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 calculation function but lacks details on permissions, rate limits, error handling, or output format. For a tool with 5 required parameters and no output schema, this is a significant gap in transparency.

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

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

The description is a single, efficient sentence that front-loads the core purpose without unnecessary details. Every word contributes to explaining the tool's function, making it appropriately sized and well-structured for quick understanding.

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

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

Given the complexity of a 5-parameter tool with no annotations and no output schema, the description is incomplete. It fails to explain return values, error conditions, or behavioral traits, leaving the agent with insufficient information to effectively invoke the tool beyond basic parameter input.

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 100% description coverage, clearly documenting all 5 parameters. The description adds minimal value beyond the schema by listing provider, service type, and region as key inputs, but it does not provide additional context like valid examples or constraints, so it meets the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose with a specific verb ('calculate') and resource ('emissions from cloud computing services'), and it mentions key parameters (provider, service type, region) to assess digital carbon footprint. However, it does not explicitly differentiate from sibling tools like 'custom-emission-calculation' or 'search-emission-factors', which might handle similar emission calculations.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions assessing digital carbon footprint but does not specify scenarios, prerequisites, or exclusions compared to sibling tools such as 'electricity-emission' or 'procurement-emission', leaving the agent without clear usage context.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it states the tool calculates emissions, it doesn't describe what the calculation returns (units, format), whether it requires authentication (though set-api-key is a sibling), rate limits, error conditions, or how it handles invalid inputs. For a calculation tool with zero annotation coverage, this leaves significant behavioral 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 a single, efficient sentence that conveys the core purpose without unnecessary words. It's front-loaded with the main action ('Calculate emissions') and includes a qualifying phrase about flexibility. However, it could be slightly more structured by explicitly separating purpose from context.

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

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

Given no annotations, no output schema, and a calculation tool with potential complexity, the description is incomplete. It doesn't explain what the tool returns (e.g., emissions in CO2e, units), prerequisites (e.g., needing an API key via set-api-key), or error handling. The schema covers inputs well, but the overall context for agent usage is lacking.

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

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

Schema description coverage is 100%, with all three parameters well-documented in the schema. The description adds minimal value beyond the schema, mentioning 'activity_id' and 'carbon calculations' but not explaining parameter relationships or providing additional context like example values or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose: 'Calculate emissions using any specific emission factor identified by its activity_id'. It specifies the verb ('calculate'), resource ('emissions'), and mechanism ('using emission factor identified by activity_id'). However, it doesn't explicitly distinguish this general-purpose calculation tool from its more specific sibling tools like 'electricity-emission' or 'travel-emission'.

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 through 'allowing for precise and flexible carbon calculations' and references 'activity_id (found via search-emission-factors)' in the schema. This suggests this tool is for custom calculations after finding factors via search-emission-factors, but it doesn't explicitly state when to use this vs. the specialized sibling tools (e.g., electricity-emission for electricity-specific calculations).

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 states the calculation action but lacks details on permissions, rate limits, error handling, or output format. For a tool with no annotation coverage, this leaves significant gaps in understanding its operational traits.

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 without unnecessary words. Every part of the sentence contributes directly to explaining what the tool does, making it highly 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 complexity of an emission calculation tool with no annotations and no output schema, the description is incomplete. It doesn't explain the return values, error conditions, or behavioral aspects like data sources or accuracy, which are crucial for proper usage in this context.

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

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

Schema description coverage is 100%, so the schema already documents all parameters (energy, energy_unit, region) with descriptions. The description adds marginal value by mentioning 'energy amount and regional grid mix', which aligns with the parameters but doesn't provide additional syntax or format details beyond what the schema offers.

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

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

The description clearly states the tool's purpose with a specific verb ('calculate') and resource ('carbon emissions from electricity consumption'), and it mentions the key inputs ('energy amount and regional grid mix'). However, it doesn't explicitly differentiate from sibling tools like 'custom-emission-calculation' or 'search-emission-factors', which might handle similar calculations differently.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose it over sibling tools such as 'custom-emission-calculation' for broader scenarios or 'search-emission-factors' for looking up data, nor does it specify prerequisites or exclusions for usage.

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 states what the tool does (calculates emissions) but doesn't describe behavioral traits like whether it's a read-only calculation, what units the output uses, error conditions, or any rate limits. For a calculation tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.

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 states the core purpose upfront. There's no wasted language or unnecessary elaboration. However, it could be slightly more structured by separating purpose from scope or adding a brief note about output.

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 calculation tool with 5 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the output represents (e.g., CO2 equivalent, units), whether it uses specific emission factors, or how the calculation is performed. The lack of output schema means the description should ideally cover return values, which it doesn't.

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 all 5 parameters thoroughly. The description mentions 'based on weight and distance' which aligns with the schema but adds no additional semantic meaning beyond what's in the parameter descriptions. It doesn't explain relationships between parameters or calculation methodology.

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

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

The description clearly states the tool's purpose: 'Calculate emissions from freight transportation' with specific resources (freight transportation) and modes (truck, rail, ship, air). It distinguishes from some siblings like 'electricity-emission' or 'hotel-emission' by focusing on freight, but doesn't explicitly differentiate from 'travel-emission' which might also involve transportation. The verb 'calculate' is specific and 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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'custom-emission-calculation' or 'search-emission-factors' that might be relevant for emission calculations. There's no context about prerequisites, limitations, or scenarios where this tool is preferred over others in the 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 states the calculation purpose but lacks details on what the calculation returns (units, format), whether it's an estimate or exact, data sources, accuracy limitations, or error handling. For a calculation tool with zero annotation coverage, this is a significant gap in behavioral context.

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

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

The description is a single, efficient sentence that front-loads the core purpose and key parameters without any wasted words. Every element earns its place, making it appropriately sized and well-structured for quick understanding.

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

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

Given the complexity of an emission calculation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., CO2 equivalent in kg), how results are derived, or any assumptions/limitations. For a tool that likely involves environmental data and calculations, this leaves significant gaps for an AI agent.

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

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

Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description adds minimal value by mentioning nights and location as key inputs, but doesn't provide additional syntax, format details, or constraints beyond what the schema provides. The baseline 3 is appropriate when the schema does the heavy lifting.

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 specific action ('calculate carbon emissions') and resource ('from hotel stays'), with explicit parameters ('based on the number of nights stayed and the location of the hotel'). It distinguishes from sibling tools like 'travel-emission' or 'electricity-emission' by focusing specifically on hotel stays.

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 through its parameter focus (nights and location), but does not explicitly state when to use this tool versus alternatives like 'travel-emission' or 'custom-emission-calculation'. No exclusions or prerequisites are mentioned, leaving usage guidance at an implied level.

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. While it explains the calculation method, it doesn't describe what the tool returns (emissions in what units?), whether it makes external API calls, error handling, or performance characteristics. For an emission calculation tool with zero annotation coverage, this leaves significant behavioral 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 a single, efficient sentence that communicates the core purpose without any wasted words. It's appropriately sized for the tool's complexity and gets straight to the point.

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

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

For a calculation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the tool returns (emissions values, units, confidence intervals), how results should be interpreted, or any limitations of the economic input-output method. Given the complexity of emission calculations, more context is needed.

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

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

The schema has 100% description coverage, so all parameters are documented in the schema itself. The description adds no additional parameter information beyond what's already in the schema descriptions. This meets the baseline of 3 when schema coverage is complete.

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 specific action ('Calculate'), the target ('Scope 3.1 emissions from procurement spending'), and the method ('using economic input-output life cycle assessment methods'). It distinguishes this tool from siblings like 'electricity-emission' or 'freight-emission' by focusing specifically on procurement emissions.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'custom-emission-calculation' or 'search-emission-factors'. It doesn't mention prerequisites, limitations, or typical use cases beyond the basic purpose.

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 describes the search functionality but lacks behavioral details: it doesn't mention whether this is a read-only operation, how results are returned (e.g., pagination, format), rate limits, authentication requirements, or error handling. For a search tool with 7 parameters and no annotation coverage, this leaves significant gaps in understanding how the tool behaves.

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. It avoids redundancy by not repeating schema details. However, it could be slightly more structured by separating the action from the goal (e.g., 'Search for emission factors... Use this to...'), but overall it's concise and well-focused.

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

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

Given the complexity (7 parameters, no annotations, no output schema), the description is adequate but incomplete. It covers the basic purpose and parameters but lacks behavioral transparency and clear usage differentiation from siblings. Without an output schema, it doesn't describe return values, which is a gap for a search tool. It meets minimum viability but has clear room for improvement.

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

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

Schema description coverage is 100%, with all 7 parameters well-documented in the schema. The description adds minimal value beyond the schema by listing the same parameters (keyword, category, region, year, source, other metadata) without providing additional context like examples beyond 'query' or explaining interactions between parameters. This meets the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose: 'Search Climatiq's database for emission factors by keyword, category, region, year, source, and other metadata to find appropriate factors for calculations.' It specifies the action (search), resource (emission factors database), and scope (by various metadata). However, it doesn't explicitly distinguish this general search tool from the more specific sibling tools like 'electricity-emission' or 'travel-emission' that appear to target specific domains.

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

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

The description implies usage context through 'to find appropriate factors for calculations,' suggesting this is for discovery before calculation. However, it provides no explicit guidance on when to use this tool versus the more specific sibling tools (e.g., 'electricity-emission' for electricity-related factors), nor does it mention prerequisites like API key setup (though 'set-api-key' exists as a sibling). The guidance is present but incomplete.

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 clearly indicates this is a configuration/mutation tool (setting an API key) and explains the authentication purpose. However, it doesn't address important behavioral aspects like whether this persists across sessions, if it overwrites existing keys, what happens on failure, or any rate limits/security implications.

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 focused sentences that directly address the tool's purpose and usage. Every word earns its place with zero redundancy or fluff. The information is front-loaded and efficiently structured.

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

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

For a single-parameter authentication tool with no annotations and no output schema, the description provides adequate basic context about what the tool does and why. However, it lacks details about behavioral consequences (persistence, error handling) and doesn't explain what 'success' looks like since there's no output schema to document return values.

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 description coverage is 100%, with the single parameter 'api_key' well-documented in the schema itself. The description doesn't add any additional parameter semantics beyond what the schema already provides (it mentions 'API key' but doesn't elaborate on format, validation, or source requirements). This meets the baseline for high schema coverage.

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

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

The description clearly states the specific action ('Set') and resource ('Climatiq API key for authentication'), with the explicit purpose of enabling authorized API requests. It distinguishes this from sibling tools that perform emission calculations or searches, making it immediately clear this is an authentication configuration tool.

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: 'for authentication' and 'to make authorized requests to the Climatiq API.' It implies this should be used before calling other emission calculation tools that require authentication, providing clear contextual guidance without needing to list specific alternatives.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does (calculates emissions) but doesn't describe important behavioral aspects: whether this is a read-only calculation or has side effects, what the output format looks like, whether it requires authentication (like 'set-api-key' sibling suggests), or any rate limits. The description is minimal and lacks operational context.

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

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

The description is a single, efficient sentence that states the core purpose and key features without waste. It's appropriately sized for a calculation tool and front-loads the essential information. 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?

For a complex tool with 11 parameters, nested objects, no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns, how results are formatted, whether calculations are real-time or cached, or how it integrates with sibling tools like 'set-api-key'. The description leaves too many operational questions unanswered given the tool's complexity.

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

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

Schema description coverage is 100%, so the schema already documents all 11 parameters thoroughly. The description adds minimal value beyond the schema - it mentions 'options for vehicle types and passenger count' which are covered in the schema, but doesn't provide additional context about parameter interactions or usage patterns. Baseline 3 is appropriate when the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose: 'Calculate emissions from passenger travel via car, plane, or train' - a specific verb (calculate) with resource (emissions) and scope (passenger travel). It distinguishes from siblings like 'freight-emission' and 'hotel-emission' by focusing on passenger travel, though it doesn't explicitly differentiate from 'travel-spend' which might be related.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions 'options for vehicle types and passenger count' but doesn't specify when to choose this over sibling tools like 'custom-emission-calculation' or 'search-emission-factors', nor does it mention prerequisites or exclusions.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It describes what the tool does (calculation) but doesn't mention important behavioral aspects: whether it's a read-only operation, what format the output takes, whether it requires authentication (like 'set-api-key'), accuracy limitations, or rate limits. For a calculation tool with zero annotation coverage, this is insufficient.

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. It lists all parameters without unnecessary elaboration. While it could potentially be more structured, every word serves a purpose and there's no wasted text.

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

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

For a calculation tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (emissions in what units?), how accurate the calculation is, what data sources are used, or whether authentication is required despite 'set-api-key' being a sibling tool. The agent lacks crucial context for proper tool 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 all 5 parameters thoroughly. The description lists the same parameters ('spend type, amount, currency, location, and year') but doesn't add meaningful semantic context beyond what's in the schema descriptions. It doesn't explain how these parameters interact or affect the calculation.

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

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

The description clearly states the tool's purpose: 'Calculate carbon emissions from travel-related spending' with specific parameters listed. It uses a clear verb ('calculate') and identifies the resource ('carbon emissions'), but doesn't explicitly differentiate from sibling tools like 'travel-emission' or 'hotel-emission' which might 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?

No guidance is provided about when to use this tool versus alternatives. The description lists parameters but doesn't indicate prerequisites, appropriate contexts, or when to choose this over sibling tools like 'travel-emission' or 'custom-emission-calculation'. The agent must infer usage from the parameter list alone.

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