TrueFixR + AtlasCast

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

Annotations already declare readOnlyHint and openWorldHint, and the description adds meaningful behavioral context: data is free, refreshed several times a day, sourced from NOAA models, and covers a 0-to-16-day horizon. This goes beyond the 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?

Three short sentences, each adding distinct value: what the tool returns, the temporal scope, and the data source/freshness. The core purpose is front-loaded and there is no filler 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?

For a simple two-parameter read-only tool, the description covers the forecast scope, output content, freshness, and source. It does not describe exact return fields or risk-grade scales, but no output schema exists and the high-level output description is sufficient 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?

Schema description coverage is 100%, so the schema already documents state and county. The description mentions 'county' in the context of the forecast but adds no parameter-level detail beyond what the schema provides, matching the baseline for full schema coverage.

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

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

The description clearly states the resource (county-level AtlasCast forecast), the specific content (severe weather risk 0 to 16 days ahead, risk grades, property exposure), and explicitly distinguishes itself from past-storm tools with 'not storms that already happened.' This is specific enough to differentiate from siblings without opening their schemas.

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 gives clear context: use this for future severe weather risk at county level, not for storms that already occurred. It does not explicitly name alternative tools or provide when-not-to-use conditions beyond the past-storm exclusion, so it falls short of a 5.

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 openWorldHint=true, so the description's addition of 'FREE' and the data sources ('reported and radar-detected') adds some context. However, it doesn't disclose potential limitations (e.g., data freshness, preview constraints) or pagination behavior. The description does not contradict annotations, and the extra hints are modest but not negligible.

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, tightly worded sentence that leads with the key selling point ('FREE preview') and immediately specifies the resource and output. There is no fluff or repetition; every word 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 five parameters and no output schema, the description adequately conveys the tool's purpose and the kind of results returned. However, it lacks guidance on usage context (when to use vs. siblings) and does not explain what 'repeat-hit properties' mean or any limits of the preview, leaving some gaps for an 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?

With 100% schema description coverage, all five parameters are already documented in the schema. The tool description itself adds no parameter-specific meaning beyond what the schema provides, so it meets the baseline but does not exceed 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 identifies the tool's purpose with a specific verb ('preview') and resource ('past storm events' for a county), and lists the specific output categories (event counts, storm types, severities, repeat-hit properties). This distinguishes it from siblings like get_hazard_forecast (future) and lookup_address_hazard (address-based), making it unambiguous.

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

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

The description implies this tool is for retrieving past storm event data but provides no explicit guidance on when to choose it over siblings or when not to use it. No alternatives or exclusion conditions are mentioned, leaving usage context to inference.

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

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

The annotations already declare readOnlyHint=true and openWorldHint=true, covering the read-only and open-world nature. The description adds a useful behavioral detail: 'past storm history (up to 365 days)' – a temporal limit. It doesn't disclose other behaviors like rate limits, pagination, or response format, but given the annotations, this is acceptable. No contradiction with annotations.

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

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

The description is a single, efficient sentence that front-loads the core purpose and then provides the input specification. No wasted words; every phrase earns its place. It's concise and immediately informative.

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

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

For a tool with 5 optional parameters, no required fields, and no output schema, the description provides enough context for an agent to call it correctly. It states what it returns (storm history and forecast risk), the time horizon, and how to specify the target. It doesn't describe the output format, but with no output schema, that's not expected. It's complete for its complexity.

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

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

The schema descriptions cover each parameter individually (100% coverage), but the description adds crucial combinatorial meaning: 'address with county+state, or lat/lon.' This clarifies that address, county, and state must be provided together, or lat/lon as an alternative. The schema alone doesn't specify these groupings, so the description adds significant value beyond the structured data.

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 comprehensive data for a single property (past storm history up to 365 days plus forecast risk). It uses a specific verb ('lookup') and resource ('address hazard'), and the content (history + risk) distinguishes it from sibling tools like get_hazard_forecast (likely forecast-only) and get_storm_preview (likely a preview). However, it doesn't explicitly name siblings or contrast them, so it's not a perfect 5.

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 input instructions: 'Give an address with county+state, or lat/lon.' This tells the agent how to supply parameters, which is clear usage guidance. It also implies this tool is for single-property lookups, differentiating it from batch or alternative tools. It doesn't explicitly say 'use this instead of X when...', but the context is strong enough.

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

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

Beyond the read-only annotation, the description discloses important behavior: no-argument calls return an API menu, addresses=false provides a free preview with counts/severities/dollar exposure, and addresses=true requires either an API key or an x402 payment payload. It also explains the HTTP 402 path and how an agent can construct and retry with payment instructions.

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

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

The description is dense but well organized: it front-loads the core purpose argument-free menu behavior, then maps datasets and address modes efficiently. Every sentence carries information necessary for an agent to decide how to call the tool, with no filler or repetition of schema 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 complex 14-parameter tool with no output schema, the description covers invocation, dataset selection, authentication/payment behavior, and the error/retry path. It is slightly incomplete in not describing parameter dependencies per dataset or distinguishing when to use sibling tools, but the universal query and menu approach compensate.

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 adds meaningful context beyond the schema by explaining dataset meanings and the free-preview/address-level distinction, including the x402 alternative to an API key. It does not deeply describe every parameter, but the schema already covers syntax and defaults.

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

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

Description states a specific verb ('query') and resource ('TrueFixR and AtlasCast weather data') and enumerates eight concrete dataset variants. It differentiates itself from specialized siblings by framing itself as a universal query entry point, and the no-arguments menu behavior makes its scope immediately actionable.

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

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

The description gives clear context for invocation: call with no arguments for the API menu, choose a dataset, and understand the free preview versus address-level distinction. However, it never mentions the sibling tools or states when a user should prefer get_hazard_forecast, get_storm_preview, or lookup_address_hazard.

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