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

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

With 0% schema description coverage, the description partially compensates by mentioning year in the example (2023). It implies the parameter controls which year's data, but does not explicitly state the format or valid range. Baseline of 3 is appropriate given the single required parameter is optional with a default.

Input schemas describe structure but not intent. Descriptions should explain 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 NOAA's annual billion-dollar disaster economics data, including event count, total losses, deaths, and historical trend acceleration. This specific verb-resource pairing distinguishes it from sibling tools like get_storm_event_history or get_climate_risk_score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb 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 enumerates use cases: ESG reporting, reinsurance negotiations, infrastructure decisions, and SEC/TCFD disclosures. It implies when to use but does not explicitly name alternatives or state when not to use, leaving room for improvement.

Agents often have multiple tools that could apply. Explicit usage 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 behavioral transparency burden. It lists the metrics and sources, which is helpful, but it does not disclose data freshness, geographic scope, update frequency, or whether results are current or historical. The description is adequate but lacks depth for a comprehensive behavioral profile.

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

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

The description is succinct: two sentences plus a target audience tagline. It front-loads the key metrics, then provides sources and audience. Every sentence adds value, no redundancy.

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

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

With no output schema, the description should fully describe the return structure. It lists the metrics but does not clarify whether results are single values or time series, how market_tier affects output, or the level of aggregation. Given the complex ecosystem of siblings, the description is adequate but missing details on output format and filtering behavior.

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

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

The schema has 0% description coverage for parameters. The description mentions benchmarks 'by property type' but does not explain the market_tier parameter or the meaning of enum values. The property_type enum is self-explanatory, but market_tier remains ambiguous, requiring the agent to infer its purpose.

Input schemas describe structure but not intent. Descriptions should explain 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 property operating benchmarks (OpEx per SF, NOI margins, occupancy rates) by property type, cites specific authoritative sources, and identifies target users. It effectively distinguishes from sibling tools like get_cap_rate_benchmark or get_ncreif_return_benchmark by focusing on operating metrics.

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

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

The description implies usage for asset managers, property managers, and acquisition underwriters, but does not explicitly state when to use this tool versus alternatives among many similar benchmark tools. No direct comparison or exclusion criteria are provided.

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

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

No annotations are provided, so the description must carry the full burden. It discloses the data source (Lincoln Institute of Land Policy) and the types of data included (rates, ratios, appeal success rates). However, it does not specify data freshness, update frequency, or return format. The behavioral traits are partially transparent but have gaps.

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

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

The description is three sentences, front-loaded with the key function, and every sentence adds value: purpose, source, target audience, and importance. There is no redundancy or filler, making it 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 the tool has no output schema and no annotations, the description provides moderate context: data source, what is included, and target users. However, it lacks details on data scope (e.g., all states?), aggregation method, or typical output structure, which would be helpful for a retrieval tool with no output schema.

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

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

Schema description coverage is 50% (state has a description, property_type does not). The description mentions 'by state and property type' which reinforces the parameters but adds no deeper meaning beyond the schema. For property_type, the enum values are listed in the schema; the description does not clarify their implications or how they affect 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 specifies the tool returns 'Property tax benchmarks — effective tax rates by state and property type, assessment ratios, and appeal success rates.' It names the verb (get) and resource (property tax benchmarks), and the scope (state and property type) distinguishes it from other benchmark tools in 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 states the target audience ('For property owners, asset managers, and acquisition teams') and provides a motivational reason ('Property tax is the largest controllable operating expense...'), but it does not explicitly say when to use this tool versus alternatives or when not to use it. The guidance is implied rather than explicit.

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

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

The description notes that the delinquency rate updates quarterly, which is a useful behavioral trait. However, it does not disclose other aspects such as response format, data source freshness for other metrics, or any side effects. Since no annotations are provided, more disclosure would be expected.

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

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

The description is concise, with two sentences covering the main data outputs and intended audience. It front-loads the key information. However, it could be more structured (e.g., bullet points) for easier scanning.

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

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

The description provides a high-level overview of what the tool returns but lacks detail on output structure, how parameters affect results, and how it fits among many sibling tools. Given the complexity and lack of output schema, it is minimally viable 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 parameters (scenario and property_type) with enums, but the description does not explicitly list or explain them. It hints at parameter usage by mentioning 'stressed cap rate scenarios' and 'CMBS delinquency by property type', but this is insufficient given 0% schema description coverage. The description should clearly link parameters to the listed data points.

Input schemas describe structure but not intent. Descriptions should explain 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 CRE debt stress benchmarks including delinquency rates, CMBS delinquency by property type, maturity wall exposure, and stressed cap rate scenarios. It also identifies target users. However, it does not explicitly differentiate from the sibling tool 'get_cre_debt_benchmark', which may have overlapping functionality.

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

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

The description lacks guidance on when to use this tool versus alternatives. It lists target audience but does not specify context, prerequisites, or situations where this tool should be preferred over siblings like 'get_cre_debt_benchmark' or 'get_mortgage_market_benchmark'.

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

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

Without annotations, the description discloses data source (NAREIT public monthly data) and that it's free, but does not mention read-only nature, rate limits, or response format.

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

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

Three concise sentences covering purpose, metrics, source, audience, and cost with no unnecessary words.

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

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

Lists output metrics but lacks details on output structure or format, which is needed given no output schema; 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?

Schema has one parameter with enum but no description; the description mentions 'by property sector' but does not explain that the parameter filters results or describe the effect of each enum value.

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

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

The description clearly states it provides REIT valuation and performance benchmarks with specific metrics like FFO multiples, AFFO multiples, etc., distinguishing it from other real estate 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?

Identifies target users (REIT analysts, portfolio managers, IR teams) but lacks explicit when-to-use vs alternatives or when-not-to-use guidance.

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

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

No annotations are provided, so the description carries full burden. It transparently lists the data sources (HUD, FRED, ApartmentList) and the types of data returned (asking rents, vacancy, rent growth, rent-to-income ratios), making it clear this is a read-only data retrieval tool. It does not mention any side effects or limitations, but the absence of annotations is compensated by the clear, non-destructive portrayal.

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

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

The description is only two sentences and directly conveys core information: what the tool returns, data sources, and target audience. It is front-loaded with the most critical information and contains no extraneous words. Every sentence serves a purpose.

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

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

Given that there is no output schema, the description should indicate the structure of the returned data. It lists data points but does not describe the format (e.g., whether results are per unit type or aggregated, time periods, etc.). It also does not specify geographic scope (national vs local) beyond what the market_tier parameter implies. For a tool with two enum parameters and no output schema, this leaves some ambiguity.

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

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

Schema description coverage is 0%, so the description must add meaning. It mentions 'by unit type' and 'by market tier', which map to the two enum parameters, but it does not explain what each enum value means (e.g., what constitutes 'gateway' vs 'secondary' market tier). The description omits details about how to select or interpret parameter values, leaving the agent to rely solely on the enum labels.

Input schemas describe structure but not intent. Descriptions should explain 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 rental market benchmarks, listing specific data points (asking rents, vacancy rate, rent growth, rent-to-income ratios) and sources (HUD, FRED, ApartmentList). It distinguishes itself from many sibling benchmark tools by its focus on rental metrics, though it does not explicitly contrast with closely related siblings like get_housing_supply_benchmark or get_residential_market_benchmark.

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

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

The description identifies the target audience (landlords, investors, property managers), which implies usage context. However, it provides no explicit guidance on when to use this tool versus alternatives, nor does it mention when not to use it. Given the large set of sibling benchmark tools, this lack of comparative guidance is a gap.

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

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

No annotations are provided, and the description does not disclose any behavioral traits such as read-only status, rate limits, authentication needs, or side effects. Listing data sources (FHFA, FRED, Census) adds credibility but not transparency about behavior.

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

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

The description is concise with two sentences. The first sentence lists metrics, and the second adds sources and audience. It front-loads key information, but the first sentence could be slightly more structured.

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

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

Given no output schema and multiple sibling tools, the description is minimal. It does not explain the output format, how metrics are calculated, or how parameters affect results. More context is needed for an agent to use this tool effectively.

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

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

The input schema has two parameters (market_tier, property_type) with enums, but schema description coverage is 0%. The description mentions 'by market tier' but does not explain the parameter values or mention property_type at all, failing to compensate for the lack of schema documentation.

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

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

The description clearly states the tool provides residential real estate market benchmarks including specific metrics (home price indices, price-to-rent ratios, etc.) and data sources. While it does not explicitly differentiate from siblings like get_housing_supply_benchmark, the specific metrics and sources make the purpose 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 (investors, agents, developers, analysts) but provides no guidance on when to use this tool versus alternatives like get_mortgage_market_benchmark or get_rental_market_benchmark. No explicit when-not 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?

Annotations indicate readOnlyHint=true and destructiveHint=false. The description adds valuable context: the data source (NOAA), authoritative recognition (reinsurers, Federal Reserve, SEC), and the output structure (event frequency, losses, deaths, trend). No contradictions with annotations.

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

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

The description is concise and front-loaded with use cases. Every sentence adds value: use cases, what it returns, an example, and the source. 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 only 2 simple parameters and no output schema, the description sufficiently explains the output metrics and provides an illustrative example. It covers the key information an agent needs 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?

Schema coverage is 50% (state described, years_back not). The description adds the example 'Texas 10-year history' and implies default behavior, but does not provide explicit format or constraints for years_back beyond the schema default. Baseline adjusted for low coverage, but description provides some 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 the tool returns NOAA's tally of billion-dollar weather disasters with specific metrics (frequency, losses, deaths, trend). It specifies the resource and action precisely, and the context of use cases distinguishes it from general sibling tools like get_noaa_disaster_economics.

Agents choose between tools based on descriptions. A clear purpose with a specific verb 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 first sentence explicitly lists specific use cases (insurance underwriting, real estate due diligence, ESG disclosures, board briefings). While it doesn't explicitly say when not to use or name alternatives, the context is well-defined and implies appropriate scenarios.

Agents often have multiple tools that could apply. Explicit usage 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, but description covers the key behavior: returns a catalog with counts and role-based recommendations. Does not explicitly state read-only, but 'Returns' implies no mutation.

Agents need to know what a tool does to the 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 complete information: purpose, content, usage instruction, and auth status. Front-loaded with 'START HERE' to immediately signal priority. 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, the description adequately describes return content (catalog with counts and recommendations). Could be slightly more specific about what 'role-based recommendations' entails, but sufficient for a discovery tool.

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

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

With zero parameters in the schema, the description adds context by stating 'No auth required', effectively covering input requirements. No further parameter info needed.

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

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

Starts with 'START HERE' and clearly states verb 'Returns' and resource 'complete Stratalize tool catalog'. Uniquely identified as the tool to call first, distinguishing it from all 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 instructs to call this first to discover all available tools, implying the right order of use. 'No auth required' provides immediate clarity on 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?

Annotations already indicate readOnlyHint: true and destructiveHint: false. The description adds that it uses official NOAA data and specifies thresholds (precipitation probability, wind >25 mph, freeze <32°F). It also states the output includes risk tier and high-risk days. This adds context beyond annotations without contradicting them.

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

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

The description is concise: three sentences plus an example and source citation. It is front-loaded with use cases and purpose, then explains analysis, provides a concrete example, and cites the data source. No wasted words.

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

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

Despite lacking an output schema, the description fully covers the return value ('risk tier and specific high-risk days') and provides an illustrative example. It addresses the simple input format, data source, and typical use case, making the tool's behavior complete for an AI agent.

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

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

Schema coverage is 100% with a description for 'location' (US city/state or address). The description reinforces this with examples ('Chicago IL', '1600 Pennsylvania Ave') but does not add significant new meaning beyond the schema baseline. Hence a score of 3 is appropriate.

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

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

The description explicitly states the tool analyzes NOAA 7-day forecast data against construction delay thresholds (precipitation, high wind, freeze) and returns a risk tier with high-risk days. It distinguishes itself from siblings by being specific to US outdoor construction weather risk, using clear verb 'Analyzes...returning' and resource 'NOAA 7-day forecast data'.

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

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

The description provides explicit use cases: 'when scheduling outdoor construction work, planning equipment deployment, or assessing weather risk for any US project site' and includes an example with actions like 'Reschedule concrete pours and crane operations.' It does not explicitly state when not to use or compare to alternatives, but the context makes usage clear.

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