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

No annotations are provided, so the description carries full burden. It describes output (probabilistic forecasts) but does not mention any behavioral traits such as idempotency, auth requirements, rate limits, data source freshness, or whether it is a read-only operation. 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?

Two sentences with no redundant information. Front-loaded with the main purpose and key outputs.

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

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

Given the absence of an output schema, the description lists important return fields. With 2 parameters and 10 siblings, the description is complete enough for the complexity, though explicit guidance on choosing among siblings would improve it.

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

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

Schema description coverage is 100%, and the description adds an example address ('5000 Westheimer Rd Houston TX 77056') which aids understanding. However, it does not add significant meaning beyond the schema's own descriptions for 'address' and 'year'.

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

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

The description clearly states the verb 'Get' and resource 'probabilistic home value forecasts for a US street address'. It distinguishes from sibling tools by specifying address granularity. It also lists key return fields (P10/P50/P90 bands, appreciation %, reliability score, fan chart).

Agents choose between tools based on descriptions. A clear purpose with a specific verb 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 'Best coverage: Houston metro area' which implies a usage hint but does not explicitly state when to use this tool versus alternatives (e.g., by county, state). No guidance on prerequisites or when not to use.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It specifies that forecasts are 'probabilistic' and 'nationwide,' which are important traits. However, it does not mention whether the tool is read-only, any authentication requirements, or rate limits. For a simple get operation, the risk is low, but the description could be more 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 consists of two concise sentences with no unnecessary words. The first sentence provides the core purpose, and the second adds scope and usage context. It is front-loaded with the essential information.

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

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

Given the tool's simplicity (single parameter, no output schema, no annotations), the description adequately covers purpose, identifier, and use case. It lacks explicit details about the return format or the probabilistic nature (e.g., what metrics are provided), but for a straightforward forecast retrieval, the missing information is minor.

Complex tools with many parameters or behaviors need more documentation. 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%—the single parameter 'fips' is thoroughly documented with examples in the input schema. The description merely echoes '5-digit FIPS' without adding new semantics. The statement 'county-level' and 'FIPS' already align with the schema, so the description adds no extra parameter understanding 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 action ('Get'), the resource ('county-level probabilistic forecasts'), the identifier ('by 5-digit FIPS'), and the scope ('Nationwide coverage'). It also provides a use case ('market-level analysis'), which differentiates it from sibling tools that target other geographic levels.

Agents choose between tools based on descriptions. A clear purpose with a specific verb 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 this tool (when needing county-level forecasts with a FIPS code) and provides context for application ('market-level analysis'). While it does not explicitly state when not to use it, the sibling tool names naturally guide the agent away from this tool for address, tract, or block-level queries.

Agents often have multiple tools that could apply. Explicit usage 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 states the geographic limitation (Houston metro) and resolution (8), but does not mention data freshness, rate limits, error handling, or whether the operation is read-only. The description partially compensates but leaves 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 with no extraneous information. It front-loads the purpose and quickly adds geographic and resolution context, then provides usage guidance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. 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 siblings, the description covers key aspects: purpose, scope, and alternatives. However, it does not describe the forecast fields returned, which would improve completeness.

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

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

Schema coverage is 100%, so the baseline is 3. The description mentions 'neighborhood-level' and 'resolution 8', reinforcing the schema descriptions but not adding significant new semantic meaning beyond what the schema provides.

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

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

The description clearly states the tool retrieves neighborhood-level forecasts using H3 hex cell IDs at resolution 8, and specifies the geographic scope (Houston metro). This distinguishes it from sibling tools that use other geographic identifiers.

Agents choose between tools based on descriptions. A clear purpose with a specific verb 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 recommends preferring forecast_by_tabblock or forecast_by_tract for new integrations, providing clear guidance on when not to use this tool.

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

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

No annotations provided, so description carries full burden. It discloses probabilistic output format, horizon range, and geographic coverage. However, it does not mention rate limits, authentication, or error handling (e.g., invalid parcel numbers).

Agents need to know what a tool does to the 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, followed by coverage and output details. No redundancy or unnecessary words.

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

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

Tool is simple (one required parameter) with no output schema, but description fully explains output shape (probability distribution over horizons) and coverage. An agent has sufficient context to use it 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 100% with detailed format explanation in the property 'acct'. The main description does not add new parameter information beyond what the schema already provides, so baseline score of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain 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 retrieves lot-level probabilistic forecasts by parcel ID, specifies coverage (Florida statewide + Houston metro), and output details (P10-P90 fan chart over 1-5 year horizons). This distinguishes it from sibling tools that forecast by address, county, etc.

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

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

Description implies use when parcel account number is available but lacks explicit guidance on when not to use or comparisons with sibling tools. Coverage constraints are noted but no decision tree for selecting among forecast_by_* 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 indicates a read operation ('Get') with probabilistic output, but lacks details on rate limits, authorization, or output structure.

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

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

Two sentences with no wasted words. The first sentence front-loads the core action and resource, and the second provides a use case. Highly efficient.

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

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

Given the tool's simplicity (single parameter, no output schema), the description sufficiently covers purpose and usage. However, it does not describe the return format or confirm it's a read-only operation, which could be inferred but is not explicit.

Complex tools with many parameters or behaviors need more documentation. 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% and already describes the fips parameter with examples. The description adds value by clarifying the scope ('All 50 US states') and providing specific FIPS-to-state mappings, which helps the agent use the correct codes.

Input schemas describe structure but not intent. Descriptions should explain 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 state-level probabilistic forecasts using a 2-digit FIPS code, and specifies that it covers all 50 states. This differentiates it from sibling tools that operate at other geographic levels.

Agents choose between tools based on descriptions. A clear purpose with a specific verb 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 a clear usage context ('Use for macro market selection') but does not explicitly state when not to use this tool or mention alternatives for finer granularity.

Agents often have multiple tools that could apply. Explicit usage 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 'probabilistic forecasts' and coverage areas (NYC, Houston), but does not disclose rate limits, data freshness, or the nature of probabilistic output.

Agents need to know what a tool does to the 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 succinct sentences, front-loaded with the main purpose, and each sentence adds vital information without redundancy.

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

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

The input is well-covered, but without an output schema, the description omits what the forecast looks like (e.g., data format, fields). This is a notable gap for a tool returning structured results.

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

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

Schema coverage is 100%, with a detailed description of the geoid format. The tool description adds value by providing coverage context (NYC, Houston) and emphasizing the 15-digit format, enhancing what the schema provides.

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

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

The description clearly states it gets probabilistic forecasts by census tabulation block, specifies the identifier format (GEOID20), and notes it offers the highest geographic resolution, distinguishing it from coarser 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 fine-grained sub-neighborhood forecasts by stating 'highest geographic resolution,' but does not explicitly guide when to use this tool versus alternatives like forecast_by_tract or forecast_by_county.

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

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

No annotations provided, so the description carries full burden. It mentions 'probabilistic forecasts' but does not disclose output structure, mutability, rate limits, or error behavior. This is a significant gap for a tool returning forecasts.

Agents need to know what a tool does to the 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 states purpose and identifier format, second covers coverage and use case. No redundant words, front-loaded with essential information.

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

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

Adequate for a simple tool with one parameter and clear coverage info, but lacks description of output format or any error handling. Without an output schema, the description should at least hint at return structure (e.g., probability values, time horizon).

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

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

Schema coverage is 100% with a single 'geoid' parameter. The description adds value by explaining the digit breakdown (state 2 + county 3 + tract 6) and providing concrete examples (Harris County, Manhattan), which goes beyond the schema's minimal description.

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

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

Clearly states 'Get neighborhood-level probabilistic forecasts by US Census tract GEOID20 (11 digits)', identifying verb (get), resource (forecasts), and specific identifier. Distinguishes from siblings by specifying coverage models and useful for cross-market analysis.

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

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

Explains that coverage differs by model (Florida+Houston vs all US tracts), implying when to use this tool over others. However, it lacks explicit when-not-to-use guidance or direct alternatives, though sibling tools are listed.

Agents often have multiple tools that could apply. Explicit usage 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 implies a read operation ('Get unit-level forecasts') but does not explicitly state safety traits like read-only nature, rate limits, or side effects. The description is adequate but lacks explicit behavioral guarantees beyond the verb 'Get'.

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

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

The description is extremely concise: three short sentences that front-load the main purpose, then provide a key option and coverage. Every sentence earns its place with no redundancy or irrelevant 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 tool with two parameters, no output schema, and no annotations, the description covers purpose, parameters, and geographic scope. It does not describe return format or error handling, but given the simplicity of a forecast retrieval tool, the description is reasonably complete. Annotations would further enhance completeness.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by providing concrete examples for the 'acct' parameter (FL DOR and Houston HCAD formats) and clarifying that 'include_siblings' retrieves units in the same condo building, which is more intuitive than the schema's 'same condo_group'. This extra context improves parameter understanding.

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

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

The description clearly states that the tool gets unit-level forecasts by assessor account, specifying the finest resolution for individual condo units, SFRs, and townhouses. It distinguishes itself from sibling tools by emphasizing unit-level granularity, which is unique among the geographic levels listed.

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

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

The description provides clear usage context: it explains how to use include_siblings to get all units in a building and notes coverage is limited to Florida and Houston. However, it does not explicitly state when to avoid this tool in favor of alternatives, though the finest resolution hint serves as implicit guidance.

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

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

With no annotations provided, the description fully discloses behavioral traits: probabilistic forecasts, coverage of ~20,000 ZCTAs, ACS model, and 6-year horizon (0-72 months). This goes beyond a simple read operation and informs the agent about model and horizon.

Agents need to know what a tool does to the 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 no waste. It frontloads the core purpose, then adds relevant details on coverage and use case. Every sentence adds value.

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

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

Given the tool has only one simple parameter and no output schema, the description provides sufficient context: what it returns (probabilistic forecasts), coverage, model, and horizon. It is complete for the agent to understand the tool's capabilities.

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

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

Schema coverage is 100% and already describes the zcta parameter with examples. The description does not add additional meaning beyond what the schema provides, so baseline score of 3 is appropriate.

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

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

The description clearly states the tool gets ZIP-level probabilistic forecasts by ZCTA, with specific verb 'Get' and resource 'ZIP-level probabilistic forecasts'. It distinguishes from sibling tools (forecast_by_address, forecast_by_county, etc.) by specifying the ZCTA geographic level and use case for cross-market comparisons.

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

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

The description explicitly says 'Use this for quick cross-market ZIP-level comparisons', providing clear context for when to use it. It does not explicitly state when not to use it or mention alternatives, but the sibling tools imply other geographic levels for 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?

No annotations provided, so description carries full burden; it only states the tool 'gets' forecasts without disclosing any behavioral traits like error handling, idempotency, or auth requirements.

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

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

Three short sentences that are front-loaded with the purpose and efficiently convey scope, scale, and positioning.

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

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

For a single-parameter tool with no output schema and no annotations, the description adequately explains the geographic level and provides examples, but lacks details on forecast type or output format.

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

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

Schema description coverage is 100%, so baseline is 3. Description adds example zip3 values (Houston=770, etc.) which aid understanding but do not significantly expand semantics beyond the schema.

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

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

Description clearly states the tool gets forecasts at the ZIP3 level, defines it as first 3 ZIP digits, and distinguishes it from siblings by positioning it between ZCTA and county.

Agents choose between tools based on descriptions. A clear purpose with a specific verb 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 when to use it based on geographic granularity but gives no explicit when-to-use or when-not-to-use guidance nor mentions alternatives.

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